MSc in Economics and Business Administration (Applied Economics and Finance) Department of Economics Master Thesis, October 14 th 2010 The impact of financial distress on external financing A comparison of non-financial corporations in market- and bank- based financial systems By Christiane Freund Supervisor: Finn Østrup, Center for kreditret og kapitalmarkedsret Number of pages 97 Characters 233,291 Copenhagen Business School
Executive summary This paper addresses a so far unexplored area of the difference between market- and bank-based financial systems. It addresses how the ability of non-financial corporations to obtain finance under the recent financial crisis is expected to change and how it actually changed. Non-financial corporations are the main value drivers of any economy. Their ability to obtain external financing affects the profitability of their investments and thus the economy s ability to cease the crisis. Large quantities of literature exist in the field of valuation of external finance, market- and bank-based financial systems and the fragility of financial systems towards financial distress. Very little literature exists in the cross-field of these three areas of external finance. The literature has so far left unexplored the subject of how developed financial markets, with different types of capital structure differ in their reaction to shocks. I use existing literature to formulate suggestions for the unexplored area. 7 hypotheses summarize the suggestions. The 7 hypothesis claim that credit tightening would force banks to sell securities and decrease their holding in bank loans. Depositors were expected to move their savings to investments in T-bills. Defaults in the Asset Backed Security market reduced the liquidity for banks with the consequence of banks selling assets and transforming the remaining assets to high risk liquid assets. All of these consequences were expected to be larger in market-based countries because systematic risk was expected to be higher. The increase in perceived risk both rationally and irrationally founded, were expected to be higher in market-based countries. Therefore value reductions in all types of external finance should be larger. Irrational behaviour should make lending and borrowing through bank loans more attractive. NFCs need for new finance should be reduced because prospects of future income decreased. Expansionary monetary policy should increase the value of especially bonds, but also bank-loans and equity. It is believed that changes in the value of already existing external financing affect the ability non-financial corporations to obtain new finance. Thus, I measure changes in the value of external finance and the mix of new finance. Regression analysis is used to measure both types of changes. The former showed highly significant results for bond, equity and bank loan financing. The latter showed insignificant results with respect to the mix of new financing. The empirical findings were as expected for bonds, equity and bank loans: The value of bonds never decreased, but it almost stagnated through 2008. Equity decreased the most through 2008 and rebounded in 2009. Bank loans already showed signs of a slowdown in 2006 and its value decreased in 2008 and 2009. The empirical findings were unexpected when comparing the change in external finance of bank- and market-based countries. Contrary to the hypotheses, bankbased countries were more volatile. They experienced a larger value reduction of securities in 2007-2008 and a larger increase in 2009.
Contents _Toc2746579701 Introduction... 1 1.1 Scope of interest... 1 1.2 Problem statement... 2 1.3 Methodology... 3 1.4 Sources... 4 1.5 Delimitation... 5 2. Literature... 6 2.1 External financing and its nature... 6 2.1.1 Bonds... 7 2.1.2 Equity... 8 2.1.3 Bank loan... 10 2.1.4 Comparison of securities and bank loans... 12 2.2 The Market-based and Bank-based Financial Systems... 14 2.2.1 Income and Capital Structure... 14 2.2.2 Information access, relationship and transaction costs... 14 2.2.3 Information asymmetry, moral hazard and agency problems... 15 2.2.4 Market efficiency and risk sharing... 17 2.2.5 Asset Backed Securities and Credit Default Swaps... 18 2.2.6 Market instability, fragility and systematic risk... 20 2.2.7 The financial crisis and capital access... 21 3 The Hypotheses... 24 3.1 The contribution of this paper... 24 3.2 Proposed effects of the shock on the financial systems... 25 3.2.1 Banks affect all types of external financing... 25 3.2.2 Different levels of systematic risk... 30 3.2.3 External financing and panics under financial crisis... 33 3.2.4 Non-risk related change in required return and interest rate... 35 3.2.5 The NFCs new finance unrelated to risk and required return... 36 3.3 The hypotheses in short... 36 4. Empirical foundation... 39 4.1 Structure of reporting the results... 39
4.2 Data collection... 39 4.2.1 Data sample... 40 4.2.2 Country determination and categorisation... 41 4.2.3 Critical assessment of the country selection... 43 4.2.4 Measurement techniques and variables... 43 5 Empirical results... 51 5.1 Research question A... 51 5.1.1 Bonds... 54 5.1.2 Equity... 57 5.1.3 Bank loans... 60 5.2 Research question B... 62 5.2.1 Results... 62 5.2.2 Why net-transactions provide insignificant coefficients... 63 5.3 Research question C... 64 5.3.1 Mathematical understanding of financial system-differences... 65 5.3.2 Introducing the results ad interpretation of these... 67 5.3.4 Change on total external finance... 75 6 Discussion... 80 Hypothesis I... 80 Hypothesis II... 83 Hypothesis III... 86 Hypothesis IV... 88 Hypothesis V... 91 Hypothesis VI... 93 Hypothesis VII... 95 7 Conclusion... 97 Bibliography... 99
Equations Equation 4.1 Degree of market-orientation, per year and per country... 41 Equation 4.2 Average degree of market-orientation, per country... 41 Equation 4 3 Weighted degree of market-orientation... 42 Equation 5.1 The contribution factor... 55 Equation 5.2 The marginal contribution factor... 56 Equation 5.3 The CCT marginal contribution factor bond level... 65 Equation 5.4 The CCT marginal contribution factor for equity... 65 Equation 5.5 The CCT marginal contribution factor for bank loan level, total... 66 Equation 5.6 Yearly percentage change in external finance... 75 Graphs Graph 5.1 Changes in bond level of NFCs... 52 Graph 5.2 Changes in NFCs equity level over time... 52 Graph 5.3 Changes in NFCs bank loan level over time... 53 Graph 5.4 Net transactions in short-term bonds... 63 Graph 5.5 Marginal contribution factor for bond level... 68 Graph 6.6 Marginal contribution factor for equity level... 71 Graph 6.1 Yearly change in consumer deposits... 84 Graph 6.2 Short-term interest rate on government bonds... 84 Tables Table 4.1 Categorization of countries... 42 Table 4.2 Short name and components of variables in Regression I... 44 Table 4.3 Properties and brief interpretations of variables in Regression I... 45 Table 4.4 Dependent variable ratios of Regression II... 50 Table 5.1 Regression I output for bond level... 54 Table 5.2 Time estimators for bond level... 55 Table 5.3 Cross-time term estimators for bond level... 56 Table 5.4 Regression I output for equity level... 57 Table 5.5 The time estimators for equity level... 58 Table 5.6 The cross time-term estimators for equity level... 58 Table 5.7 Regression I output for bank loan level... 60 Table 5.8 The time estimators for bank loan level... 61
Table 5.9 The cross time-term estimators for bank-loan level... 61 Table 5.10 Volatility in the marginal contribution factor for bond level... 69 Table 5.11 Volatility in the marginal contribution factor for equity level... 72 Table 5.12 Marginal contribution factors on bank loan level for each country... 72 Table 5.13 Volatility in the marginal contribution factor for bank loan level... 74 Table 5.14 Percentage change in total external finance... 75 Table 5.15 Example of unrealized return... 77 Table 5.16 Percentage standard deviation from the average level of external finance... 78 Appendix Appendix 1: Country categorization Appendix 2: Variable coding for Regression I Appendix 3: Normal distribution of the level data in logged values and a natural scale Appendix 4: Regression 1 output for all types of external finance Appendix 5: Variable coding for Regression II Appendix 6: Regression II output for all ratios between different types of external finance Appendix 7: Graphs for net-transactions in long and short-term bonds, equity and bank loans. Appendix 8: Marginal contribution factors and associated graphs Appendix 9: Capital structure for each country and each type of finance over time Appendix 10: Correlation coefficients between level and capital structure Appendix 11: Discussion of the hypotheses Appendix 12: Bank assets
1 Introduction 1.1 Scope of interest For many years it has been discussed whether bank-based financial systems contribute better to growth than market-based financial systems. A bank-based financial system is a region (typically a country) where financing through loans dominates bonds and stocks and vice versa. Several authors have described relative efficiency, stability, strength and weaknesses of the two different systems. However, no authors have addressed how and to what extent these two systems affect the non-financial sector s ability to obtain finance under periods of financial distress. This is what I want to do in this paper. The non-financial corporations (NFCs) represent the part of the economy that produces services and products; both private and public. Hence the non-financial sector holds a significant share of jobs and value creation in all countries. In the end, financial intermediaries (incl. pension funds, insurance companies and commercial banks) invest their money in the non-financial sector; directly or indirectly. Financial distress can evolve into financial crises and this can begin and develop in many ways. Depending on how financial instability starts and how it evolves, the impact on both the financial and non-financial sector will differ. Therefore one must be careful not to generalize the findings on how and to what extent macroeconomic determinants affect the NFCs. Yet, understanding why and how NFCs ability to obtain finance was influenced by the recent crisis can lead us to better future decisions about the optimal financial structure. Therefore the paper only considers the recent financial distress beginning in 2007 and the scope is purely the NFCs. In addition the focus is on European countries and well as industrialized Anglo-Saxon countries in order to control as much as possible for other major discrepancies than financial structures. Since NFCs are the main value driver in all countries, their access to finance is absolutely crucial for how fast a real economy of a country tackles financial distress. Finance is not the only remedy to cure low growth rates. Nevertheless access to finance at favourable prices increases the number of profitable businesses benefitting the creditors and investors as well as the labour force and hence countries in general. Finance is in this paper defined as external finance, not internally generated cash. External finance is roughly divided into bond financing, equity financing and bank loan financing. 1
1.2 Problem statement 1) How would one expect new financing and the level of finance for non-financial corporations to change in the event of financial shocks similar to the one occurring in 2007-2009? Would such expectation differ between market-based and bank-based financial systems? 2) In what way have new financing and level of finance for the non-financial corporations actually changed during the recent financial crisis? Do such changes differ between market-based and bank-based financial systems? The theoretical answers to problem statement 1 are formulated as hypotheses. These are based on existing literature in the field and they distinguish between the effects that strike bonds, equity and bank-loans in market- and bank-based financial systems. The empirical answers to problem statement 2 are provided through three research questions that differentiate between the types of external finance: A. How does the overall level of finance change under the crisis? B. How does the mix of the three kinds of new finance change during the crisis? C. How do the results of A and B differ between bank and market-based economies? Research questions A and B use the level of finance and kind of new finance as variables. The level is a measure for the total value of finance as stated on the balance sheet of NFCs. Changes in level equal net-transactions, value changes and accounting related changes in the period of interest. New finance is in practice measured as net-transactions. Thus the difference in the changes in the level and new finance lays in valuation and other accounting related changes. New finance is the most interesting measure as it directly addresses the NFCs ability and willingness to obtain new finance. However, changes in the level through revaluation are expected to affect net-transactions and highlight why new external finance would change from one period to another. Therefore both variables are included in the analysis. Research question B concentrates on whether one type of new external finance changes more than another type of new financing. It highlights relative change and consequently it addresses if one type of external finance was more exposed than another to the crisis, regardless to the total change in external finance. Answers to research question C uses the results provided in research question A and B. 2
1.3 Methodology The methodology serves to describe the general structure, data treatment and limitations of the paper. In-depth description of the purpose of each chapter as well as the structure and limitations of these are presented in the respective chapters. Figure 1 below presents an overview of the paper, the order in which it is presented and how the different parts are related. Figure 1- Model of the structure 3
Chapter 2 provides the informational foundation which serves three purposes: (1) To obtain the tools needed to create solid hypotheses, (2) to be able to treat relevant empirical data correctly and (3) to create a framework for interpreting the results. The informational foundation is divided in two subchapters; one presenting tools for valuation of financial assets and one introducing existing literature in the field of market and bank-based financial systems and financial volatility. Chapter 3, The Hypotheses, first discusses how the theory and existing research coincide (and collide) and the literature gap this leaves. The literature gap is consistent with the problem statements. Secondly, expectations on how the level of, and access to, external financing changed during the recent crisis are formulated and thus problem statement 1 is answered. Chapter 4 describes the most appropriate way for measuring research question A and B. Research question C uses the output of research question A and B. The chapter exposes the statistical implications that must be dealt with to ensure the validity of the results. The chapter implicitly uses knowledge obtained in chapter 2 for formulating mathematical relational arguments, i.e. causal relations. The mathematical arguments are formulated in terms of regressions and treated in SAS Enterprise 4.2. The results are presented and analyzed in chapter 5. They are divided into three parts, each corresponding to one of the three research questions. Each of these parts is again divided into subchapters concerning bond, equity and bank loan financing respectively. Chapter 5 merely points out the findings and a deeper argumentation for the underlying reasoning for the results is not carried out until chapter 6. The Discussion, chapter 6, compares the three results. It is the part of the paper where information from all chapters are woven together in order to attain a higher level of analysis, reflection and discussion. All arguments are structured according to the hypotheses and they therefore highlight discrepancies and unanimities between the hypotheses and the results. In the end, the conclusion sums up all findings in light of the problem statements and the hypotheses. 1.4 Sources My second-hand sources are primarily used to answer problem statement 1 and consist of two types of input: 4
(i) (ii) Models and theory describing the nature of external finance. These are used in chapter 2.1 and build on literature published with an educational purpose and the chosen books are used at Copenhagen Business School in courses. One, a statistic book, is used in chapter 4. Research papers. Chapter 2.2 uses research papers where theoretical models associated to market- and bank-based finance as well as financial volatility are tested and accepted or rejected. A significant amount of researchers contribute to the empirical findings. Often they disagree and as the macroeconomic structures have changed, what seemed true once is perhaps wrong today. I quote as many authors as possible and hold their arguments against each other. I do not judge who is right and who is wrong. I merely highlight all views in order to expose the uncertainty in the literature and use the perspectives to create my own picture of the current literature. My first-hand sources are used to answer problem statement 2: (i) (ii) I have collected empirical data from national statistical databases and the central banks in each country. These databases are also used by the OECD and Euro Stat. Since I consider these as valid data sources, I also consider the national data bases to be valid. I have backed statistical theoretical arguments and methods with dialogues with a statistic teacher at CBS, Cedric Schneider. 1.5 Delimitation Specified delimitations will be described in details throughout the paper where appropriate. In general I have delimited my thesis in three areas: (i) (ii) (iii) Scope delimitation. The paper does not distinguish between different types of NFCs and their associated risk level. No relations between NFCs access to external finance and the real economy s situation is examined, even though the paper claims such relation to exist. Data and literature delimitation. Hundreds of articles and theoretical books have been written on the broader subject of financial systems (though none on my specific subject). I strive to use as wide a pallet of perspectives as possible, but I trust that the amount of information used to cover the general view is sufficient. Expectations to the reader. In general it is expected that the reader possesses thorough knowledge in the area of finance, valuation and risk. Furthermore a basic understanding of statistic regression analysis is required. 5
2. Literature Chapter 2 is divided into theories and models on external financing (2.1) and research papers describing the characteristics of market and bank-based systems (2.2). Then this chapter is used throughout the rest of the paper as a framework for intuitive treatment of problem statement 2 and directly to answer problem statement 1. 2.1 External financing and its nature At its broadest level, one can split financing into three categories: debt, equity and internal financing. Debt and equity are together referred to as external financing. Internal financing of a project originates from either liquid assets sold to obtain the necessary finance or from net income earned in the previous periods. As this paper is interested in external financing, internal financing will not be further addressed. External financing as debt includes primarily two types of debt: Bank-loans and bond securities. Both of these exist in many forms and with different maturities. Bank-loans are credit by banks and differ mostly from bonds in the sense that they seldom are traded on the secondary market (Brealey et al, 2008). The bank tends to keep its investment to maturity. Bonds on the other hand are sold all the time at the secondary market. External financing in form of equity is distinct from bonds and loans, as equity investors stand as owners of the company with the right to all residual income. External finance can be addressed based on whether it is publicly traded (secondary market trading) or held by the original creditor (primary market). Bonds and equity are traded on the secondary market and hence referred to as securities. Financial markets where most of the external finance consists of securities are referred to as market-based financial systems. When NFCs rely heavily on bank-loan the system in question is named the bank-based financial system (Demirgüς-Kunt & Levine, 1999). This distinction is used in the rest of my paper as we check if and how the two systems react to financial distress. In order to conduct a thorough analysis of shock effects, one has to understand the similarities and differences between the three types of external financing. The next four subchapters serve the purpose of explaining the role and terms of bond-financing, equity-financing and bankloans. In the end, they will be compared and their relative attractiveness is addressed; first from the NFC s perspective, then from the investor s and creditor s perspective. 6
2.1.1 Bonds The explanation of bond characteristics is divided into contractual composition and valuation of bonds. The same division is used for equity. 2.1.2.1 The contractual agreement Bonds are debt securities which a debtor can issue in order to receive a certain amount of cash today. It is a certificate stating how much a debtor owes to a creditor, the payment terms, price, trustee, sinking fund terms and potential call provision (option to repay the debt before it is due). It does not state who the creditor is as the security in principal could shift hands 1000 times a day (Brealey et al, 2008, p.670). Debtors have the option to pay the principal (the money borrowed) back to the creditor. If he decides not to, the creditor can claim the debtor s assets corresponding to the value of the debt. This option is referred to as the option to default. If the value of the assets is larger than the value of the debt, the residual value belongs purely to the debtor, i.e. equity holders. If the assets are worth less than the debt, the creditor can only claim the value of the assets and not the value of the debt. This is a valuable option for the debtors, as the loss is limited to the value of equity, i.e. assets minus debt (Brealey et al, 2008). The option increases the creditor s risk and thus bondholders require a premium to take such additional risk. The bondholders will never earn more than they were promised if they keep the bond until maturity. If the promised cash flow differs from what they expect (e.g. due to the risk of default) creditors adjust the price to their expectations (Brealey et al, 2008, chapter 4). Bond creditors are more risk adverse than stockholders. They can never receive more than promised, only less (in event of bankruptcy), but on the other hand they are entitled to the NFC s cash flow before the stockholders. Bonds differ with respect to maturity, type of interest rate (floating or fixed), currency and contractual promises such as collaterals and payment structure. The price of such a bond will depend on all the variables above. In addition a fee is charged by the underwriter to manage issuing and reselling of bonds. The price in the US is about 7 % of the sum raised from creditors. No significant scale economies seem to exist (Brealey et al, 2008, p.418). 2.1.2.2 Valuation of bonds Bond holders required return is called yield to maturity (YTM). It is used to determine the market price of a bond given the time to maturity, coupon rate, face value and payment structure. YTM can be seen as the creditor s required risk adjusted compensation over time for lending out money. YTM is an average rate of what the creditor is promised to receive in 7
nominal return for investing in 1-year 0-coupon bonds in sum replicating the terms and cash flow values of the original investment. in bond value is a function of the in expected yield to maturity (YTM) (Brealey et al, 2008, chapter 4). YTM consists of different types of compensation which can be divided into (I) compensation related to default free treasury bonds and (II) the additional premium related to all other types of bonds (Ross et al, 2006, chapter 7). In detail: I.a. Real interest rate or the time value of money (Brealey et al, 2008). I.b. Inflation premium: Compensation for the value reduction of money over time. I.c. Interest rate risk premium: It points back at the two former premiums and express compensation related volatility in the nominal interest rate. II.a. Risk of default: It is already explained above. II.b. Taxability premium: The additional tax the government charges on corporate bonds. II.c. The liquidity premium: Compensation for illiquidity of assets (more about this in the subchapter 2.1.3 Bank loans) 2.1.2 Equity The explanation of equity characteristics is divided into contractual composition and valuation of bonds. 2.1.2.1 The contractual agreement Equity is the capital belonging to a firm s owners. Equity and bonds are first issued at the primary market by a given company, and then it is sold and bought on the secondary market. It is the price available on the primary market that is relevant for the NFC, but also the signalling price on the secondary market, since it affects the primary market price. Signals indicate the risk/stability of a company s strategies and operations as well as the profitability of the company. The equity value = because the owners can refuse to pay debt if it exceeds the value of assets (Brealey et al, 2008). Ones they have repaid their obligation, there is in theory no limit for how much they can earn. The result is (i) higher volatility in income and (ii) a non-normal distribution of income, thus enhancing equity owners motivation to take risk. If only one owner exists and he operates his company, the interest of owners and the management is aligned. The risk of conflicts arises when the management has different incentives than the owners. A relating monitoring cost is encountered and contracts and 8
incentive schemes are used to reduce the problem. Such problem stems from asymmetric information and moral hazard (Bennedsen & Nielsen, 2009) and it cannot be eliminated, only reduced. Hence a risk premium is added by owners. 2.1.2.2 Valuation of equity One crucial factor separates the valuation method related to equity from that of bonds and bank-loans: A corporation is assumed to live forever and therefore it does not mature at a certain point in time. Not all products are relevant in infinity, but ones a product is pulled back from the market it is assumed the money is used for a new product, just as profitable. If not, the owner will receive the money, and given the market is efficient the owners can freely reinvest their money in other firms. The market value is the sum of all discounted cash flows, where the discount rate is the cost of equity, also called the required return, R (Brealey et al, 2008). R is determined by the general market expectations given a perceived risk level. It includes three types of compensation: 1. Real interest rate or the time value of money 2. Inflation premium 3. Risk premium: Just as with bonds, equity holds risk. Risk is associated with volatility in (i) the future cash flow and (ii) interest rate risk premium. (i) The variance of the cash flow is a function of the volatility in the difference underlying cash flows, e.g. the cash flow of purchasing costs and the cash flow of income, and the weights of these cash flows. In addition the correlation between the underlying cash flows also affects the total volatility. For accepting cash flow risk, a risk premium is required. Risk can be separated into two categories, unique risk and market risk. Unique risk refers to the volatility in cash flow which affects one company or a few companies. It can be diversified away by investing in negative correlated assets. Market risk refers to the type of volatility in cash flow that to some extent affects all companies in the same way. For the same reason one cannot hedge away this type of risk (Brealey et al, 2008) and thus a premium is required for absorbing market risk, also named systematic risk. (ii) The change in real interest rate and inflation follows the same logic as for bonds. The price of equity changes as the expected cash flow and components in R changes. Reasons for this are discussed later in the paper. Given a certain required return of equity or interest of 9
bonds, the volatility in equity prices (stemming from either changes in expected cash flow or changes in R) is larger than for bonds, because the duration is infinite. 2.1.3 Bank loan The 1 st subchapter concerns the activities characterizing banks, as banks are the only who can grant bank-loans. Understanding them is important to understand bank-loans. In the same manner the 2 nd subchapter addresses the competitive advantage of banks and bank-loans. 2.1.3.1 Structural form Financial intermediaries (from now on FIs) are, in contrast to NFCs, firms who hold large quantities of financial claims as assets (Greenbaum & Thakor, 2007, p. 50). Parts of these assets correspond to the liabilities of NFCs and households. FIs take many forms such as commercial banks, pension funds or insurance companies. The financial claims they hold exist in numerous versions. Banks hold both securities and loans. Loans are issued in shape of consumer credit, business loans and mortgage (Greenbaum & Thakor, 2007, p. 174). Therefore, when explaining the nature of bank loans one has to be careful venturing into an analysis of banks as a whole. The fact that bank loans exist implies that they can offer something that the capital market cannot. When banks, many years ago, started to lend, it was in shape of loans. Today, credit exists in many hybrid versions, which is partly a result of a more efficient capital market. It has reduced the benefit of bank loans and forced banks to compete in and with the secondary market where securities are traded (Greenbaum & Thakor, 2007, p. 179). Commercial banks held around 40 % of their assets as loans in the US in 2007 1. Of this, about 50 % went into mortgage, 40 % into business loans and 10 % into consumer credits. Thus, loans are still important to banks and understanding the role of bank loans is to understand banks and their competitive advantage. 2.1.3.2 The role of banks Banks occupies two major activities: Brokerage and quantitative asset transformation (QAT) ((Greenbaum & Thakor, 2007, chapter 2). Brokerage concerns matching creditors and debtors by screening them and categorizing them into different types of debtors and creditors (depositors). QAT involves transforming deposits with one type of characteristics into loans with other types of characteristics. 1 Among the country sample used in this paper, US NFCs are the ones holding least liabilities in form of loans and it is therefore reasonable to believe banks in other countries to hold at least 40 % of their assets as loans. 10
Banks earn money on difference between the interest paid to depositors and the interest required by the debtors. Loans are usually more expensive than bond-loans collected on the free market, so to stay attractive banks must provide something the capital market cannot. The role of the broker is to reduce the problems that can occur due to asymmetry between information held by the bank and the information held by the debtor. Informational symmetry is only a problem if the debtor uses his private information to maximize own utility to exploit the creditor. Such action is referred to as moral hazard. Brokers seek to align both pre and post informational asymmetry and they succeed better than individual creditors, because they are specialized and because they serve so many debtors that scale advantages exist and cost of screening per debtor decreases. However, the broker role can exist without the bank holding any significant assets, i.e. by servicing other investors on their respective investments. Yet, banks choose to conduct the broker role by using their own assets and liabilities. Matching assets in the form of loans with liabilities in the form of deposits (incl. interbank lending), involves four overall tasks and hence four types of risk: 1. Matching assets and liabilities with different durations. Creditors tend to prefer short term investments and debtors long term investments. Higher price volatility and hence risk exists for long term assets than for short term liabilities. The related interest rate risk premium results in YTM Long > YTM Short and this creates a base for profit. 2. Matching liquid deposits with illiquid bank loans. It benefits the depositor as he can withdraw instantly keeping risk low. It benefits the NFC as it expands the capital market. Withdrawal risk generates liquidity risk, as banks ability to meet depositor claims is in danger if a large number of depositors withdraw at the same time. Governments partly insure deposits which reduce overall liquidity risk and hence premium of the bank loans. 3. Matching deposit and loans with different default risk. Banks absorb debtors risk of defaulting, partly by diversifying risk (only possible because they serves many debtors), partly by monitoring debtors and partly with help from the government s deposits insurances. This attracts money from very risk adverse depositors thus expanding the credit market available for the NFC. For this service and risk absorption banks charge a credit risk premium. 4. Matching depositor savings with debtor loans. Savings are usually smaller than demanded loans, so the task enhances access to loans and in this way banking increase market efficiency. 11
2.1.4 Comparison of securities and bank loans The properties and valuation of bonds, equity and bank loans were described above, because potential changes in NFCs financial structure and level during the crisis somehow is caused by changes in the components used to value external finance. These components, i.e. discount rate and expected cash flow, differs depending on the specific type of external finance which is characterised by its contractual agreement. This chapter compares the contractual agreement and price of external finance with the relationship between the creditor and debtor. It discusses how these variables determine the creditor s and debtor s preference for one type of finance over another. 2.1.4.1 Comparison from the NFC s point of view It has been shown that NFCs tend to borrow from venture capitalist when they possess few tangible assets and as they grow larger they turn to banks for capital. The mature firms prefer the capital market, but they also use bank loans and of course equity (Greenbaum & Thakor, 2007, p. 61). Lack of tangible assets means lack of insurance against the loan and thus the borrower has a larger incentive for hazardous behaviour. Therefore venture capital works as equity which gives the creditor legal right to shape and affect decision making. In turn, this reduces the risk of moral hazard. Since this is the only way the borrower can obtain any capital, they will pay the price the venture capitalist requires. Banks do not work as venture capitalist. Banks (with blurry limits) are creditors, not owners. They use contracts including covenants in form of collateral to reduce monitoring cost. In addition, the governments partly insure deposits. Therefore, the deposits carry a lower interest rate than venture capital does: Seen from the depositor s perspective, the default risk is lower and so is monitoring costs. NFCs prefer bank loans to venture capital when they have sufficient assets to obtain the lower interest on bank loans. Bank loans usually mature faster than bonds and therefore loans need to be refinanced from time to time (James, 1987). The security market uses the information of refinancing as a sign of creditworthiness, so the longer a NFC has obtained loan from one specific bank, the more the security market interpret these signals as signs of high creditworthiness. In the end, it enables a trusted NFC to avoid the banks and their broker fees and instead enter the security market. The lower interest rate in the security market is an incentive for NFCs to protect their reputation. The value of reputation works as self-control not to conduct moral hazard. 12
Therefore the creditors monitoring costs are reduced, but not eliminated, and this is part of the reason why interest rates on bonds are lower than those on bank loans. Concerning equity, NFCs do not issue new equity each day, as it can send mixed signals: Some could interpret new issuing as a last resource to external capital and as a rejection from banks and other bondholders. Others could interpret new issuing as a sign of yet another profitable investment where equity financing would keep the weighted average cost of capital (WACC) at a minimum. The fact is still that loans and bonds are used much more for refinancing and new investment financing than stocks. This is referred to as the pecking order of finance (Myers and Majluf, 1984). 2.1.4.2 From the Investor s Point of View When an investor requires a return or price, it covers a basic compensation premium plus a risk premium and perhaps intermediary costs (broker fees). This is valid for all types of new external financing. According to the capital asset pricing model (CAPM), an efficient market holds a constant relation between the price required by investors (return) and the market risk related to a certain portfolio. If asset prices divert form the constant relation the market will correct the price. In reality, many critics have attacked the model, but intuitively it makes sense if we recognize that no one will take risk without perfect compensation and that the market is efficient enough to prevent superior profit in the long term. If the CAPM is accepted, then FIs investments in bank loans or securities illustrate how the different financial sources with different risks meet different preferences. The interest rate merely balances the risk and the expected return. When banks invest in all three types of finance they create a portfolio corresponding to their risk tolerance. The same is true for the borrower, i.e. the NFC, who also chooses a portfolio of liabilities which ideally creates the lowest WACC and all things equal the highest firm value (Ross et al, 2006, chapter 15.6). The relevance for this paper is not to determine appropriate WACC and thus capital structure, but rather the change in the balance in the event of financial distress. 13
2.2 The Market-based and Bank-based Financial Systems This chapter is divided into different subjects relevant to problem statement 1. The subjects present research findings and proposed theoretical relationships by other authors. In general they are structured according to the two main division of the literature: (i) the characteristics of the two systems disregarding point in time and (ii) how these systems reacts to changes over time, such as financial shocks. 2.2.1 Income and Capital Structure Several authors have proven that as GDP increases countries tend to move towards a more market-oriented financial system (Murinde et al, 2004). Murinde, Agung & Mullineux demonstrate how especially equity plays a still larger role for European countries as these countries get richer, while bonds hold a more constant position and bank-loans decreases. Internal finance plays a still larger role. Demirgüς-Kunt and Levine (1999) found the same relationship between the degree of market-orientation and income to be true, but they used a worldwide selection of countries. They defined the degree of market orientation as a function of the stock market relative to bank loans; more specifically measured according to size, activity level and efficiency and limited to the private sector. Bonds were excluded in the survey. My paper has limited its sample to high income countries, so in light of Murinde et al and Demirgüς-Kunt & Levine, these countries should all be relatively market-oriented. In so, even though some of them are defined as bank-based and some of them as market-based their financial structure should not divert as much as if we included low income countries. Therefore we would not expect the sample countries potential reaction to the crisis, as a function of capital structure, to divert as much. 2.2.2 Information access, relationship and transaction costs Banks fulfil an intermediary role that on one hand increases the price of bank-loans relative to bonds (Holmstrom & Tirole, 1997) and on the other hand provide the more efficient and thus cheaper access to information for certain types of borrowers. Hence, on average bank loans are more expensive than bonds, and NFCs would all things equal prefer bonds. But for some NFCs, such as small companies with little collateral, banks can be the only way to obtain external capital. In the end, the cost of external financing depends on risk and information cost, where the latter partly is a function of the former. Ross Levine includes information cost implicitly in his estimation of relative structural efficiency, as bank efficiency is measured partly by the interest rate level and partly by overall 14
cost to asset value (Levine, 2001). Hence he believes that informational costs affect relative efficiency and therefore it does not only affect NFC s financing choice, but also the overall access to external financing and hence growth. The harder it is to gain information, the more expensive it is. Thakor refers to informational economies of scale as information reusability and it originates from the fact that specialists of screening, such as banks, have many clients with either cross-sectional or intertemporal similarities (Chan et al, 1986). The price of such information is less than the price paid by the free market investor, who has to gain company specific information from scratch every time a new potential investment appears. The cost of gaining proper information to avoid adverse selection and moral hazard is reduced as investor protection is improved by La Porta (2005). Levine and La Porta agree that both the market and the bank-based system benefit from good accounting standards, investor protection, law enforcement and common law traditions. All these criteria seem to create an overall more developed financial system which in turn decrease the borrowing costs (Boyd & Smith, 1998) and increase growth. As the banks strength mostly lie in NFCs where information reusability is significant, and as these NFCs for the same reason only can obtain external finance through bank loans, a low level of bank-financing enhances income inequality. According to Chakraborty & Ray (2006) none of the systems are better for growth as long as the overall financial market is efficient. Levine supports this view. Given a certain level of financial development, the security market obtains information through signalling and through the reliance of how important the market is for the debtor. The lower the debtor s wealth, the higher the gambling incentive. The higher the gambling incentive, the more the debtor will need to get his money from the banks. As the solvency decreases under financial crisis, more debtors might be forced to use bank loan financing. 2.2.3 Information asymmetry, moral hazard and agency problems Carey did in 1995 find evidence that information asymmetries are not an important factor in bank loan contracting with large borrowers, but moral hazard is (Boot & Thakor, 1997, p. 727). This is interesting because information asymmetry itself is a precondition for moral hazard to occur: The debtor uses the asymmetry to increase own utility at the expense of the creditor and owners. The problem of moral hazard is twofold: First, depending on the degree of asymmetric information and the perceived likelihood of agency problems, the cost of 15
reducing asymmetric information (through screening and monitoring) and moral hazard (through contracts and monitoring) can be significant. Secondly, even though the highest level of monitoring and contractual agreements is conducted, contracts are incomplete and agency problems (moral hazard is a type of agency problem) cannot be prevented completely (Allan & Gale, 2001). Thus, the associated risk premium and cost of screening and monitoring increases with moral hazard. According to Allen & Gale renegotiation can reduce the agency problems associated with incomplete contracts, because it gives the borrower an incentive to deliver the agreed result if refinancing must be an option. Does it mean that agency problems are controlled better in a bank-based system, assuming it is managed in a cost-efficient way? Dewatripont & Maskin (1995) argues that debtors using the secondary market for external financing face several creditors and hence the debtor is not as important for the single creditor as he will be for the financial intermediary from whom he receives a larger fraction of his total loan. Renegotiation in the market is costly, because it involves more creditors. The debtor is more depended on the creditors as a whole than the creditors are on the single debtor. As a result, the capital market s threat of no refinancing in event of moral hazard is more real and this can reduce the agency problem. The two arguments reflect upon the relative power and interdependences between the borrower and creditor. Note that the discussion takes for granted the level of asymmetric information and focus on the relative agency problem of the two systems and how this can be reduced through renegotiation. Both markets can be just as efficient in reducing the agency problem; banks by creating tight relationships and the secondary market by reducing its dependence of the debtor. Relative efficiency depends on the cost of reducing agency problems of one specific debtor. Thus relative efficiency is not a constant unrelated to the characteristics of the debtor, but differs depending on how the strength of the two systems fits the profile of the debtor. Moral hazard arising from agency problems, i.e. the fact that the interest of the creditor and debtor is not aligned, is linked to a non - normal distribution of the NFC s profit. A debtor can maximum lose his equity, but profit can be infinitely large. As debt relative to equity increases, the incentive to take more risk increases at the expense of the creditor. Thus, the probability of moral hazard increases (Allan & Gale, 1998). Owners should ideally be represented perfectly by the managers of the company and in this case investors face no agency problems. Still, La Porta et al emphasise the difference between insiders and outsiders, i.e. managers and controlling shareholders versus residual 16
shareholders and creditors (La Porta et al, 2000). Under financial crisis the solvency degree decreases and therefore controlling shareholders and management use a higher degree of selfdealing and risk taking (La Porta et al, 2005). Less profit is left for the remaining shareholders and thus securities to sell for a discount. 2.2.4 Market efficiency and risk sharing Closely related to monitoring debtors is the concept of risk-sharing, which cover the situation in which a principal and an agent somehow align their interests in accordance with their riskpreferences (Shavell, 1979). It involves transferring of risk and associated required return from one actor to another actor. Risk-sharing is closely connected to market liberalisation as less regulations encourage innovative products such as securitization (more about this later) and as these products enables intermediaries to repackage assets into new type of assets with different risk levels. As a result the market of financial assets expands in form of higher overall supply and demand driven by more diversified products. The effect is increased liquidity, motivated by increased efficiency (less deadweight loss and lower transaction cost as the markets become more competitive). Gallegati, Greenwald, Richiarti & Stiglitz did in 2008 write that securitization proved an effective means to obtain diversification and risk reduction in good times. However, when the economic conditions started to deteriorate these linkages became detrimental and dangerous, building up a significant risk of systemic failure to the U.S. financial system and imposing a significant a threat to global financial stability (p. 3). Risk-sharing can reduce default risk because more actors share a potential loss which decreases the risk of a chain-reaction in defaults. But risk-sharing in a combination with new innovative products has a downside in that the claims on the same underlying assets are sold and repackaged many time. First, this diminishes the final creditor s ability to monitor the debtor which enhances the debtor s incentive to take more risky positions. Secondly, the risk of miscalculating the risk associated to the underlying cash flow increases as information access decrease. Still, risk-sharing increases competition and thus efficiency in good times. Financial market efficiency is not a matter of the bank- or market-based systems relative efficiency, but of their accumulated efficiency. In liberalized markets the two systems supplement each other, but bank-activities tend to move toward market-activities. Parallel to this, Boot & Thakor mention that an observable quality cut-off, where borrowers below this level is served by banks, tends to increase as banks become more competitive and the overall 17
market more efficient (Boot & Thakor, 1997). Hence, more debtors obtain finance from banks, but now in form of securities and loans later transformed into Asset Backed Securities. Allen & Gale describe how banks have a long-term perspective and therefore diversify risk inter-temporally much more than other investors and creditors, (Allan & Gale, 2001 and 1995) 2. In contrast, these other investors and creditors do not allow such a long perspective and therefore risk-sharing is more cross-sectional here. During the past years, banks have reduced inter-temporal risk sharing in favour of cross-sectional risk sharing in order to be more competitive and market-oriented,. In context of this paper it might be relevant to ask which of the two types of risk diversification is more fragile under financial distress. 2.2.5 Asset Backed Securities and Credit Default Swaps Securitization can be seen as a fourth way of financing. It requires that the originator (a bank) holds cash-flow generating assets (securities, rental, leasing, loan etc). If the originator sells the cash flow (not the loan itself) to someone else, it can receive money today corresponding to the future cash flow. A claim is changed into something tradable; bank loans are transformed into a financial asset that is tradable on the secondary market. Securitization results in an increase in the bank s assets corresponding to the cash received. The liabilities increase with the same value and the debt account is referred to as Asset Backed Securities. The process of transforming loans into Asset Backed Securities is referred to securitization. Securitization is a benefit for the originator as it isolates risk of a specific loan and free capital that can then be used for new investments (Greenbaum & Thakor, 2007, p. 180). Securitization of assets that are better rated than the total assets, result in lower interest requirement by the Asset Backed Securities - buyers and correspondingly cheaper access to capital for both the bank and the original debtor. From the buyer s point of view, the securities can take the form and risk preferred, because many different loans are repackaged into new format that suits different risk preference of the investor. Consequently, more bondholders become interested in investing. In turn, market liquidity and money aggregates increases (Weithers, 2007). If the risk is perceived as too big, Credit Default Swaps can be offered by a bank, an agency or others to design the perfect product for the bondholder. The seller of the Swap guarantees a pre-specified compensation level to the buyer of the Credit Default Swap in the event of 2 It means that banks hold larger reserves than other actors in the financial market, because it is in their interest to ensure future profitability. Such interest serves future generations, and results in less volatile asset prices today. 18
default. To provide this service, the credit enhancer, e.g. Freddie Mac, Fannie Mae or Ginnie Mae, receive an insurance fee. Credit Default Swaps do not have to be combined with Asset Backed Security and they can be sold by everyone. But they often are combined, and they are often bought and sold by banks. The reasons for combining them are many and complicated and thus out of the scope of this paper. The consequences, however, are important: 1. The risk of default is reduced to the extent that the seller of the swap does not default, i.e. to the extent that a double-credit event does not occur. 2. The liquidity of the Asset Backed Securities might increase, because the information gap is less important: As the risk is transferred to the Credit Default Swap-seller, risk assessment is reduced and so is the perception of risk while the interest rate might not fall correspondingly. Thus the demand increases. If the perception of risk is reduced, NFCs can possibly obtain capital cheaper thus making the market more efficient and growth-enhancing (Kaufmann & Valderrama, 2007). 3. Since the bank does not bear the risk of default, it might lower the quality of its monitoring and screening to increase profitability. The risk of this happening should increase the interest of Asset Backed Securities and the insurance fee of Credit Default Swaps. But if good lasts long, the credit enhancement institutions might underestimate the true risk and buy too many and too expensive Asset Backed Securities. Thus, the banks will earn profit on taking on additional risk. This profit relates to (i) fees and (ii) a potential spread between the interest rate on Asset Backed Securities and the interest received from the original loan. Asset Backed Securities are owned by the same investors as the ones holding other securities (Mengle, 2007). Banks are the major sellers and buyers of Credit Default Swaps (PIMC, 2006). NFCs barely buy or sell Credit Default Swaps. As a result NFCs are affected by Asset Backed Securities and credit enhancers to the extent it affects their access to capital. Many market players are involved in the Asset Backed Securities and Credit Default Swaps markets and the latter is often issued many times the principal value of the original cash flow. Therefore, ones a borrower defaults it affects investors all over the market. Stiglitz & Greenwald (2003) describes cross-sectional risk sharing as an efficient shock absorber, because many parties lose some which decreases the risk of default. If defaults are comprehensive enough, cross sectional risk-sharing appears to be contagious and thus create systematic liquidity problems. It could reduce NFCs access to new finance because creditors 19
liquidity is reduced, but also reduce the value of NFCs debt and equity as its creditors and investors default. 2.2.6 Market instability, fragility and systematic risk Kaufmann & Valderrama (2007) show that there is a relationship between credit aggregates 3 and asset prices 4 : (i) Credit and asset prices reinforce each other. (ii) Asymmetric dynamics mean that the relationship between credit aggregates and asset prices is not constant over time; it changes with the level of financial stability. (iii) Credit aggregates and asset prices reinforce each other more in the market-based financial system than in the bank-based system. It means that a credit tightening in market-based countries is expected to decrease asset prices more than in bank-based countries. According to Allen & Gale (2004) intermediaries in the developed financial markets are so closely linked and engage in the same activities that financial fragility increases. Fragility enhances systematic risk. But for such risk to result in a systemic slowdown requires that something trig a reaction. This could be withdrawal of bank deposits. Banks operate on competitive terms and invest in many different financial instruments and their access to capital depends on deposits with short-term withdrawal rights. Banks can experience liquidity squeezes, if all depositors withdraw at once. In that case they can be forced to sell rather illiquid assets (e.g. mortgage) below their true value. As a result, the market value of banks assets quickly falls. Thus banks equity decreases which reduces the portfolio value of bank investors. If the reduction in bank assets and equity is large enough it can trigger insolvency for many market actors. Thus interaction between different market actors, and similarities in their asset investments, combined with a heavy weight of illiquid assets, enhances fragility (Allen & Carletti, 2007). The more risk-sharing the financial system engage in, the more interdependent and fragile it will be, but it will at the same time become more competitive and efficient. Yet, Allen & Carletti argue that fragility is a sign of inefficient liquidity. Berger et al (2008) add that they have found evidence supporting this competition-fragility view; the more competitive the financial system is, the more exposed to risk it is. Combining the perspective presented by Allen & Gale (2004) with the fact that Asset Backed Securities and Credit Default Swaps enhanced overall leverage before the crisis and thus increased credit risk, I argue that the level of interaction between the market and banks 3 Credit aggregates are the financial assets belonging to banks (Collins and White, 1999), but with special attention to credit, not equity investments. 4 Asset prices include equity and therefore more indirectly the investments conducted by the actors who borrow from the banks. 20
was so high that even small shocks in the financial market could trigger large changes in asset prices. In contrast to Allen & Gale, Finn Østrup (2010) and Greenwald, Richiarti & Stiglitz (2008) argue that risk is more concentrated in bank-based economies than in market-based economies, and that this means less risk-sharing and thus larger risk of a crisis. Whether one system is more exposed than another probably depends on the magnitude of the shock (more on this later). We end up with a paradox: Volatility in market prices can cause both stability and financial distress. Efficient markets demand that assets be sold quickly to their true value and that capital be efficiently distributed in the markets where it gives the highest risk-adjusted return. Intuitively such markets must allow for rational founded volatility which ensures that a decrease in true value is reflected directly and immediately in market prices. Since volatility is exactly what characterizes financial distress, it seems as if well-developed markets all things equals are more likely to experience financial distress. On the other hand volatility also ensures stability in the way that market prices are constantly corrected and thus never stays long below or above true value. 2.2.7 The financial crisis and capital access It is not relevant for this paper to describe the recent financial crisis in detail, but it is crucial to understand how it hit the financial market and especially secondary market. It is often argued that capital regulation is necessary to control the moral hazard problems generated by the existence of deposit insurance. In other words, the bank has an incentive to make excessively risky investments, because it knows that in the event of failure the loss is borne by the deposit insurance fund and in the event of success the bank's shareholders reap the rewards (Allen & Carletti, 2007). Again, the asymmetric outcome distribution triggers an actor s incentive to maximize his own utility at the expense of other market actors. Plender (2006) support this view. Diamond & Dybvig agree but emphasise that the deposit insurance also enables banks to provide liquidity insurance to NFCs so they can obtain capital in the bond market and thus stabilizing NFCs liquidity (Diamond & Dybvig, 1983). Many factors reinforcing each other can explain the crisis, but the statement above is a good starting point to understand the recent crisis. In addition deregulation of the financial markets, 21
and thus increased liquidity, motivated investors to bid up asset prices (e.g. housing prices). Bidding up prices was largely enhanced by creditors lack of observance of risk and true value of the debtors investments. Such passive behaviour was caused by risk-sharing 5 and thus a higher incentive to conduct moral hazard. This view is supported by Kaufmann and Valderrama s survey of how credit aggregates and asset prices reinforce each other: Creditor investments in securities were for a moment selling at a price above its fundamental value. Thus the perceived credit risk and associated premium primo the crisis was wrongly estimated too low. Debtors investments in relative high-risk assets (houses) were possible for a price (interest rate) that did not reflect the risk. As mentioned before, profit is earned when the underlying asset price is volatile. Return is even more volatile when the supply of assets is relatively fixed such as for houses (Allen and Gale, 1998). Combining (i) the asymmetric outcome distribution for the end-borrower/owner with (ii) the banks higher risk incentive (due to secured deposits and Credit Default Swaps selling with too low risk spreads), leads to an attractive environment for high return high risk high leverage investments (Whalen, 2007). Thus the price is suddenly not based on the fundamental value of housing, but on liquidity in the market. If investments result in lower returns than expected, some people might withdraw from the market leading to overall reduction in asset prices. Where individual investors do not act in accordance to their private information, but act as everyone else, aggregated information is not reflected in the price. If asset prices fall in this context, herd behaviour intensifies the trend and panics can occur (Cipriani & Guarino, 2008). The remaining investors can suddenly find themselves insolvent and this will be reflected on the banks balance sheets as well. Credit tightening has the same result, as it might force investors to sell their assets, because they cannot repay debt (and they cannot repay debt, because the asset prices have fallen). Thus, bubbles burst and, due to the interdependence of financial market actors and high level of systematic risk, panics occur. Housing assets are rather illiquid and liquidity shocks also happen because price reduction is larger than it would have been for more liquid assets. Liquidity shocks in the housing market proved to be (i) very contagious, i.e. the shock multiplied from one market to another and (ii) risk-sharing resulted in a shared loss (Gallegati, Greenwald, Richiarti & Stiglitz, 2008). It went from end-borrowers to banks and/or other Asset Backed Securities -buyers to credit enhancers (Freddie Mae etc) and to the 5 For an example through Asset Backed Securities and Credit Default Swaps. 22
equity investors. The latter investing in FI s did naturally loose a great deal of value fast, as the deposit claims were fixed and asset values decreased. But equity investors in the NFCmarket also lost significant amounts over a very short period of time. Here panics happened in a highly liquid market. NFCs use FI s, incl. commercial banks, to obtain capital in form of loans, bonds and equity. This means that all types of new financing were exposed to banks credit tightening. Credit access had amplified through Asset Backed Securities and efficient risk sharing until the crisis began and correspondingly one could imagine credit tightening to be severe, because these credit enhancers were directly and largely affected. To sum up: Systematic risk can be wrongly assessed since it is only fully recognised in retrospect. In the words of the former US Secretary of Defense, Donald H. Rumsfeld: As we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns the ones we don t know we don t know. 6 When the financial crisis occurred, interdependence had reached new highs due to Asset Backed Securities and credit enhancers. Before the crisis, we knew that we did not know the actual risk the system involved. We knew only that a crisis would occur eventually (as it has done several of times before), but we also knew that we did not know when and to what extent it would hit. That is, we knew that there existed unknown risks, risks we did not put a premium on. Now that we know, that we know the actual risk related to the crisis (risk still defined by historic experiences), we also know that the perceived level of systematic risk before the crisis underestimated the true risk (Whalen, 2007). However, this knowledge does not change the fact that we still know, that the actual risk today in unknown. 6 This was said at a press conference in the U.S department of Defense, the 12th of February 2002. 23
3 The Hypotheses In Chapter 3 I put forward my hypotheses as promised in problem statement 1. It is addressed what the effects of the recent financial crisis could have been on the NFCs access to and level of external finance, and how these effects differ depending on the type of external finance (bonds, equity or bank loan financing) and the market characteristics (market or bank-based financial system). 13 effects are in the end of the chapter accumulated in 7 hypotheses, because some of the effects are oppositely directed, some one way directed, and it is only their combined impact on external finance that concerns the NFCs. In addition it is only the combined impact that can be estimated empirically, and this is done in the chapters 4, 5 and 6 as promised in problem statement 2. As the reader is now acquainted with existing literature in the field, he has the ballast to understand the contribution of this paper. The problem statements and thus the effects proposed below address a so far unexplored area in the research of financial systems. The independent contribution of this paper is explained in the section below. Subsequently, the effects are presented and finally the hypotheses. 3.1 The contribution of this paper In chapter 2.2 several authors contribute to pros and cons of the market- and bank-based financial systems disregarding time, that is to say disregarding macroeconomic changes. Such still pictures of pros and cons under normal times are necessary to understand how systems react to shocks: Two different systems, both efficient and liquid, can perform equally well during stable periods, as La Porta notes. It is a bit like two smiling people, both seeming attractive in the absence of troubles. But underneath the smile two different personalities exist, and how the face changes when an accident happens depend on the underlying characteristics. Knowing that market-based systems are more liberal and bank-based systems all things equal provide better finance for small firms with little collateral, helps us to understand how the systems react differently to shocks. In contrast to the still picture literature, the literature on fragility, stability, contagious characteristics and crisis all address the situation in which somethingb changes. The literature mainly bases its observation on the well-developed and efficient financial market disregarding the degree of market or bank-orientation. Instead it draws lines between 24
fragility, stability, contagious effects and crisis in efficient versus non-efficient markets. Efficiency is closely linked to liquidity and hence terms such as risk sharing and liberalization. The continental European countries and Anglo-Saxon countries used in this paper are well-developed and thus relatively efficient, liquid and liberalized. Hence, all theories of efficient markets are applicable to these countries no matter their financial system. The literature provides useful information on how, in what order and through what channels shocks occurs. One part of the literature looks solely at the characteristics of the two systems (including efficiency) in normal times and ignores how exposed they are to changes. The other part makes no distinction between the two systems and looks instead at how exposed systems are to shocks depending on their efficiency. Of all these combinations, one is unaddressed: How developed financial markets, with different types of capital structure differ in their reaction to shocks. This unexplored area left by the current literature is what this paper seeks to address and uncover. 3.2 Proposed effects of the shock on the financial systems Formulating the hypotheses of the paper requires that we for a moment look back at problem statement 1: How would one expect new financing and level of finance for non-financial corporations to change in the event of financial shocks similar to the one occurring in 2007-2009? Would such expectation differ between market-based and bank-based financial systems? Crucial assumptions (i) Banks also borrow in order to lend. Other investors have the option to invest without meeting predefined obligations, i.e. without borrowing. (ii) I assume that market- and bank-based systems are equally developed and efficient. The price of gaining information and trading is the same. Below, the effects are presented in five sub-chapters. 3.2.1 Banks affect all types of external financing Effect 1. Investing through FIs can diminish credit risk through (i) deposit insurance and (ii) reduced agency problems and costs associated to the final debtor if his debt-concentration to banks is high. 25
i. If deposit insurance was guaranteed on 100 % of the deposit and if the government issuing the guarantee was default free, depositing money in the bank would be risk-free. If so, the only interest earned would be an inflation premium and real interest rate. No real interest rate risk premium would be offered, because it is free for the depositor to withdraw the money at any time. Thus, the banks are required to repay the depositor at any point in time. However, the government only cover a part of deposits value if the bank defaults. A government is not default free; there is always a risk that it cannot keep its promise of meeting the deposits (Greek is an example of a country where the credit risk of the government is high). In general, such risk is small. For this reason, the government insurance decreases the risk premium. Under a financial crisis and given depositors perception of deposit risk, their risk preferences might change in favour of less risky investments, because the real credit risk of securities is difficult measure. If prices drop below what the real risk suggests, such drop is irrational. However, it can still be systematic and thus unavoidable. Since security investors obtain knowledge of the real risk via signalling (and because signalling under distress also includes irrational behaviour), the effect of rational risk assessment and irrational risk assessment can be difficult to distinguish. Thus the real price, which the market should correct to over time, is difficult to assess. Even more so as irrational drops can have real economic consequences which indeed must be assessed and factored into the price. A precondition for this argument is that one believes the secondary market to react more severely and more irrationally to the financial crisis (arguments follow later). ii. The agency problems are expected to increase in the secondary market in parallel to a drop in market value of bonds and equity. Agency problems (i.e. the problem arising when the debtor, here the NFC, chose actions to increase its own utility at the expense of the creditor) increase as the solvency degree of the NFC decreases. Whether the NFC s incentive to conduct moral hazard is larger for owners than for creditors depends on the owner s power over the management. In any case, the management will have a larger incentive to make risky decisions and that will increase the risk of default. As market players find signalling less reliable and the probability of agency problems higher, they might look for information sources using less signalling, as for example banks. NFCs usually owe a larger amount to one bank than to any other creditors and therefore the relationship to the bank is more crucial to the NFC. This decreases the NFC s incentive to create agency problems for banks. Thus, as the perceived risk related to agency problems 26
increases in the security market and the real risk is more difficult to assess, creditors might shift to investments where both problems are reduced, that is in deposits. As a result of effect (i) and (ii) above, banks liquidity improves resulting in easier and cheaper access to finance through banks. Assuming bank s keep the asset portfolio constant such effect would favour bank-loan finance to NFC s. The effect is expected to be higher in market-based countries as the systematic risk and hence portfolio volatility is higher. Effect 2. FI s activities can themselves create credit risk for the depositors as under the recent crisis when high gearing by the banks combined with risk-sharing resulted in moral hazard by the banks. The higher the default risk of banks, the higher the risk is that depositors do not receive their savings, because deposits are only partly insured. If depositors had underestimated the credit risk of the banks assets, depositors withdrew their money when re-assessing the credit-risk. The remaining depositors require a higher liquidity risk premium for keeping money at the saving accounts. The effect would be the exact opposite of Effect 1: Bank-loans will be most affected if banks seek to meet their liabilities by decreasing all assets with the same degree. They do this by selling their securities, stop buying new securities, redeeming loans and stop granting new loans. Since banks are not the only security traders, but the only bank-loan creditors, the total value of NFCs securities in the economy will not decrease as much as the total value of bank loans. One exception is if other security holders also reduce their balance of securities with the same degree as banks. In addition bank loans are affected more, because higher risk premium demanded by depositors is directly transferred to loans, not securities, as other investors than banks set the price in the secondary market. Effect 3. Liquidity is largely created through lending. If a bank lends money to another bank it receives interest/income. The second bank then lends the money to a third bank and receives interest for this. The third bank does the same thing and so on. Hence the same monetary base shifts hands many times and this is exactly how they all create profit. Some of the money is also lent to NFCs and private debtors, who invest them or spend them. The point is that interbank lending is the main source of short-term capital, and thus it significantly affects liquidity, stability and profit of banks. Lower interbank lending reduces liquidity: The effects are: (i) Higher transformation risk associated with especially long-term investments such as bank loans. Banks partly use interbank lending as a bouncer if they cannot meet 27
(ii) deposit withdrawal and serve the debtors in a specific period. Thus interbank lending is a crucial component in reducing withdrawal risk (and thus liquidity risk) and the complications occurring when matching short-term liquid liabilities with long-term illiquid assets. Lower access to inter-bank lending forces banks to reduce liquidity risk by shifting assets to be more liquid. The effect is similar to Effect 2. Thus, seen from the NFC s point of view, it should result in a larger value drop in bank loans than securities. Secondly as bank loans are the least liquid ones the banks are expected to reduce its stock of them further. It is important to notice that fewer security and bank-loan investments first are reflected in the market prices of securities as these are easily sold. Illiquid bank loans are reduced more slowly, as reduction more happens through limiting new loans and it is more drastic measure to redeem loans than to sell securities. Besides the fact that higher risk requires a more liquid asset allocation, it also reduces the banks overall access to capital. Every time one bank lends to another the assets and the liabilities of the borrower increases and vice versa. Therefore as short-term lending is reduced and liabilities are repaid, the total asset value for banks decreases. Consequently, the bank is forced to reduce all types of assets, not only transform them into something more liquid. Again, the effect is identical to Effect 2, thus enhancing the overall strength of the effects. Effect 4. If depositors behave as predicted in Effect 2, money could also shift to low risk investments such as treasury bills or government bonds. Such behaviour could have the following effect: The price on treasury bills would increase thus reducing the real interest rate premium.. As the yield on treasury or government bonds are used as a benchmark for return on other assets, the price increase will also increase prices on NFC securities as the required interest rate falls. In reality such effect is probably limited, especially for equity valuation. The yield on NFCs security is mostly linked to systematic risk associated with the specific company and not the underlying yield on risk free assets such as government bonds. Therefore, the volatility in yields mostly relates to company risk and the effect is predicted to be limited. Effect 5. Banks all over the world invested in American Asset Backed Securities before the crisis (IMF WEO, 2009). Defaults in the underlying assets of Asset Backed Securities and value reduction in the remaining securities resulted in: (i) Reduced liquidity, as the overall asset value fell. The fall was twofold: 28
(ii) a. First, the value of defaulted securities was limited to whatever the debtor had left after liquidating its assets. b. Secondly, the value of securities that did not default was reduced, because the perceived default risk increased. Given a fixed portfolio strategy, this reduction in assets especially affected new bank loans. This should be directly observable in net-transactions. As a consequence of reduced liquidity and lower asset value, banks solvency was reduced. Thus the response to lower solvency does not appear in the first period of the crisis, but only after bank assets have experienced their first decrease in value. Low solvency is expected to motivate banks to: a. Sell off risky assets to fulfil the Basel requirement, which is the McDonough solvency ratio 8 % (Basel Committee, 2005). Liquidity is enhanced when assets are sold in favour of solid money. Securities are more liquid and therefore more volatile than loans. This speaks in favour of selling these instead of loans for two reasons: First, liquidity normally reduces the risk that assets are sold with too large discount (if we for a second ignore irrational behaviour which is also a consequence of liquid markets under specific circumstances). Loans can per definition not be sold unless they are securitized, and doing so under the crisis could result in huge discounts. Alternative they could be redeemed, but also this can result is large losses. Secondly, removing very volatile assets can improve the McDonough solvency ratio. Thus, securities are sold in favour of bank loans. Of securities, equity is expected to be sold rather than bonds, because it is more volatile. b. For the assets which are not sold or redeemed and stored in reserves or T-bills, the banks are expected to shift strategy and: o invest in more liquid assets (meeting the Basel requirements) o invest in assets with higher risk of default (more risk is preferred as the solvency degree decreases). The net-effect of lower liquidity and reduced bank solvency is expected to impact on NFCs level of finance and new finance in the following ways: NFCs overall level of external finance will decrease: Partly because the market value decreases (due to default and high risk premiums) and partly because banks sell securities, redeem loans and reduce new investments and lending to increase their cash holdings. For the remaining assets (that are not placed in 29
cash), high risk liquid assets are preferred by the banks. All liabilities have become more risky since also NFCs solvency degree has decreased. Yet, especially equity and then bonds have increased their risk premium (see Effect 8 for elaboration). Securities are more liquid than bank loans, so banks are expected to increase their holdings of securities on expense of bank loans. The effect for the NFCs is a rebound in equity and bond prices, while bank loans are expected to decrease steadily (as some are redeemed and new financing is rejected). The change security financing for NFCs, also depends on the behaviour of other security holders and their share of the securities market. Banks do not set the market price by themselves. Effect 6. Banks granting loans to NFCs traditionally create a strong relationship with these. The loans granted are usually significant to the bank as well as the NFC in order to reduce marginal cost (for both), moral hazard (for the bank) and the interest rate (for the NFC). Enhanced short-term volatility in the security market can - under a financial crisis - reflect speculations and result in valuation not reflecting true value. A rational change in expectations to either cash flow or risk results in a larger value reductions for high risk portfolios than low risk portfolios. Large value reductions will (for reasons explained later) enhance the probability of irrational volatility in prices. Thus high risk portfolios will be more exposed to short-term irrational volatility stemming from signalling. Banks normally do not use signalling to gather information on bank-loan debtors. Instead they rely on screening and monitoring. This method enables them to focus on rationally assessed risk without being affected by irrational signalling and herd behaviour. It is therefore likely that banks move towards their competitive advantage; that is transforming deposits to loans. In addition the interdependence between the banks and the NFCs tends to be stronger than between NFCs and other security holders. This reduces the NFCs incentive to conduct moral hazard and increases the banks incentive to grant them the loans needed and thus reduce the default risk of the NFCs. The effect for the NFCs is (i) relatively stable flow of new loans and (ii) value reduction in securities as banks sell these at the expense of loans. 3.2.2 Different levels of systematic risk Effect 7. Different assets tend to hold different levels of systematic risk. It is important to emphasise that risk relates to volatility in the cash flow that the investors and creditors expect to receive in the future. Volatility in expected cash flow is equivalent to the volatility in the cash flow generated by the NFC even if creditors and investors do not hold the investment until maturity, but sell it at the market price. The volatility in market price should therefore correspond to the volatility in the underlying cash flow. If not, irrational price determination 30
plays a role. Volatility in liberal markets has the most systematic risk, because market players are more interdependent. Liberalization of markets increases competition and thus lowers the cost of borrowing, including transaction costs. The results are higher demand of capital as more debtors experience positive NPV projects. In addition lower transaction costs make it easier and cheaper to invest. This enhances the liquidity and correspondingly lowers the liquidity risk. Higher demand and higher supply increases the real money supply (real M3) 7 via gearing. This increases real GDP (which is a good thing), but it does so by enhancing the interdependence between different market actors and thus it enhances the systematic risk. Systematic risk all over the world can have been misinterpreted before the financial crisis, because (i) humans place too much weight on recent events and (ii) investors become increasingly confident in the trend if prices move consistently for a longer time (Brealey et. al, 2008, p. 368-71). Prices had increased over a long time and consequently the probability of negative outcomes was underestimated. Thus, even if the investor recognizes the normal distributive nature of returns, the expected return was overestimated. (iii) The variance in portfolio returns equals, σ 2 = (, where = expected return and = actual return (Brealey et. al, 2008, chapter 8). The expectation to the variance is often estimated with the use of historic returns. Just before the recent crisis, the expectation to the variance (risk) in the market portfolio was underestimated as the historic time horizon was limited to recent returns. Therefore risk was wrongly interpreted as very low. 8 The change in perceived credit risk is expected to be higher in market-based countries, since systematic risk is higher. When systematic risk is higher, so is the real volatility. Therefore the difference between the expected volatility (perceived risk) and true volatility (real risk) is higher in market-based countries. The true volatility is not expected to be reflected in the prices before the crisis in any country, but the miscalculation is expected to be larger in market-based countries. After adjusting the perception of risk, investors require an additional risk premium. Not only is the premium itself higher in market-based countries. The additional premium will also have larger impact on the market price since the market-based countries were perceived as more risky to begin with. This argument is valid for bonds, equity and bank-loans alike. Effect 8. The increase in credit-risk premium (Effect 7) is expected to decrease the value of equity more than the bond value, and the bond value more than the value of bank-loans. The 7 http://stats.oecd.org/mei/default.asp?lang=e&subject=14 8 Historic volatility in prices does not necessarily tell anything about the future, and if it did, how far back should we look? What events are to be repeated? 31
reason why the value of equity is decreased more than bonds and bank loans is that: (i) Equity owners require the highest premium per unit of credit risk, i.e. given a certain default risk. Thus, whatever such perceived risk is changed to, the change will affect the premium of equity owners the most. (ii) Higher duration for equity than for bonds and bank-loans leads to a larger decrease in equity prices given the same change in risk premium for all types of finance. It is only the perception of default risk that increases 9 - not other types of risk. All creditors, no matter whether they lend through bonds or bank-loans, are equally entitled to the NFC s assets in the event of default. Therefore, increase in credit risk premium is expected to be the same for all creditors. The difference lies in two factors: Bonds will normally hold longer duration than bank loans. Thus a given increase in the premium will result in larger value reductions for bonds than for bank-loans. Secondly, the risk of bank loans is higher due to transformation risk in all its aspects. An additional risk premium will therefore have a lower percentage impact on the value of bank loans given the same time to maturity. Effect 7 is thus expected to impact the required return of equity percentagewise more than the interest rate of bonds, and the interest rate of bonds is expected to increase more than that of bank-loans. The effect would be readable in the level of external finance. However, as long as the premium is rationally assessed larger value reductions in equity and then bond prices should not affect NFC mix of new external finance. What it can impact is the amount of profitable new investments and in that case we would mainly see the effect in the value of net-transactions. Effect 9. The contagious effect of the crisis was heavily reinforced by risk-sharing through Asset Backed Securities and Credit Default Swaps. They are a result of a more liberal system (and creative minds), but Effect 7 and Effect 8 will also exist even though these two financial instruments did not exist. Systematic risk in the US was therefore not only higher due to higher involvement of FIs in securities (which is motivated by a more liberalized financial system), but also due to additional risk sharing motivated by Asset Backed Securities and Credit Default Swaps. However, it is not obvious that market-based countries per definition lead to a larger use of Credit Default Swaps and Asset Backed Securities. It is only a precondition. Thus, I do not believe these two types of financial engineering play a larger role in a general comparison of the two types of systems than they do in Effect 5. 9 It is true that liquidity risk also increases in the period of financial distress, but such risk is not directly linked to the perception of risk associated with the investment. It is more a symptom. 32
3.2.3 External financing and panics under financial crisis Underlying expectation If the assets in a market are strongly positively correlated, the standard deviation of the most diversified portfolio will be higher than if the assets were less correlated. The most diversified portfolio only holds systematic risk. Systematic risk is unavoidable risk (Brealey et. al, 2008, p. 188) and the portfolio that only holds systematic risk is named the market portfolio. Thus, in a market where the standard deviation of the market portfolio is high, the systematic risk is high and the reason is that the individual assets in the portfolio are highly correlated. For reasons already explained, the systematic risk of the market portfolio in marked-based economies is higher than in bank-based countries and so is the standard deviation for which investors require a premium. The argument is valid even if we assume investors react rationally to market information. Panics are stimulated by lack of correct or sufficient information and the fact that signalling (not always rational) plays a major informational role in valuing securities. As Allen and Gale note, such system force people to rely on market information to an extent that can create irrational herd effect and hence increased volatility. The liquid nature of the market insures more than two traders recognize the current price, and this is why signalling generally is perceived as a valid source of information. Panics related to signalling occur for two reasons: (i) That the public rationally accounts for the irrational nature of humans and (ii) that humans simply sell because they are afraid of losing more money - disregarding the probability of additional losses (Brealey et. al, 2008, p. 371). Since systematic risk is higher in market-based countries, the Value at Risk (VaR) is higher in market-based countries. The more money people rationally lose under financial distress, the more it hurts and therefore it is rational to believe that investors sell, because investors are expected to think irrationally, i.e. they disregard the true value of the assets. In that case, VaR for the market portfolio also increases due to irrational behaviour motivated by signalling and it increases more the higher the VaR was at a base point. I would argue that panics are more likely when the actual loss related to signalling is bigger. In other words, panics are more likely in market-based countries. Irrational value drops are not the same as panics it depends on the degree of the irrational reaction. Furthermore, panics can themselves create real economic slowdown (see the next two effects below). Effect 10. Market panic affects the liquidity of assets negatively. The liquidity is therefore expected to decrease, i.e. it becomes more difficult to sell and buy assets fast and for a 33
fair/true value. When liquidity decreases, the associated risk and liquidity-premium is expected to increase. The effect is largest for high risk and liquid financial markets. The reasons are that: 1. Panics are more severe and likely in high risk markets, because high risk portfolios hold a higher systematic standard deviation in return. As it is explained above, I believe high systematic volatility increases the probability of panics. A given increase in the perceived default risk of each asset in the market portfolio will decrease the value of the market portfolio more when systematic risk is higher and thus it hurts more. This argument assumes that the relative change in perceived default risk is the same for all assets in all countries. A second argument is then that the change in perceived credit risk is larger in market-based economies (Effect 7) thus increasing losses and the probability of panics. 2. Liquid markets, are on one hand the optimal environment for stability and efficiency as long as the underlying volatility it not too severe. But they can also be the most volatile ones for the following reason: Liquidity is a precondition for signalling to be regarded as a reliable information source. When investors trust signalling the market prices are generally regarded as correct. If the market, for some reason, loses confidence in the price, the price will drop heavily because; (i) many investors must agree for a price to drop heavily and thus there must be a good reason for it. (ii) If the investors realize that irrational price drops are a consequence of the first price drop, the price will drop even further. Both reasons rely on signalling as the primary source of information. The result can be herd behaviour and thus panics. Liquid markets can also have a stabilizing effect. Whether liquidity generates panics or creates stability is a question of how severe the first drop is. In 2008 stocks dropped so dramatically that it is often seen as panic reaction in part. Panics are to some extent believed to be caused by high liquidity. Market-based countries are more liquid and therefore believed to experience more panics. Thus, the value of assets decreases more as a function of higher liquidity and higher systematic risk in market-based countries and especially with regards to equity as it is the most risky type of financial investment. In reality enhanced liquidity premium and panics happen simultaneously and it will merely appear as one large drop in market value. Effect 11. Even though panics affect the whole market through signalling and herd behaviour, panics hit the security market directly. The reason is that securities are traded in the secondary market where signalling is directly used and where liquidity is higher. 34
Investments that are calculated as profitable based on rational risk assessment can be impossible for the NFC to launch, because security investors require a higher risk premium, based on irrational risk assessment. The result is a potentially large discrepancy between the capital needed to invest and the value investors want to pay for the expected future cash flow. All types of external finance are expected to be more expensive in the period where panics are most severe. However, panics result in irrationally high discount rates for equity in particular, but also for bonds which speak in favour obtaining capital through bank loans. The ability of banks to invest in loans decreases as a function of market panics, because their overall asset value decreases. Still, lowering bank loan investments is a rational response to secondary market panics. Given a certain level of risk preference, and assuming that banks do not gamble banks might change their preferences from investing in securities to investing in loans. The reason is first that rational risk assessment is easier to do. Secondly, the risk of NFCs conducting moral hazard on the expense of bank loan creditors is less, as NFCs are more dependent on banks as creditors to loans than security holders. The effect is expected to be stronger in market-based countries due to higher systematic risk and therefore value at risk increases by irrational behaviour. 3.2.4 Non-risk related change in required return and interest rate Effect 12. Central Banks all over the world bought up bonds and as they executed expansionary monetary policy to stimulate investments and spending. As bonds are bought by central banks, money aggregatesincreases and the effective interest rate decreases. The latter is a function of higher bond prices. Lower real interest rates counteract the enhanced risk premiums associated with liquidity and credit risk. In addition, the slowdown in real economy reduced the inflation premium again counteracting the risk premiums. The overall effect is believed to be lower risk-free interest rates, that counterbalance enhanced risk premiums to an extent that has visible impact on the yield on NFC bonds and thus the market value of these. The same tendency is assumed to be visible for bank-loan values as their interest rate also is highly dependent on changes in the real interest rate and inflation expectations. However, as banks charge a higher risk premium than bond-holders (given the same time to maturity), the impact of a lower risk free interest rate is expected to be less. As the cost of capital on equity is mainly a function of risk-compensation, decreases in real interest rate and inflation premium is expected to have more moderate effects on equity valuation than for bonds and bank loans. 35
The central banks are not expected to intervene before the first slowdown appears, i.e. before the crisis has been a reality for some time. The same is true of the effects. 3.2.5 The NFCs new finance unrelated to risk and required return Effect 13. Systematic risk includes the fact that financial distress spreads to the real economy, thus affecting the consumers and then the NFC cash flow. The real economy is usually affected over a longer time once the slowdown sets in, because it include factors such as higher unemployment rates, lower consumptions and lower production. It is a vicious and self-fulfilling spiral and it takes time and huge efforts to break the trend. The security market knows all this and as security prices are a function of expectations to the underlying future cash flow, the market tries to factor lost revenue into the current price. Therefore the prices decrease given a perceived risk level. NFCs make the exact same calculation and reduce their expectations of the cash flow of new and old investments. Thus, given a certain discount rate, less profitable investments occurs and this reduces the need of new finance. Less need of new finance is expected to reduce net-transactions in all types of external finance, but not until the crisis has affected the economy or at least is expected to affect it. 3.3 The hypotheses in short All effects are stated as isolated arguments. Some of the effects are oppositely directed, some one way directed, and it is an assessment of their combined impact that is summarized in 7 hypotheses. Hypothesis I. Effect 1, 2 and 3 argue for and against banks enhanced capital access. Summarizing effect 1 and 2, I would argue that depositors lack of confidence in banks is larger than their lack of confidence in the secondary market, thus the combined effect is in favour of bank-loans. Effect 3 on interbank lending on the other hand is expected to have significant impact on total liquidity and liquidity risk. In order to enhance liquidity and thus reduce the liquidity risk, all types of assets are expected to be reduced. This will mostly affect bank loans, unless security holders others than banks reduce their holding of securities with the same degree as banks do. Bank loans are the least liquid type of financial assets, so they are reduced more slowly than securities. The total effect of reduced interbank lending is expected to countervail higher consumer deposits. The reduction in the NFC s securities are at first more severe, but reduction in bank loans will continue over a longer time. 36
Hypothesis II. Effect 4 states that government bonds are expected to serve as an appealing alternative to deposit savings thus lowering the risk free interest rate premium, the effective bond rate and equity returns. The impact on equity is expected to be limited, as the interest rate premium accounts for a small fraction of the total cost of capital. Effect 12 adds the effect of expansionary monetary policy. This results in a higher demand of bonds which lowers the interest rate premium even more. The total effect is expected to be significant simply because the agenda of central banks was to stimulate investments and consumption. However, the effect is expected to be greatest for bond levels, then bank loans levels and the least affected capital source is expected to be equity. The effect is not expected until the central banks assess that the risk of real economic slowdown is present. Hypothesis III. Effect 5 concerns the impact of banks on the financial market through defaults in Asset Backed Securities. The overall level of NFC external finance will decrease: Partly because the credit risk premium increases and partly because banks sell securities, redeem loans and reduce new investments and lending to increase their cash holdings. For the remaining securities and bank loans, held by banks, high risk liquid assets are preferred. The risk premium for NFC securities has increased while they are more liquid than bank loans. This makes securities more appealing for banks. Thus, banks are expected to increase their holdings of securities at the expense of bank loans. The NFC equity and bond prices are expected to rebound, while bank loans are expected to decrease steadily. The change in security financing of NFCs, also depends on the behaviour of other security holders and their share of the securities market. Hypothesis IV. Effect 7 states that a basic higher level of systematic risk occurs in marketbased countries, because a higher degree of liberalization increases credit aggregates and thus enhances interdependence. This results in a larger rational change in risk perception for market-based countries. Evidence shows that investors assess expected returns and risks using few recent observations. Consequently, they put too little weight on the danger of low returns, as good times had lasted long when the crisis occurred. Since returns before the crisis were expected to be higher in high risk (market-based) countries, so was the miscalculation of downside risk. The effect was a larger increase in required risk premium for all financial assets. Furthermore Effect 8 emphasises that especially the required risk premium (and thus value reduction) associated to equity would increase. The required risk premium would increase less for bonds and least for bank loans. As long as the change is rational it should not 37
change NFC s preferred mix of new external finance. However, it can impact the amount of profitable investments thus resulting in lower net-transactions for all types of finance. Hypothesis V. Effect 10 argues that panics stemming from changes in perceived credit risk enhance the liquidity risk of the financial markets. This enhances the liquidity premium and decreases the price even further than what the change in credit risk did. The effect is expected to be largest for: a. Financial assets subject to high systematic risk; that is assets in market-based countries and especially equity and then bonds. The probability of panics is expected to increase as a function of systematic risk, because high systematic risk results in a larger percentage loss of the portfolio and this hurts more. Investors sell to avoid further loses. b. Liquid markets where signalling plays a larger role. Reliance on signalling can reinforce irrational volatility and thereby increase liquidity risk. If the market, for some reason, loses its confidence in the price it will drop heavily because; (i) many investors must agree for a price to drop heavily and thus there must be a good reason for it. (ii) If investors realize that irrational price drops are a consequence of the first price drop, the price will drop even further. As liquidity is expected to be larger in market-based economies so is the effect. Hypothesis VI. Effect 6 argues against Effect 5 while introducing the role of irrational behaviour. One thing is that banks might increase their preferred risk level. Another thing is if the market volatility is based on irrational reactions. Under such circumstances profit can be better earned by using direct screening and monitoring, that is by granting loans opposed to investing in securities. Effect 11 suggests that NFCs for the exact same reason prefer loans since the price of these allows for a more rational risk assessment and thus more profitable projects. The risk of NFCs conducting moral hazard is less severe because the interdependence between the bank and the NFC is greater. This and a larger interest in keeping the NFCs running motivate banks to grant loans. The effect is expected to be largest in market-based countries where panics are expected to be more severe. Hypothesis VII. Effect 13 suggests that at a certain risk level, NFCs estimate fewer projects to have a positive NPV, because the prospect of income in the following years are lower than before the crisis. This will reduce net-transactions for all types of finance and the market price of equity. 38
4. Empirical foundation This chapter provides a description of the procedure used to answer the problem statement 2 as well as an introduction and discussion of the variables and the technique applied. The main focus will be on the statistical implications of the variables and observations, as they affect the estimation method applied. Systematic tests of all possible statistical problems are excluded and the focus is on the most important problems. A recap of the research questions are presented below: A. Does the overall level of finance change under the crisis as opposed to before? B. Does the mix of the three kinds of new finance change during the crisis as opposed to before? C. How do the results of A and B differ between bank and market-based economies? 4.1 Structure of reporting the results The structure will take its point of departure in the three research questions. All three questions are set forth in the context of the financial crisis. Research question A and B serve dual tasks: Question A addresses changes in the level, i.e. balance values of all three types of finance. Each type of finance is treated separately and not relative to each another. Question B addresses the mix of refinancing or new financing, measured by net-transactions. Thus, the interest lies in whether e.g. net-transaction for bonds relative to net-transactions of stocks changes during the crisis opposed to before. The focus is contrary to research question A, where the focus is on total changes. Answers to both questions serves as the foundation for answering research question C where both the level and the structure are analysed in order to account for any differences in these variables depending on the characteristics of the two financial systems. 4.2 Data collection To answer question A, B and C we need a country sample and question C requires a distinction between market- and bank-based countries. In addition a definition of the relevant variables is presented: First the variables relevant for measuring change in level and then the variables to measure net-transactions. Parallel to a presentation of the variables, I present the estimation technique that is used to treat the variables in a manner that ensures valid results. All these considerations are presented below. 39
4.2.1 Data sample The sample consists of observations from 8 western countries. Four are bank-based and four are market-based. Chapter 4.2.2 provides an explanation of what countries that are used and why they are used. The observations are registered quarterly from 2003Q1 and until 2009Q3. 2003 is chosen as the first year, because data prior to this date is not available for all countries. The total number of observation (data points) per variable equals number of quarters * number of countries = 27*8 = 216 observations. However, as some variables are lagged values (the value from the previous period is used), the total number of observations is limited to 200. One potential problem originates from the time horizon: The results cannot be used for forecasting. The sample provides cross-sectional data, i.e. data collected over time for different countries. To the extent these results are appropriate for forecasting, the purpose would be to predict shock effects. Stationarity, i.e. a constant mean, variance and trend over time, is a precondition for forecasting (Gujarati, 2003, p.797). To address if the distribution is stationary over time we need a sample over more years. Where a sufficient number of years is unavailable, it is not possible to conclude if the process is stationary and thus we cannot use it for forecasting. However, it makes no sense to forecast the results of question A and B since the recent financial crisis was unique, just like previous crisis. As the outcome in question C builds on results from question A and B these cannot be generalized for forecasting either. But the financial distress appeared to be quite systematic and affects both market- and bankbased countries. Thus, to some extent the results can yield a general insight into the difference between market- and bank-based economies and how they react to systematic risk regardless of how the shock was triggered. 40
4.2.2 Country determination and categorisation Whether a country is characterized as bank or market-based is a matter of degree. Both systems rely on equity, bank-loans and bonds. There are several ways of defining a bank versus a market-based system, but in this paper a ratio based on liability accounts of NFCs balance sheet is created: Equation 4.1 Degree of market-orientation, per year and per country All values are quoted at market value, as stated on the balance sheet of NFCs. Bonds consist of both short and longterm bonds as well as mortgage. Equity is a sum of shares, retained earnings and other equity items. Bank-loans also consist of short and long-term values. Balance sheet values are periodically corrected for changes in value, nettransactions and accounting changes. The sum of these three types of changes equals the total change in the balance sheet accounts. t = quarter and C = country. Source: OECD statistics, repeated in Appendix 1. The ratio is estimated on a yearly base from 1996 to 2008 for 18 western countries. These were the countries available in OECD statistics. By choosing western countries the impact of inefficient systems, different cultural origins and social characteristics is largely isolated. Western countries also differ on these parameters, but not to the same extent as if we compared with e.g. Asian countries. Statistical validation increases, as irrelevant differences (that could be partly correlated with the variables of interest) do not disturb the picture. Yet several countries are needed to highlight deviation in the variables of interest and that such deviation is statistical significant. Besides calculating D for each year, from 1996 to 2008 is calculated for each country. Equation 4.2 Average degree of market-orientation, per country is the Average Degree of Market-based System from 1996 to 2008 for country C. It is calculated as the sum of all D s (Equation 4.1) for country C over time divided with the number of years (14 years). Source: OECD Statistics, repeated in Appendix 1. To some extent it is believed that the changes in the capital structure, D, will be random over time. In could be misleading only to focus on the capital structure in 2006 if the purpose is to conclude on a country s general financial preference. Still, the actual structure just before the crisis is more important than the structure in 1996. 41
Therefore a constant is calculated which recognise the past, but put more weight to the capital structure just before the financial crisis, i.e. ultimo 2006: Equation 4 3 Weighted degree of market-orientation The ConstantC is product of and. It is used to label the countries as market- or bank-based. Thus the Constant expresses a degree of orientation. Below the actual Constant for each country is calculated in order to categorize them as either bank- or market based: Table 4.1 Categorization of countries Constant USA 48.47 Belgium 11.24 Finland 9.18 France 8.11 Poland 5.44 Hungary 5.3 England 4.49 Slovak Republic 3.92 Sweden 3.27 Norway 2.68 Netherlands 2.51 Austria 2.23 Greece 2.13 Germany 2.05 Portugal 1.81 Denmark 1.43 Italy 1.2 Spain 1.1 Average excl. USA 4.01 Market-based countries Bank-based countries The table shows the Constant for all 18 countries. The higher the Constant, the more market-based the financial system. The calculations appear in Appendix 1 parallel. The dashed line indicates whether a country is more of less market-based than the average level, excl. USA. The US is excluded as its constant equals 200 and therefore is treated as an outlier causing the dashed line/the average to be misleadingly high. Countries above the dashed line are categorised as market-based. The values highlighted in red show the eight countries used in the rest of the analysis. These are chosen, because they are the ones with most information available. Choosing countries spread more or less equally over the whole range demonstrates that bank or market-based is matter of degree. Table 4.1 highlights the country sample used in the rest of the paper. That is, US as the most market-based country and Spain as the most bank-based country. It should be stressed that the Constant tells nothing about whether a country s securities take the form of equity or bonds. 42
4.2.3 Critical assessment of the country selection The sub-chapter is meant to highlight the complications of the chosen method. Often, complications are unavoidable and the task is instead to qualify them. The Constant described above is used to identify several countries financial structure. One could argue that more weight should have been put into the recent structure, but doing so would itself be based on pure intuition. England has changed its structure enormously during the past years (from market-based to more bank-based) and England could easily have entered the bank-based countries if more weight was put into the recent years structure. The implications of the chosen approach can be that we do not see a clear picture of the countries reaction to shocks. In this case, we will go back to the chosen categorization and reassess it. D T,C does not address whether its value is high due to a high levels equity or a high level of bond financing relative to bank loans. This does not imply that equity and bonds are considered as similar financial instruments. Later on, in question C the distinction between countries who use more equity or more bond financing will be made. 4.2.4 Measurement techniques and variables 8 countries and 200 observations per variable makes it possible to conduct Ordinary Least Square (OLS) regressions and measure correlation between level data, mix of new financing, capital structure, individual countries and time. No causality is evident. Causal arguments are introduced in the analysis and discussion. Two types of regressions are created and tested at a 5 % significance level: Regression I supporting research question A. Three sub-regressions are created, one for bonds, one for equity and one for bank loans. For all explanatory variables in all regression: H0: β=0, H1: β 0 Regression II supporting research question B. Six sub-regressions are created as the mix of new financing originates from 6 different types of net-transactions: Short- and long-term bonds, equity and bank loans. For all explanatory variables in all regression: H0: β=0, H1: β 0 Research question C will be answered using Regression 1 and 2. 43
Regression I presentation of the explanatory variables The variables used for regression 1 on bond level are presented below. All sub-chapters under Regression I also use bond level to explain the method. The regressions concerning equity and bank loans follow the exact same procedure and measurement techniques. Appendix 2 shows the observations used to estimate the regressions and their underlying data source. Table 4.2 presents an overview of the variables, i.e. only the names and components are displayed. Table 4.3 present the properties and a brief explanation of each variable. Table 4.2 Short name and components of variables in Regression I Variables Name Components Dependent variable Explanatory variables Bond level Bond- ratio (B/TL) Time dummies (D T ): D 2004, D 2005, D 2006, D 2007, D 2008, D 2009 lag(log(b t,c )), where B = Bond level, t = Quarter, C = Country lag(log(b t,c / TL t,c ), where TL = total external finance = sum of equity, bond and bank loan level D 2004 =1 if T = 2004 (T quarters) and D 2004 =0 if T 2004 Same logic goes for the other time dummies. Interaction term: Cross time -terms (CTT) Country dummies (D C ): D Denmark, D Spain, D England, D France, D Germany, D US, D Finland CTT = D T * lag(log (B t,c / TL t,c ) D 2004 =1 if T= 2004 and D 2004 =0 if T 2004 Same logic goes for the other time dummies. D Denmark =1 if C= Denmark and D Denmark =0 if C Denmark Same logic goes for the other country dummies. The dependent variable and explanatory variables used in research question A for bond level. Three main explanatory variables are used: Bond - ratio which indicates the NFCs capital structure with respect to bond level. In addition a Time dummy is included to measure if significant changes in level happened over time. A Country dummy for each country is included to control for country specific characteristics other than capital structure. The interaction term is per definition variables created as a product of two other explanatory variables. It highlight if the combination of two explanatory variables tells a story different from the simple sum of both. As the time dummy is quoted in years and Bond - ratio is quoted in quarters, DT takes the value 1 if a quarter (t) is included in the respected year. Table 4.3 elaborates on the interpretational meaning of each variable and their properties. Similar variables are used for equity and bank loans. However, no variables are logged with regards to bank loans. 44
Name Table 4.3 Properties and brief interpretations of variables in Regression I Properties and brief interpretation Bond level Values are quoted in 2003 prices and altered to $ prices registered quarterly. They are balance-values and they change based on accounting requirements for market value alignment, net-transaction and other technical changes. Ultimo period quotation. Bond - ratio Same properties as for bond level. The variable is lagged and represents the primo periodical capital structure. This serves an interpretational purpose explained later. Significant coefficients tells how must log(lag(b) will change as lag (log(b/ TL) change with 1, disregarding point in time and country in question. Time dummies The reference variable is 2003. T = years unlike the observation that are quoted in quarters (t). Significant coefficients tells how much log(lag(b T )) differs from log(lag(log(b 2003 )). Country dummies The reference variable is Norway and any significant coefficient tells how much log(lag(b C ) differs from log(lag(b Norway ) at any point in time. Cross time-term The reference variable in the cross time-term of 2003. Any significant coefficient tells how lag (log(b T,C /TL T,C ) =1 impact lag(log(b T,C ) differently than in 2003. Table 4.3 present the interpretation and properties characterizing each variable: Bond - ratio, time dummies and country dummies are referred to as constitutive variables, i.e. variables that are included in the interaction terms. The same properties and interpretational meaning exist for the sub-regressions of equity level and bank loan level. However, no variables are logged for bank loan observations and therefore estimators for explanatory variables on bank-loans refer to the natural scale whereas the estimators for equity and bond level referred to the logged scale. Observations for bond and equity levels are logged, as it is necessary to create normal distributed residuals. If the residuals are not normal distributed, the coefficients will not be BLUE (Gujarati, 2003, p.79) if the observations are logged. This problem does not exist for bank loans. If the observations of bank loan were logged they would not be normal distributed anymore. See Appendix 3 that illustrates the distribution of observation for bond level, equity level and bank-loan level, both logged and in the natural scale. The coefficients must be interpreted differently depending on whether the variables are logged or not. This affects the analysis and discussion throughout the paper. 45
The purpose and further use of the explanatory variables 1. The intercept captures the level that is expected in Norway, in 2003 if lag(log(b/tl)) equalled zero, i.e. if Norway used no bonds. First, this makes the intercept unintuitive and only useful together with a lag(log(b/tl)) > 0. Secondly, it does not contribute to the understanding of how economies with different capital structures reacted to the crisis, as (1) capital structure is not included in the intercept and (2) the years in which the crisis occurred is not included. Therefore, I will not spend any time interpreting its meaning in chapter 5, but the intercept estimator will be presented as it is used as a reference variable. 2. The country dummies highlight country specific characteristics that determine the level, regardless of time and capital structure. It is important, because potential correlation between the country dummy and capital structure affects the estimator of lag(log(b/tl)) less than if the dummy variables were excluded (more on this below). This is the only purpose it serves. It enables us to ignore country specific characteristics in determining how different financial systems react to the crisis. Therefore, I will not spend any time interpreting its meaning in chapter 5, but the estimators will be presented, as they are used to strengthen the validity of the other estimators. 3. The time dummies, D T, show how much the level differed from the 2003 level, regardless of the country in question and regardless of the capital structure. It is used to answer research question A and therefore it is interpreted in chapter 5. It is also used as a constitutive variable for the cross time-term. 4. The bond - ratio, lag(log(b/tl)), tells to what extent the bond level is expected to increase as ratio increases, regardless of time and country specific characteristics. It therefore predicts the level based on the capital structure, but it does not predict how the level changes over time. Therefore it does not directly contribute to answering research question A and C and I will not spend any time interpreting its meaning. However, the estimator and its significance are important to report, because it is used as a constitutive variable to create the cross time-term. 5. The cross time-term uses the bond level in 2003 as a reference, i.e. it uses the intercept as a reference point. It then addresses how much the level is expected to deviate from the level in 2003 depending on its capital structure. The cross time-term is constructed as product of its constitutive variables bond-ratio and time dummies. If we expect bondratio and time dummies to be linearly correlated with the dependent variable log(b), then 46
the cross time-term log(b/tl)*d T provides information on whether the linear relationship suggested by the constitutive variables takes a nonlinear detour. That is, it suggests if the bond level in, let us say 2005, changes differently conditional on lag(log(b/tl)) than it would in 2003 (Cohen et al, 1983). If the cross time-term is highly correlated with its constitutive variables, it is misleading to use it at the same time as the constitutive variables. The variable captures what happened over time and how this differed depending on the capital structure. This makes the cross time-term the most important variable in the regression and its estimators will be interpreted in chapter 5. Statistical problems Since the country dummy is included in order to control for multicollinearity, we recognise a certain level of this. In addition, it is also natural to find high collinear relationship between the constitutive variables (lag(log(bt,c/tlt,c)) and D T ) and their interaction term. Excluding some variables does not necessarily solve the problem, but can instead incorporate some of the effect of the omitted variables in the remaining estimators (this is the reason why country dummies were included in the first place). Multicollinearity is not necessarily a problem, with respect to the classical OLS assumptions. However, in the event of high multicollinearity, the estimators can be difficult to measure and their standard deviation tends to be very high. This has four consequences. Firstly, it results in higher confidence intervals (all other things equal resulting in more variables being significant). Secondly, if the variables are highly collinear, one of them tend to be insignificant in spite of the fact that the variable actually does explain the dependent variable. Thirdly, even though the individual explanatory variables can be insignificant, the overall explanation degree, R 2, is usually very high. For the same reason I pay no further attention to the R 2 value in this paper. Fourthly, multicollinearity results in the estimators being very sensitive towards data changes. This point is very clear in Appendix 4.1-4.3 (see definition clarification of the appendix below) (Gujarati, 2003, p. 350). Therefore, we stand in the cross field of two countervailing arguments: One speaking in favour of including all constitutive variables and the interaction term in the same regression in order to avoid potential multicollinearity effects overtaken by another variables (Brambor et al, 2005). The opposite argument points out that including highly collinear variables can result in misleading estimators even though the overall regression is just as reliable. 47
Trials of Regression 1 on bond, equity and bank loans level showed correlation coefficients between the constitutive variables (especially the time dummies) and the interaction terms close to 1. Therefore the risk of wrong estimators caused by multicollinearity is overwhelming. To overcome such problem, one can estimate Regression I in three rounds, all with the level as the dependent variable 10 : (i) Regression I.i: The first results show all variables included in 1 regression. This regression is only used to get a picture of how the estimators would be if they were extracted for the overall regression. None of these estimators are used. (ii) Regression I.ii: The second regression includes all variables except CCTs and CTTs. Coefficients for the time and country dummies as well as the intercept and bond-ratio are extracted from regression 2. (iii)regression I.iii: The third regression includes the variable on capital structure and the cross time-term. The coefficients of CTTs are extracted. Splitting the regression enhances the risk of one variable taking over the effect of another. Looking at the trials of the three partial regressions proves that the interaction term, easily could be removed. The reason is that the additive effect of the constitutive variables provide almost the same effect of level as the interaction terms do (González & Cox, 2007), i.e. β log(b/tl)+ β D(T) β 11 log(b/tl)*d(t). The cross time-terms are so highly correlated with especially the time dummies that they work as an alternative (see appendix 3 on correlation matrixes). To use it as an alternative requires that it provide information that the constitutive variables do not. As explained above it provides very unique information. Thus, if they are significant and highly correlated with the constitutive variables, their marginal contribution to the bond level will not be added directly to the effect the constitutive variables. This paper does not seek to forecast the total value of bonds, but to identify the marginal effect of the cross time-term. Therefore the estimator of cross time-term can be interpreted without placing any attention on the estimators of the constitutive variables. It is the most important variable in Regression 1 and it is mainly used to answer research problem C. 10 Cedric Schneider, professor in Statistics at CBS, suggested this technique 11 The illustration is a bit simplified, as the presence of logged values change the interpretation of marginal contribution coefficients. 48
Special properties of logged variables Both the dependent variable and the explanatory variable on capital structure are logged with regards to bonds and equity, i.e. they are double log regressions. This requires cautious interpretation of marginal effects of all estimators in the regression: The estimator of any logged explanatory variable can be read as followed: The estimators express the percentage changes in the levels when the explanatory variable changes with one percentage. Thus the estimator represents elasticity. For all explanatory variables, logged or not, their marginal effect can also be calculated in the following way: When; β β where ε = error term Then; β The equation above is used extensively in the rest of the paper. Note that each variable affects B with some factor, i.e. percentage, because 10 X works as a multiplier. In regressions where the dependent variable is logged any estimator provides information on the factor with which it affects B. The factor equals a percentage impact of. Any variable rewritten to 10 X is from now on referred to as a contribution factor. Log(B/TL) and log(e/tl) always take negative values, because we log a value < 1. Thus, the higher B/TL, the less negative or the closer to zero is log(b/tl). As lag(log(b/tl)) also affects the value of the CTT observations, its nature is important to bear in mind. Regression II Regression II is used to answers research question B. The interest is now the mix of new external financing and how the mix changes during the crisis as opposed to before. Nettransactions are used to measure new financing and it measures the difference between new capital obtained and liabilities redeemed each quarter (see Appendix 5 for all variables and observations) 12. 12 Net-transactions are extracted at the national statistics of each country. See exact references in Appendix 5 49
Net-transactions are divided into four types: Short-term bonds Long-term bonds Bank loans Equity For a detailed analysis of changes in the mix, 6 regressions are estimated based on ratios between short-term bonds, long-term bonds, bank loans and equity: Table 4.4 Dependent variable ratios of Regression II Long-term bonds Equity Bank-loan Short-term bonds Long-term bonds Equity The table states the ratios of external finance that are used as dependent variables in Regression II. The explanatory variables purely consist of time and country dummies similar to those in Regression 1. Capital structure, as used in Regression I, is excluded, because trials showed highly insignificant coefficients for all regressions (see Appendix 7 for SAS output). Therefore no interaction terms are included. Country dummies are again included to control for country specific characteristics, so be get a clearer picture of changes over time. 50
5 Empirical results This chapter represents core of the paper. It seeks to answer problem statement 2 and its three research questions A, B and C: A. How does the overall level of finance change under the crisis? B. How does the mix of the three kinds of new finance change during the crisis? C. How do the results of A and B differ between bank and market-based economies? All answers are in the form of regression output. The output will be briefly analyzed as it is presented while a full analysis linked to the theory and hypotheses will be conducted in chapter 6 Discussion. 5.1 Research question A The answer to Research question A is divided into 3 sub-chapters; one for bond level, one for equity level and one for bank loan level. For each sub-chapter Regression I (see chapter 4.2.4) is presented and then interpreted according to the significant variables and their estimators. In appendix 4 all 3 sub-regressions conducted for each type of finance are presented. Only the 2 nd and 3 rd sub-regressions are used to extract the final set of estimators. The subscription t indicates a quarterly observation. The subscription T indicates that a quarterly observation is used with the purpose to estimate the level in a specific year. When quarterly observations are used to estimate yearly changes (as they are for the cross timeterms) an average of the four observations in one year is used. Thus, if a capital ratio (bondratio, equity-ratio and bank loan ratio) is subscribed with T it is an average of the quarterly capital ratio in year T. This quotation is used throughout the rest of the paper. Before I present my regression results, it is illustrated how the level changes from 2003 to 2009. The illustrations below serve to ensure a more intuitive interpretation of the results 51
2003Q2 2003Q4 2004Q2 2004Q4 2005Q2 2005Q4 2006Q2 2006Q4 2007Q2 2007Q4 2008Q2 2008Q4 2009Q2 Index 100 2003,Q2 2003Q2 2003Q4 2004Q2 2004Q4 2005Q2 2005Q4 2006Q2 2006Q4 2007Q2 2007Q4 2008Q2 2008Q4 2009Q2 Index 100= 2003,Q2 Graph 5.1 Changes in bond level of NFCs Aggregated bond level for NFCs 230 210 190 170 150 130 110 90 70 50 Norway DK Spain England France Germany US Finland Development in the balance level of bonds: All underlying values are quoted in 2003 prices and $US. The 2 nd quarter of 2003 is the reference year. Claiming a clear trend among countries or countries divided into bank and market-based systems will be putting too much confidence into my own analysis. Not even the financial crisis stands out: For some countries (Germany, France and Finland) a decrease happens during the crisis, but such decline started off already in 2006. For other countries (Denmark and Norway) the increase continues until mid 2008. Hereafter considerable reductions in the level happen. Thus the tendency is mixed. Graph 5.2 Changes in NFCs equity level over time Aggregated equity level for NFCs 400 350 300 250 200 150 100 50 Norway DK Spain England France Germany US Finland Development in the balance level of equity: All underlying values are quoted in 2003 prices and $US. The 2nd quarter of 2003 is the reference year. The interpretation is similar to that of bond level. With equity a much clearer picture stands out. All stocks tend to increase until mid 2007 where after they all fell. The 3 rd quarter for 2007 is known as the time where the first signs of 52
2003Q2 2003Q4 2004Q2 2004Q4 2005Q2 2005Q4 2006Q2 2006Q4 2007Q2 2007Q4 2008Q2 2008Q4 2009Q2 Index 100 2003, Q3 the financial crisis occurred. Denmark and Norway stand out, but it is more a matter of higher volatility, than a different trend. Actually, all countries seem to change the level in the same direction at any given point in time. Similar trends emphasize the systematic nature of the financial crisis. Graph 5.3 Changes in NFCs bank loan level over time Aggregated bank loan level for NFCs 300 250 200 150 100 50 Norway DK Spain England France Germany US Finland Development in the balance level of bank loans: All underlying values are quoted in 2003 prices and $US. The 2nd quarter of 2003 is used as the reference year. The interpretation is similar to that of bond level. Bank-loans follow clearer pattern than bonds, but less than equity. Disregarding Norway, all countries hold their level rather constant and signs of the financial crisis are, if any, small. It actually looks as if minor increases in loans happen in the end of 2008 followed by a weak decrease in 2009. The reasons for what appears as delayed reaction to the financial crisis will be analysed later. 53
5.1.1 Bonds First the regression output is presented. Then the estimators for the time dummies and the cross time-terms are briefly interpreted. Every time the expression bond-ratio is used in chapter 5.1.1 it is equivalent to the variable lag(log(b/tl). 5.1.1.1 Regression output for bonds The two sub-regressions used to extract the estimators have an explanation degree at approximately 99 % (Appendix 4.1). R 2 = emphasises high degree of multi correlation.. This is extremely high and Table 5.1 Regression I output for bond level Explanatory variable Estimator Standard deviation Intercept 5.033 *** (.0913) lag(log(b T/TL) T,C 0.413** (.0797) Time dummies D 2003 - - D 2004.015 (.0159) D 2005.068*** (.0175) D 2006.116*** (.0192) D 2007.143*** (.021) D 2008.154*** (.02) D 2009.175*** (.0179) Country dummies - D Norway - - D Denmark -.169*** (.0238) D Spain -.135* (.0771) D England.566*** (.0268) D France.623*** (.0367) D Germany.428*** (.0248) D US 1.736*** (.0464) D Finland -.484*** (.0221) Cross time-terms CTT 2003 - - CTT 2004.011 (.0125) CTT 2005 -.049*** (.031) CTT 2006 -.081*** (.0142) CTT 2007 -.098*** (.0153) CTT 2008 -.113*** (.0146) CTT 2009 -.141*** (.0134) Source: Appendix 4.1 Table 5.1 provides the outcome, i.e. the estimators for the explanatory variables, of Regression 1 for the logged values of bond level. Thus, bond level is the dependent variable, which is defined as the aggregated value of NFCs bond liabilities as quoted at the balance sheet in a specific country. All values are in 2003 $ prices. The bond level is expected to be explained by: 1. Bond-ratio primo the period of interest, i.e. lag(log(b T,C/TL T,C). In addition the level is expected to vary from year to year, so that the bond level also is explained by exogenous non-stationary macro-economic variables captured by the time dummies (e.g. D2004). An example could be changes in the Central bank s monetary policy. It is expected that the bond level change due to other country specific determinants than just preferred bond-ratio. An example could be GDP or creditor protection. These are exogenous variables captured by the country dummies (e.g. DDenmark). The cross time-term (CTT) captures the part of the yearly changes in bond level that is caused by a non-stationary relationship between the bond-ratio and the bond level. For the time dummies and cross time-terms, year 2003 is used as the reference variable. This means that all estimators represent the change in level compared to 2003. Norway is the country reference variable, and the country dummy estimators reflect how each country s level differs from the level in Norway. No star= insignificant coefficient, * = Significance at a 10 % level. **= significance at a 5 % level and *** = significance at a 1 % level. 54
5.1.1.2. Interpretation of the time and cross time-term estimators The time estimators tell the value of log(b) relative to the level in 2003. The higher the estimator, the higher log(b), but also B. The marginal change in estimators from one year to the next indicates the overall level change during the second year. The marginal yearly change slows down through 2007 (though still positive) and even more in 2008. In 2009 the marginal yearly change increases again. Table 5.2 Time estimators for bond level Time Time estimators Marginal yearly change 2005.068*** 0.068 2006.116*** 0.048 2007.143*** 0.027 2008.154*** 0.011 2009.175*** 0.022 Marginal yearly change = D T - D T-1 Source: Table 5.1 It is not intuitive to show how much log(b) change during the crisis, if you want to say something about bonds. The change in B itself is more informative. In chapter 4, I explained that an estimator calculated on the basis of a logged dependent variable affects the unlogged dependent variable with a factor, i.e. a percentage: Let us for instance look in the table at the year 2006 with a time estimator of 0.116. Measured as factors, the marginal yearly change in e.g. 2006 show how many percent the level in 2006 differs from the level in 2005: The difference = the marginal contribution factor = 10 0.048 = 1.116 or 11.6 %. The corresponding change during 2007 is 10.027 = 1.06 or 6 % and in 2008 it was 1.025 or 2.5 %. However, the level did not fall at any point in time. The cross time-term is probably the most interesting of all variables. It shows that (1) the level has increased every year since 2003 and (2) the increase has been larger for countries with a low bond-ratio. To understand why this is so, the contribution factor is calculated, i.e. the factor impact the cross time-term has on the bond level measured on the original scale: Equation 5.1 The contribution factor In table 5.3 you see that all estimators are negative. There is a good reason for this: B/TL is always less than 1. Thus, lag(log(b/tl)) is always less than zero, i.e. negative. Multiplying them gives a positive value. The higher B/TL, the less negative is lag(log(b/tl)) and therefore the smaller (yet positive) is the CTT T estimator Χ lag(log(b/tl)) and the contribution factor. 55
This is the mathematical explanation for why countries with a low B/TL experienced the higher increase in bond level since 2003. Table 5.3 Cross time-term estimators for bond level Time Cross time estimators Marginal yearly change 2005 -.049*** -0.049 2006 -.081*** -0.033 2007 -.098*** -0.017 2008 -.113*** -0.015 2009 -.141*** -0.028 Marginal yearly change = CTT T - CTT T-1 Source: Table 5.1 The lag(log(b/tl) t) estimator predicts that countries with a high (B/TL)t in general holds a higher bond level. The CTT estimator adds that the bond level in high (B/TL) T countries differs less from the corresponding level in 2003 than the bond level in low (B/TL) T countries does. Since economies with a high bond ratio experience less level change from year to year they are more robust to macroeconomic changes. This is very interesting with respect to the financial crisis. The relationship is discussed in chapter 5.3. However, for all bond-ratios, the contribution factor is positive for all years and therefore B is expected to be higher in the years after 2003. The marginal effect tells how much the CTT estimator changes from one year to next. The CTT contribution factor changes from one year to the next with: Equation 5.2 The marginal contribution factor Marginal contribution factor. The marginal contribution factor tells the % change in bond level over a specific year conditional on lag(log(b/tl) T). Thus the higher (B/TL) T, the smaller the percentage change in level from one year to the next. This conclusion is important to bear in mind as it provides explanation of the results in chapter 5.3. In line with the time contribution factors, the contribution factor for CTT increases over time with a decreasing trend (see marginal yearly change in Table 5.4) and reaches its lowest increase in 2008. To sum up, the time dummies states that the level never falls during the crisis, but its trend of continuous growth slows down. The cross time-term dummy adds that the same relationship exists conditional on the bond-ratio. An interesting point is that the higher the bond-ratio, the less bond levels differ from its 2003 level. The decrease in the level trend, i.e. the in the marginal contribution factor, reached its lowest point in 2008. 56
5.1.2 Equity First the regression output is presented. Then it is briefly interpreted. Every time the expression equity-ratio is used in chapter 5.1.2 it is equivalent to the variable lag(log(e/tl). 5.1.2.1 Regression output for equity level The two sub- regressions used to extract the estimators explain the variation in the equity level with R 2 = (Appendix 4.2). This is extremely high and emphasises a high degree of multi correlation. Table 5.4 Regression I output for equity level Explanatory variable Estimator Standard deviation Intercept 5.955*** (.0655) lag(log(e T/TL) T,C 1.663*** (.1759) Time dummies D 2003 - - D 2004.009 (.0225) D 2005.077*** (.0234) D 2006.138*** (.0259) D 2007.175*** (.0288) D 2008.137*** (.0275) D 2009.207*** (.0239) Table 5.4 provides the outcome, i.e. the estimators for the explanatory variables, of Regression 1 for the logged values of equity level. Thus, equity level is the dependent variable, which is defined as the aggregated value of NFCs equity liabilities as quoted at the balance sheet in a specific country. All values are in 2003 $ prices. The equity level is expected to be explained by: 1. The equity-ratio primo the period of interest, i.e. lag(log(e T,C/TL T,C). In addition the level is expected to vary from year to year, so that the equity level also is explained by exogenous non-stationary macro-economic Country dummies D Norway - - D Denmark -.091*** (.0227) D Spain -.392*** (.0176) D England.349*** (.0216) D France.355*** (.0184) D Germany.525*** (.02) D US 1.289*** (.0365) D Finland -.701*** (.0307) Cross time-terms CTT 2003 - - CTT 2004.032 (.0603) CTT 2005 -.214** (.0677) CTT 2006 -.466*** (.079) CTT 2007 -.633*** (.0874) CTT 2008 -.468*** (.0643) CTT 2009 -.660*** (.0593) Source: Appendix 4.2 variables captured by the time dummies (e.g. D2004). An example could be changes in the Central bank s monetary policy. It is suspected that the equity level change due to other country specific determinants than just preferred equity-ratio. An example could be GDP or creditor protection. These are exogenous variables captured by the country dummies (e.g. DDenmark). The cross time-term (CTT) captures the part of the yearly changes in equity level that is caused by a non-stationary relationship between the equity-ratio and the equity level. For the time dummies and cross time-terms, year 2003 is used as the reference variable. This means that all estimators represent the change in level compared to 2003. Norway is the country reference variable, and the country dummy estimators reflect how each country s level differs from the level in Norway. No star= insignificant coefficient, * = Significance at a 10 % level. **= significance at a 5 % level and *** = significance at a 1 % level. 57
5.1.2.2 Interpretation of the time and cross time-term estimators Table 5.5 The time estimators for equity level Time Time estimators Marginal yearly change 2005.077*** 0.077 2006.138*** 0.061 2007.175*** 0.037 2008.137*** -0.038 2009.207*** 0.070 Marginal yearly change = D T - D T-1 Source: Table 5.4 All time dummies except for 2004 are significant and carry positive estimators. Marginal yearly change is the change in the time estimators from one year to another. Interestingly the picture is the same as for bonds: From 2005-2007 the level increased at a decreasing rate. In 2008 the equity level fell back to the level of 2006 (bond level never decreased). Through 2009 equity level again increased and that to a higher level than before the crisis. Equities are riskier than bonds. This could be the explanation as to why the reaction is more drastic than the bond market during 2007. Through 2007 the level increased with 10.037 = 1.088 = 8.8 % and 2008 time the level fell with 1-10 -0.038 = 8.38 %. As expected, it appears that equity- and bond time factors are highly correlated, and that equity possesses a higher degree of volatility. The reasons for this will be addressed later. The cross time-term, repeated in Table 5.6 show that the level, regardless to equity-ratio, is increasing every year unless in 2008. For the same reason as for bonds, the level increased more for countries with a low (E/TL) T. One can also say, that the more negative the CTT estimator, the larger a gap will we see between the equity level for market-based countries (high (E/TL) T) and bank-based countries (low (E/TL) T). The reason is that the CTT estimator and lag(log(e/tl) is multiplied when calculating the contribution factor. Table 5.6 The cross time-term estimators for equity level Time Cross time-term Marginal yearly effect 2005 -.214** -0.214 2006 -.466*** -0.252 2007 -.633*** -0.167 2008 -.468*** 0.165 2009 -.660*** -0.192 Marginal yearly change = CTT T - CTT T-1 Source: Table 5.4 58
Until 2008, equity level increased as a function of the equity- ratio and so did the difference in level between market- and bank-based economies. In 2008 the level decreased which is also why the marginal effect is positive in 2008. The decrease was more severe for bankbased economies, i.e. countries with a low equity-ratio. As for bonds, the CTT contribution factor for equity is extremely interesting because countries with different equity-ratios all things equal will find their equity levels differing more in 2007 than in 2008. The reason is that the factor is calculated by multiplying the CTT estimator and the E/TL ratio. In practice it means that the countries with low equity- ratios experienced a percentagewise higher increase in level from the glowing economy in 2005-2007. In the same manner they have suffered a larger absolute value decrease in 2008. The marginal effect is used heavily in chapter 5.3. To sum up, the time and cross time-term dummies are suitable supplements of each other and both show significant results. The cross time-term estimators indicate how much the country levels differ from the one in 2003 depending on the period s primo equity-ratio and the period in question. The level increases every year, except for 2008, as a function of the equity-ratio. The level increases less for countries with a high equity-ratio, i.e. market-based economies. These results are useful for comparison with the corresponding estimators for bond and bankloans as they together tell which system has been most exposed to the financial crisis. 59
5.1.3 Bank loans First the regression output is presented. Then it is briefly interpreted. Every time the expression bank loan-ratio is used in chapter 5.1.3 it is equivalent to the variable lag(bl/tl). 5.1.3.1 Regression output for bank loan level For bank loans no logarithmic variables are included as the observations were rather normally distributed in their natural scale. The result is a regression that is much easier to analyse and that seems more intuitive. The explanation degree (R 2 ) is a bit lower for loans. R 2 takes values between 90.59 % and 94.26 %, depending on the sub-regression (Appendix 4.3). Table 5.7 Regression I output for bank loan level Explanatory variable Estimator Standard deviation Intercept -150,478 (140,921) lag(bl/tl) t,c 721,037** (282,670) D 2003 - - D 2004 14,239 (34,062) D 2005 91,596*** (34,953) D 2006 176,471*** (36,531) D 2007 249,129*** (37,701) D 2008 298,143*** (33,947) D 2009 271,789*** (39,558) D Norway - - D Denmark -33,425 (38,867) D Spain 563,532*** (32,084) D England 339,561*** (51,045) D France 292,461*** (52,209) D Germany 768,419*** (33,828) D US 1,133,574*** (117,700) D Finland -145,004*** (70,929) CTT 2003 - - CTT 2004 45,267 (96,325) CTT 2005 259,575** (104,468) CTT 2006 518,814*** (114,779) CTT 2007 695,826*** (122,226) CTT 2008 670,868*** (95,888) CTT 2009 474,232*** (99,950) Source: Appendix 4.3 Table 5.4 provides the outcome, i.e. the estimators for the explanatory variables, of Regression 1 for bank loan level. Thus, bank loan level is the dependent variable, which is defined as the aggregated value of NFCs bank loan liabilities as quoted at the balance sheet in a specific country. All values are in 2003 $ prices. The bank loan level is expected to be explained by: 1. The bank loan -ratio primo the period of interest, i.e. lag(bl/tl) t,c. In addition the level is expected to vary from year to year, so that the bank loan level also is explained by exogenous non-stationary macro-economic variables captured by the time dummies (e.g. D2004). An example could be changes in the Central bank s monetary policy. It is expected that the bank loan level change due to other country specific determinants than just preferred bank loan -ratio. An example could be GDP or creditor protection. These are exogenous variables captured by the country dummies (e.g. DDenmark). The cross time-term (CTT) captures the part of the yearly changes in bank loan level that is caused by a nonstationary relationship between the bank loan -ratio and the bank loan level. For the time dummies and cross time-terms, year 2003 is used as the reference variable. This means that all estimators represent the change in level compared to 2003. Norway is the country reference variable, and the country dummy estimators reflect how each country s level differs from the level in Norway. No star= insignificant coefficient, * = Significance at a 10 % level. **= significance at a 5 % level and *** = significance at a 1 % level. 60
5.1.3.2 Interpretation of the time and cross time-term estimators Table 5.8 The time estimators for bank loan level Time Time estimators Marginal Change 2005 91,596 91,596 2006 176,471 84,875 2007 249,129 72,658 2008 298,143 49,014 2009 271,789-26,354 Marginal yearly change = D T - D T-1 Source: Table 5.7 Interestingly, level changes in BL seem delayed compared to both equity and bond level: The marginal increase for each year slows down through 2007 and 2008, but unlike equity and bond levels, the marginal change (and thus level) is not negative until 2009. It only falls back about one and a half per year, but as we do not have data for 2010 it could decrease further. In this manner, we see a difference between market and bank-based financing, which will be analysed in the discussion chapter. Cross time-term estimator Χ lag(bl/tl) T, i.e. CTT contribution, states how much the level differs from that in 2003 depending on bank loan ratio. Note that it is not a contribution factor, i.e. it does not express a percentage change in level, but an absolute change. Conditional on lag(bl/tl) T the level is higher in every year following 2003, except 2004. Table 5.9 The cross time-term estimators for bank-loan level Time Cross time-term estimators Marginal yearly effect 2005 259,575** 259,575 2006 518,814*** 259,239 2007 695,826*** 177,012 2008 670,868*** -24,958 2009 474,232*** -19,645 Marginal yearly change = CTT T - CTT T-1 Source: Table 5.7 The higher the lag(bl/tl) T, the higher the CTT contribution. Therefore, any change in the CTT estimator affects the CTT contribution more for countries with a higher BL/TL, i.e. based-based countries. This is the exact opposite conclusion than what we saw for bond and equity levels. As for CTT Bonds and CTT Equity the lower the estimator, the more countries with different bank loan - ratios approach each other. 61
This relationship is linear on the original scale, not percentagewise and therefore the finding stands in contrast to those of bonds and equity: The fact that countries with high bank loan ratio have a higher absolute increase in level in a given year, does not mean that they have a higher percentage change in level. However, the percentage change is crucial to estimate, as it enables us to compare bank loan level with equity and bond level. This comparison is done in chapter 5.3. To sum up, the most interesting finding is that the decrease in bank loan level seems to hit all countries more softly than equity and bonds, and not reach its full effect until 2009. In the meantime bank-based countries felt the change more severely and therefore bank-based countries were more exposed to macroeconomic changes. Hereby I do not claim that the percentage impact is larger in bank-based countries, it could be the opposite for reasons explained later. As the crisis hit in 2009, bank- and market-based levels of loans converged; the absolute level fell more for bank- based than for market-based countries. 5.2 Research question B Research question 2 digs a bit deeper and focus on the mix of net-transaction, i.e. the periodical change in stock caused by repayment of old debt/equity and new issuing of debt/equity. Hence net-transaction is the new net-financing for each period cleaned from accounting figure and re-valuation. It indicates the change in finance used for investments. The focus is not of the absolute change in net-transactions, but instead changes in the mix of net-transactions in form of equity, short- and long-term bonds and bank loans. The regression tests this question by holding the 6 possible de-components of capital structure up against both country and time dummies. Dummies are used as explanatory variables and the de-components for capital structure as dependent variables. 5.2.1 Results In appendix 6.1 to 6.6 all the 6 regressions (B-short/B-long, B-short/E, B-short/BL, B-long/B, B-long/BL and BL/E) are presented. In contrast to my expectations, none of them are significant at any level (insignificant p-values, i.e. p-values > 0.1). Therefore none of them are included in the actual paper. To understand why all regressions are statistical insignificant a graph on net-transactions in short-term bond level is shown. Corresponding graphs for the other variables are not presented in the paper, as they tell almost same story (see Appendix 7). 62
Index 100 = 2003, Q1 5.2.2 Why net-transactions provide insignificant coefficients The graph below shows net-transaction of all countries except Norway. Norway stands out of any context and adds no information. Its presence would require a scale going down to - 10.000. Thus it is excluded from the graph. Graph 5.4 Net transactions in short-term bonds 800 Net-transaction in short-term bonds 600 400 200 0-200 -400 France Germany US Spain Denmark Finland England -600 Net-transaction in short-term borrowing equals addition short-term borrowing minus repayments in a given quarter. All underlying values are quoted in $2003, Q1 prices and the same period is used as Index 100. Source: Domestic statistic databases summed in Appendix 5. It is crucial to note the tendency of reinforcement characterising all countries, i.e. after a period of increase in net-transactions a period of decrease occurs. It appears as if the observations for each country fluctuate randomly around a mean with no trend. This is exactly why the OLS model finds no significant estimators there is simply very little system in nettransactions over time. The time dummies typically include 4 quarters of observations. If the switch from increases to decreases in transactions were accompanied with a longer trend, the time dummies would average the four quarter s opposite signs out and focus on their general trend compared to other years. That this is not the case confirms what look like a random walk. 63
Surely, companies do not issue securities or require additional loans in the bank based on pure randomness. On the other hand, decisions on new financing do not have to follow a systematic trend over time. For example, a given company might have a predetermined idea about how projects are refinanced and such an idea is not necessarily something that is changed every year or month. In addition, and probably more importantly, investments are seldom something that is planned to be done in predetermined intervals. Sometimes good investments turn up and sometimes they do not. Good investments occur more for conservative companies in bad times where prices are low. For more risky companies investments can be low in bad time, because they are out of capital, whereas the same companies heavily invested in good times. Therefore, many reasons for holding a certain nettransaction level for all types of finance exist and these reasons are not necessarily systematical over time or for countries in between. 5.3 Research question C When comparing the estimators on the cross time-terms with estimators on equity and bank loans it is possible to see which market that changes most from year to year. Holding the estimators against each other while keeping country specific characteristics constant, highlights which market that was most affected by the crisis and thus which system that lost the most while keeping country specific characteristics constant. Question C asks whether absolute level changes and structural changes in net-transactions differed depending on the type of financial system. The conclusions from research question A and B works as the base arguments for answering research question C. As the results of Regression II were insignificant they are only used to support arguments related to the findings in Regression I. Research question C will therefore only use findings from Regression I. Sub-chapter 5.3.1 elaborates on the properties of the marginal contribution factor, as it is the key to estimate what type of financial system that is mostly affected by the recent crisis and how. Sub-chapter 5.3.2 presents the marginal change in level over the year for each country and each type of external finance conditional of the capital ratios (e.g. the bond-ratio). Sub-chapter 5.3.3 weights the findings in sub-chapter 5.3.2 and estimates the total change in external finance for all countries during the crisis. 64
5.3.1 Mathematical understanding of financial system-differences The cross time-term, must be seen as a variable allowing the impact of the capital-ratio on the capital- level to change in a non-linear way over time. However, within a given year, the cross time-term still provides a linear relationship and this is true for all types of external finance. For equity and bonds, such relationship is percentagewise linear. For bank loans the relationship is linear in absolute values. The total change in level over time conditional on the capital -ratio is calculated by multiplying the capital - ratio with the CTT estimator at a given point in time. This is referred to as the cross time-term s contribution to level. It is calculated for all types of finance. For bonds and equity contribution regards the logged levels. For bank loans contribution regards the level measured in the natural scale. For bonds and equity a contribution factor was calculated, because it enabled us to focus on contribution of the cross time-term to the level measured in the natural scale. The Marginal yearly change was in chapter 5.1 calculated as the difference between the CTT estimator T and the CTT estimator T-1. The marginal contribution factor for bonds = % in the contribution factor, contribution factor,.. And the marginal contribution factor for equity = % in the Equation 5.3 The CCT marginal contribution factor bond level = = Equation 5.4 The CCT marginal contribution factor for equity = = Thus, the marginal contribution factor predicts level changes through year T that is conditional on the capital structure. For example; The Marginal yearly change for bonds in 2007(see chapter 5.1.1.2 on CTT) = -.017 The bond-ratio in 2007 for the US = lag(log (B/TL) 2007,us = -.669 The marginal contribution factor for countries with the same capital- ratio (here bondratio) as the US in 2007 = 10 -.017*-.669 = 1.027 or 2.7 % It means that we expect the bond level to increase with 2.7 % compared to the level in 2006 conditional on lag(log (B/TL) = -.669. Thus, the marginal contribution factor allows us to 65
compare the level in a current year with that of the previous year and assess how the capitalratio affects such yearly change (opposed to the contribution factor which compared with the level in 2003). For bank loans we cannot calculate a marginal contribution factor directly from the regression estimators. It is crucial for this paper s problem statement to be able to compare changes in equity, bond and bank loan level. Such comparison gives the most descriptive results as the change is quoted relatively, i.e. in percentage. This is exactly why the marginal contribution factors provide important information. The factors provide information on the CTT to the predicted level, not the true level. It is possible to estimate a variable that is comparable to the marginal contribution factor using the predicted bank loan level. Therefore we call this variable for marginal contribution factors for bank loan even if it is calculated under other circumstances. This can have impact on the estimates, more about this later. The factor is calculated as follow: The marginal contribution factor for bank loans consists of two components: Component 1: Component 1 simply estimates the $ contribution to the bank loan level in year T dependent on the capital ratio in the current year. As the marginal yearly change is calculates as for bonds and equity, but the outcome is in $, not logged $. Component 2: The predicted levels is extracted from SAS output of Regression 3 in Appendix 4.3. DC = the country dummies and these are subtracted to isolated the effect that the cross time-term has on the predicted level. Thus the predicted level is standardized to the Norwegian level (Norway is the reference dummy), so divergence in the predicted level from country to country is now a function of other variables, such as the structure or time. Equation 5.5 The CCT marginal contribution factor for bank loan level, total The ratio is a simply the marginal contributions factor s percentage impact on the level in year T. It tells the total value that the overall loan level increases over 1 year conditional on the capital structure. In appendix 9 the marginal contribution factors for equity, bonds and bank loans are calculated. 66
5.3.2 Introducing the results ad interpretation of these Below you will see two graphs and one table: (i) Graph on the marginal contribution factor of the cross time-term on B. (ii) Graph on the marginal contribution factor of the cross time-term on E. (iii) Table on marginal contribution factor of the cross time-term on BL. Due to the nature of the values a table is in this case more informative than graphs. In each graph a trend appears for each country. The trick is now, that the only country characteristic included in the graph is the different structures. GDP and other individual characteristics are captured in the original regression.each graph change over time due to two things: 1. the marginal yearly changes of the CTT estimator changes and 2. the capital- ratio changes. The latter only changes very little, i.e. the countries kept their ranking relative stabile over the periods of interest (see appendix 10) and therefore we can focus on the marginal yearly change. Below the marginal contribution factors for bonds, equity and bank loans are presented. The three sub-chapters do not follow the exact same structure, as some of the point made on bonds is the same for equity and thus not worth repeating. Since the marginal contribution factor for bank loans is calculated in a different why that for bonds and equity, it must be addressed in a different why. 5.3.2.1 Bonds The following sub-chapter first present the marginal contribution factors for each country form 2005-2009. It then seeks to explain the changes over time from a mathematical point of view. These explanations can appear heavy and irrelevant, but approaching them systematically ensures that interpretation of the underlying economic reasons for the changes in level is causally valid and that we do not miss a point. Bank-based countries are the ones with the highest marginal contribution factor regardless of the year in question. When (B/TL) T decreases, so does lag(log(b/tl)) T, i.e. becomes more negative. The more negative log(b/tl) T, the higher the marginal contribution factor. The main question is then why the CTT estimators are negative and countries with low bond-ratios have the highest marginal contribution factors. 67
Factor change in bond level conditional of the capital structure Graph 5.5 Marginal contribution factor for bond level Marginal contribution factor for bond level 1,30 1,25 Norway 1,20 Denmark 1,15 Spain 1,10 England 1,05 France Germany 1,00 US 0,95 Finland 2005 2006 2007 2008 2009 The graph demonstrates the marginal contribution factor, i.e. the change in level over the recent year conditional on the bond-ratio, lag(log(b/tl). The cross time-term of each year is multiplied with the bond-ratio in each country in the same year. The bond-ratio, lag(log(b/tl) T, C = average the capital structure over the year. Explanation of the factors and why they approach each other in bad times For all countries at every point in time the marginal contribution factor stayed over 1 (France being an exception in year 2009). Thus, the cross time-term contribution factor grew in every period for every country and so did the equity level. However, the speed by which the contribution factor grew decreased throughout the whole period until end 2008, i.e. the marginal contribution factor approached 1. Parallel the marginal contribution factors for each country approach each other, because they are a product of the marginal yearly change T ( CTT estimator T) and bond-ratio (lag(log(b/tl) T). When these two variables are multiplied the marginal contribution factor will be largest for countries with low (lag(log(b/tl) T) as their value is more negative. The difference between the factor of two countries with different bond-ratios, depends on the value of the marginal yearly change (the more negative the larger the difference). The fact that we use a new cross time-term estimator each year allow the regression to tell if the claimed relationship between low (B/TL) T and large % changes in level since 2003 did not exist in a specific year. The marginal contribution factors show that it is the bank-based countries that experienced the largest changes in the trend in level over the years. That is, they are the countries with the highest increase in level (or lowest decrease in 2008), but at the same time the marginal contribution factor changes with most percentage for these countries, i.e. the change in level 68
(one can call it the Return) is more volatile for bank-based. Remembering that the return distribution of portfolios is more or less normal distributed and the higher volatility the higher the expected return. It appears as if the bond level in bank-based countries is simply more volatile (in contrast to our expectations). To support this argument it would be natural to look at the graphs in chapter 5.1. However, these graphs are not controlled for country specific characteristics and thus it is misleading to conclude anything from them. Instead, we can look at the standard deviation in the marginal contribution factor and it confirms that the bankbased countries (and France) were most exposed, see table 5.10. The underlying reasoning for why such tendencies occur is reflected upon in the discussion. Table 5.10 Volatility in the marginal contribution factor for bond level Country Standard deviation in the marginal contribution factor US 0,022 England 0,032 Finland 0,033 Norway 0,040 France 0,041 Denmark 0,042 Germany 0,049 Spain 0,080 Source: Appendix 8 Mathematical explanation of the relationship between the bond level and cross time-term Appendix 10.1 shows that the % in B from 2003 to 2009 is negatively correlated with (B/TL) 2009. The same pattern is obvious when comparing % in B 2003-T with any other year T. Thus, the higher % in level compared with the 2003 level, the smaller (B/TL) T ratio in the present years. It means that countries like the US, whose NFCs heavily use bonds, probably experienced less increase in the level through the years than countries with a very low dependency of bonds. Thus, the correlation coefficients support the cross time-term estimators. One reason for this tendency could be that countries with low (B/TL) T experienced a larger increase in the (B/TL) T, i.e. a change in capital structure in favour of bond financing, and therefore they also experienced a higher increase in the level. However, appendix 10.1 demonstrates that a positive correlation between % (B/TL) 2003-T and the (B/TL) T - ratio. Hence, the countries mostly depended on bonds, experienced the higher increase. This stands in contrast to the cross time-term estimators. Thus, we can exclude the possibility that the 69
bond level increased more in low (B/TL) T ratio countries, because of a change in preferences toward bond financing. Instead, countries with a low bond - ratio can also have experienced a larger increase in the bond level over time, because the whole economies have expanded more. This would require not only the bond level, but also the equity and bank loan level to increase more in these countries. Estimates of the correlation between (B/TL) T and % E 2003-T is negative, and so it the correlation between (B/TL) T and % BL2 003-T (Appendix 10.2 and 10.3). Thus, the lower (B/TL) T, the more did the bond, equity and bank loan level increase percentagewise and the more percentage did the total level of external finance increase. Therefore, bank-based countries apparently went through a larger increase in all types of finance from 2003 to 2009 and therefore the bond level increased more in these countries, even though the bond-ratio changed less. It must be added that correlation figures not necessarily are significant. However, they still prove the point that bank-based countries not only experienced a more positive change in the bond level due to an overall larger increase in liabilities of the NFCs, but that they did so even though the market-based countries parallelly experienced a higher increase in (B/TL) T. One could argue that the reason why market-based countries experienced a larger increase in the bond-ratio is simply because they did not go through as large an increase in equity and bank loan level as bank-based countries did. One very important property of correlations must be mentioned: Negative correlations merely state that there is a tendency of one variable increasing as the other decreases. This is not contradicting to a general decrease in both variables. Thus; the level could in theory decrease for all countries through times, but we will then see that the decrease is smaller in bank-oriented countries. 5.3.2.2 Equity The marginal contribution factors are to be read in the same manner as bond level. The yearly change in equity level follows a slightly different order than that of bonds, but interestingly three out of the four least solvent countries (measured for NFC s), are also the ones with the lowest bond ratios, i.e. they seem to prefer neither bonds nor equity especially high. This is Norway, Spain and Germany. 70
Graph 6.6 Marginal contribution factor for equity level 1,45 1,35 1,25 1,15 1,05 0,95 0,85 Equity Marginal contribution factor 2005 2006 2007 2008 2009 Norway Denmark Spain England France Germany US Finland The graph demonstrates the marginal contribution factor, i.e. the change in level over the recent year conditional on the equity-ratio, lag(log(e/tl). The cross time-term of each year is multiplied with the equity-ratio in each country in the same year. The equity-ratio, lag(log(e/tl) T, C = average the capital structure over the year. The reader might be surprised over the moderate decrease in level in 2008, as everybody knows that market value of equity dropped severely in 2008-2009. It is important to remember that the marginal contribution factors only focus on changes in equity level that can be explained by the financial structure in the countries used in the paper. The factor emphasises that the countries percentagewise mostly affected by marginal yearly level changes, are the least equity-based ones. The reason is exactly the same as for bonds. In appendix 10.2 the correlation between (E/TL) 2009 and % E 2003-2009 is negative and the same pattern is visible for all other years. There exists a negative correlation between the % B 2003-2007 and % BL 2003-2007 and the (E/TL) T- ratio in the same years. The same tendency can be found in 2008 and 2009. Therefore a certain level of evidence support that the bank-based countries with low (E/TL) T experienced a more positive change in total finance. However, the correlation coefficients supporting such evidence are weak and no more attention will therefore be put into these findings. The volatility in the marginal yearly change is larger for equity than for bonds and so it turns negative in 2008. Comparing Table 5.10 and 5.11 provide evidence of volatility higher related to equity than to bonds. In addition Table 5.11 shows that 3 out of the four most volatile countries are bank-based. 71
Table 5.11 Volatility in the marginal contribution factor for equity level Country Standard deviation in the marginal contribution factor US 0.058 Finland 0.079 Denmark 0.093 England 0.101 Germany 0.102 Spain 0.126 Norway 0.128 France 0.190 Source: Appendix 8 For the same reason the marginal contribution factors approach each other more than for bonds. Note that Graph 5.6 shows that the countries shifts order, so the bank-based countries in 2008 were the ones experiencing the largest drop (the contribution factor is less than 1) in level. The mathematical reason is that the marginal yearly effect turns positive, but lag(log(e/tl) T) still is most negative for the countries with a low (E/TL) T. Multiplying the two variables resulted in lower marginal contribution factor for bank-based economies. In the same manner the bank-based countries experience that largest increase in equity level through 2009 and as for bonds they turn out to be the countries with the most volatile level due to their structural characteristics. Interpretation will follow in the discussion. 5.3.2.3 Bank loans The marginal contribution factors calculated with regards to bank loans are so close to each other that a graph presenting the results would be hard to read. Instead the marginal contribution factors are presented themselves: Table 5.12 Marginal contribution factors on bank loan level for each country Country 2005 2006 2007 2008 2009 Germany 1,407 1,297 1,173 0,976 0,978 France 1,419 1,309 1,179 0,977 1,019 Denmark 1,422 1,320 1,190 0,976 0,979 Finland 1,428 1,334 1,192 0,977 0,981 Norway 1,431 1,313 1,182 0,976 0,978 England 1,439 1,306 1,172 0,976 0,978 Spain 1,485 1,324 1,179 0,976 0,978 US 1,529 1,351 1,190 0,976 0,979 Standard deviation /average factor 2,83% 1,29% 0,64% 0,05% 1,43% Source: Appendix 4.3 for the CTT estimators and Appendix 8 for calculation of the marginal contribution factors The table present the marginal contribution factor calculated as Equation 5.6. The percentage standard deviation from the mean is calculated, as it highlights how close the values follow each other. Equation 5.5:. 72
Explanation of why the country exposure deviates as little as it does Table 5.12 emphasis a crucial point: The % in the bank loan level from the previous period for each country varied with only 2.83 % to 0.05%. The ranking follows no system with regards to the capital structure. This might appear contradicting to the fact that cross timeterms exactly proved that a significant relationship between level change over time and (BL/TL) existed. Below I try to explain why the variation was so small between the different financial systems. As the marginal yearly effect is the same for all countries in a given year, the lack of variance is mathematically explained by a constant relationship between the bank loan-ratio and the predicted level minus the country specific impact (See Equation 5.4-5.6), i.e.; Constant =. Such constant is quite intuitively, as we have a constitutive variable, lag(bl/tl) T, which on one hand claim a constant linear relationship with the bank loan level if controlling for country specific impact and time. It that sense, the marginal contribution factor simply supports the constitutive variable in contrast the factors for bond and equity level. However, with regards to the contribution factor of bonds, the cross time-terms predicted the relationship between lag(b/tl) T and B to be less positive the higher lag(b/tl) T. The same was true for equity. This is not the case for the cross time-term linked to bank loans: If the level is predicted to increase in 2005 relative to 2003 and the relationship between BL and lag(bl/tl) T is truly constant over time, the absolute level must increase more for high lag(bl/tl) T countries. Only so will the relationship stay constant. This is exactly what the cross time-term estimators for bank loans predict. The marginal contribution factor for bank-loans shows the interesting point; it provides no new information in addition to its constitutive variables, i.e. time dummies and bank loan-ratio as long as they both are used to predict the level at the same time. But, the constant spells out how much more the level must increase more for high lag(bl/tl) T countries than low lag(bl/tl) T to keep the relationship constant. A very important point support the explanation above: Correlation estimates between lag(bl/tl) T and % BL from 2003 to T shows no convincing signs, but for what it is worth these signs are positive (see appendix 10.2). Therefore, there is no convincing evidence for why countries with certain capital structure characteristics should change their level 73
percentagewise more than others and thus there is no reason why the time dummies and the bank loan-ratio variables should change relationship when combined. If we should read anything into the positive signs of the correlation, it merely supports the constitutive variables and perhaps shows weak signs of a synergy between terms 13. The graph on bank loan level in chapter 5.1 supports the correlation estimates: No distinct system appears. The general trend in the marginal contribution factor It is important to highlight that changes over time did happen, but the change was almost the same for all countries and their exposure to level changes was rather constant. Compared with the % in level for equity and bonds, bank loan level increases significantly more in 2005 and 2006. Actually the level increases with around 40 % in 2005 and 30 % in 2006 (see Table 5.12). It does not decrease as much as equity in 2008, but on the other hand it continues to decrease in 2009. Does it mean that bank loans are less volatile over time? Table 5.13 provides the answers. First, there are no convincing signs of what system that experienced the highest volatility in the contribution factor for bank loans from 2005 to 2009. Table 5.13 Volatility in the marginal contribution factor for bank loan level Country Standard deviation in the marginal contribution factor France 0,188 Germany 0,192 Denmark 0,200 Norway 0,202 England 0,203 Finland 0,204 Spain 0,221 US 0,240 Source: Appendix 8 However, comparing Table 5.10 to Table 5.11 and Table 5.13, the marginal contribution factor shows much stronger volatility for bank loans in general than for equity and bonds. This is unexpected and will be discussed later. However, the result is that countries for whom the lag(bl/tl) T is high, will all things equal feel a higher volatility in the total level. Bank loans overall exposure to the crisis, relative to that of bond and equity, plays a leading in whether the total level of external finance in bank-based or market-based countries is most exposed. The answer to which system that was overall most exposed to the crisis is provided below. 13 A synergetic interaction term is one where its two reference variables, the constitutive variables, both provide marginal impact on the dependent variable, but where there exist a positive spillover effect. An example could be that a child, who is bright, receives better grades and a child, who is working hard, receives better grades. But a child that possess both skills receive better grades than simply adding the effect of intelligence and work effort. 74
5.3.4 Change on total external finance The total % in external finance in year T is calculated in the following way: Equation 5.6 Yearly percentage change in external finance Equation 5.7 calculates percentage change in total external finance through year T for country C. It is calculated as the weighted average of marginal yearly change in bond, equity and bank loan level conditional on the capital structure. The marginal contribution factors already take a specific value for each type of finance, in each country through 2005 to 2009. See Appendix 8 for the marginal contribution factors and Appendix 2 for the quarterly capital structure (Equation 5.5 use the average capital ratio for the four quarter in year T). The results are presented in the table below: Table 5.14 Percentage change in total external finance Country 2005 2006 2007 2008 2009 Standard deviation Ranking 2007 2008 2009 1 = most positive US 9,98% 8,79% 5,27% -3,31% 6,65% 5,24% 8 1 6 Finland 16,46% 13,65% 7,91% -4,40% 7,33% 8,02% 7 2 3 Denmark 23,20% 18,57% 10,70% -5,37% 6,72% 11,10% 6 4 5 France 22,42% 18,75% 10,92% -5,31% 22,27% 11,66% 5 3 1 England 22,10% 18,87% 11,26% -5,41% 7,37% 10,81% 4 5 2 Germany 25,11% 20,98% 12,18% -5,71% 6,60% 12,20% 3 6 8 Norway 28,16% 23,08% 13,47% -6,07% 7,22% 13,50% 2 7 4 Spain 29,58% 23,92% 14,29% -6,46% 6,65% 14,26% 1 8 7 Source: Appendix 2 and Appendix 8 Presentation of the yearly changes in total external finance calculated as demonstrated above. The last three columns present the country mostly affected during the crisis at its different stages. The last row tells how much the countries deviated in their yearly changes from the average change in year T. Especially 2008 and 2009 introduce higher volatility between the countries. The table above reveal important information needed to answers research question C, as it ranks countries exposure to macroeconomic changes based on their capital structure. If we pay no significant attention to level changes as a function of technical accounting changes, the changes must be either value or transaction wise. Regression II showed lack of significant changes in mix of new finance. The underlying time series of all types of net-transaction revealed that the lack of significance could be explained by the fact that no type of finance 75
changes significantly over the years. Thus, it is valid to claim all significant changes in Regression I, i.e. level, to be caused by revaluation. If so, changes in level approach the measure return seen from the investor s point of view. From the NFC s point of view revaluation first of all have impact on credit risk and the company s ability to collect more finance. In short, the countries experiencing the largest decrease in level in 2008 were all bank-based and they do not rebound in 2009 to the same extent as the market-based countries. Changes in total external finance through time The ranking in 2007 and 2008 shows a certain consistency in which countries that enjoyed the good times the most are the countries affected mostly by the crisis. However, using a broader time frame (back to 2005), the column Standard deviation show no consistency between volatility in return/revaluation and the financial system characteristic. In the meantime, a pattern in volatility does exist, but as all economies hold three types of finance, the total volatility is a product of: 1. The weights of each type of finance 2. The standard deviation in the marginal contribution factor of each type of finance 3. The correlation between the marginal contribution factors of each type of finance Especially the correlation between each type of finance makes direct interpretation as to why one country is more volatile than another, hard. The marginal contribution factors do also differ from country to country and therefore it is not only the weights and correlation that explain why one system is more affected than another. Looking at the marginal contribution factors individually, as we did above, is therefore much more revealing. This paper is mostly interested in the financial shock and its impact of NFC financing in the period of 2007 to 2009. This period confirms that bank-based countries were more exposed to macroeconomic slowdown in 2008 and that their volatility from 2005 to 2009 was also the highest. Risk and return Interestingly one could already see clear signs of economic slowdown in 2006 for all countries: With no exception, the trend in level, i.e. the marginal change, decreased every year from 2006 to 2008. Only in 2008 the marginal change was negative and so the level decreased. In 2009 the trend in level increased, turned positive, and thus the level increased again. In this sense they all follow the same path. 76
The marginal contribution factors for all types of finance approach each other from 2005 to 2008. In the same manner the % in total finance also approach each other until 2008. For this to happen, the countries experiencing the highest % in total finance in 2005 must have experienced the largest change % point drop in the total change. E.g. Norway s total level increased by 23.08 % in 2006 and US s 8.79 %. In 2007 Norway s level increased by 13.47 % and US s 5.27 %. Norway s change in level, i.e. trend, from 2006 to 2007 dropped with 9.61 % point or 41.6 %. US s trend in level dropped with 3.52 % point or 40.02%. Therefore, one could argue that the trend, i.e. change in level, changes with more or less the same for the two countries. However, investors and the NFCs both strive for growth and usually a constant growth. Thus if we put the growth (the change in level) in focus and compare the overall return if the crisis had not appeared with the trend now that it did appear, we see how Norway missed out on bigger return than the US: Table 5.15 Example of unrealized return Country % change in TL 2006 % change in TL from 2006 to 2008 (Assuming the same trend in 2007 as in 2006) Actual % change in TL 2007 Actual % change in TL from primo 2006 to ultimo 2007 % point difference between expected return and actual return Norway 23.08 % 23.08 % * 1.2308 = 28.41 % 13.47 % 23.08% * (1-.1347) = 26.18 % 28.41 % - 26.18 % = 2.22 % point unrealized return US 8.79 % 8.79 % * 1.0879 = 9.56 % 5.27 % 8.79 % * (1-.0527) = 9.25 % 9.56 % 9.25 % = -0.31 % point unrealized return Source: Table 5.14 The table provides an example of how much the total level in Norway and the US increased less, than if the return in 2008 had stayed on the same level as in 2007. Of course, that would not have been the case even if the crisis had not become a reality, but it illustrates why countries with the highest return primo the crisis, also missed out on most profit during the crisis. The point can be generalized in the sense that there is a relationship between high profit and high risk of return and this data simply confirm such relation. 77
Statistical caution Norway missed out on 2.22 % point and US missed out on -0.31 % point level increase. Still, it is crucial to emphasise that these calculations only use the coefficients provided in Regression I and they seek to explain level changes in a specific year as a linear function of the capitalratio. Table 5.16 provides simple standard deviation from the mean value- figures for the bond level, equity level, and bank loan level from 2003 until 2009. First, there is no distinct pattern in which type of finance that deviates most from its average level. Thus, for Denmark equity clearly deviated more, but for England bank loan deviated more. Secondly, this stands in contrast to the standard deviation in the marginal contribution factors, which do not take the country specific changes into account. The country dummies serve the role of capturing such difference in level changes and therefore volatility in the marginal contribution factors are not affected and neither is the volatility of total external finance. This is crucial to emphasise. Table 5.16 and the volatility in Graph 5.1, 5.2 and 5.3 in chapter 5.1highligth the total change in level for each country was affected by more and other factors than the capital structure. Table 5.16 Percentage standard deviation from the average level of external finance Bonds Equity Bank loans Norway 27% 39% 37% DK 14% 41% 31% Spain 31% 22% 26% England 20% 14% 24% France 9% 22% 12% Germany 9% 13% 6% US 17% 21% 28% Finland 10% 25% 20% Measure: First the standard deviation is calculated from 2003-2009. Then the average in the same period is calculated. Values in Table 5.16 = Standard deviation / average level. Source: Appendix 2, real 2003$ values of bond, equity and bank loan level. Explanation of the change in total external finance through 2009 That the bank-based countries in general were the most exposed ones during the crisis is, as already mentioned as a sum of weight, marginal contribution factors and correlation among these. It is extremely difficult to give a precise answer to why we see this ranking and why the exact % change in level for each country at each point in time turns out as it does. Every single value holds a different underlying story and this story change in any point it time. It is the correlation between the three types of finance that complicates the picture. A thorough 78
analysis will itself require a whole paper, so I will limit the analysis to 2009 to give the reader an idea about the complexity of the % change in total external finance. The countries that experienced the largest drop in 2008 were not the countries that had the largest increase in 2009. Thus, we cannot claim that we are witnesses to a normal distribution of risk and return based on such a short period of time. The general reason for why the market-based countries experienced the largest increase in 2009 goes as follow: The NFCs in market-based countries are according to the marginal contribution factors for 2009 on equity expected to increase their equity level less than bank-based countries. As E/TL on the other hand weights more for the market-based countries equity s impact on changes in the total level is still ambiguous. The exact same tendency is true for bonds, however the increase is smaller and so is its share out of total external finance for all countries. The bank loan level decreased in 2009, but very little (1-2 % decrease according to Table 5.12). The decrease in level reduced the change in total external finance for all countries, and the reduction should weigh more for bank-based countries as they hold a larger bank loan - ratio. Thus, the total explanation for why market-based countries experienced a larger increase in value in 2009 is first of all a consequence of bank-based countries experiencing a heavier weighted decrease in bank-loans. According to the marginal contribution factors we would see a higher volatility in the total level for bank-based countries throughout the whole period. Thus, if it was only the marginal contribution factors that determined the percentage change in total external finance we should have seen a larger increase for bank-based countries in 2009. That this is not the case is simply due to the difference in the weight of bonds, equity and bank loans. During the crisis market-based countries simply outperformed the bank-based countries due to the mix of external finance and not because the single type of finance increased more in 2009 than the corresponding financial sources in bank-based countries. 79
6 Discussion This chapter analyzes and discusses the relationship between the results (chapter 5) on one side and the seven hypotheses (chapter 3) and theoretical content (chapter 2.1 and 2.2) on the other. Macroeconomic data is introduced to support causality between the empirical findings and the hypotheses. Many hypotheses treat both research question A, B and C. In order to make valid conclusions causality must be proved between the results and the reactions (and reasons for such reactions) to the crisis as postulated in the hypotheses. Such proofs require their own examination to an extent that goes beyond this paper, if they are to be valid. Thus, any causality presented now are merely indicators of what to expect if we decided to conduct new and more thorough calculations on each hypothesis. However, the results presented in chapter 5 are indeed valid. Still holding these results against new macroeconomic data merely indicate correlation and causality. All macro-economic data is quoted in current prices. Most macro-economic data quoted in current prices focuses on the balance sheet of banks; their allocation of assets and credit tightening. Banks, like everyone else, address the return on assets and credit tightening in the currency and price used at the day where the go to work and this is the quotation they react to. They to not say: Measured in 2003 $ prices the credit tightening is actually not so bad. I believe banks to react of what happened today, measured in current prices and it is their reaction to the crisis that is interesting. For balance sheet information England is not included. The reason is that England does not use the same balance sheet accounts and thus the data is incomparable. However, as the discussion chapter do not claim to be perfectly statistical valid, but merely to show indications, it is acceptable that England is not included. However, it would have been preferred. Hypothesis I Effect 1, 2 and 3 argue for and against banks enhanced capital access. Summarizing effect 1 and 2, I would argue that depositors lack of confidence in banks is larger than their lack of confidence in the secondary market, thus the combined effect is in favour of bank-loans. Effect 3 on interbank lending on the other hand is expected to have significant impact on total liquidity and liquidity risk. In order to enhance liquidity and thus reduce the liquidity risk, all types of assets are expected to be reduced. This will mostly affect bank loans, unless security holders others than banks reduce their holding of securities with the same degree as banks do. 80
Bank loans are the least liquid type of financial assets, so they are reduced more slowly than securities. The total effect of reduced interbank lending is expected to countervail higher consumer deposits. The reduction in the NFC s securities are at first more severe, but reduction in bank loans will continue over a longer time. Discussion of hypothesis I Before looking into how the total level and allocation of bank assets changed during the crisis, the first step is to prove whether or not customer deposits and interbank deposits changed. If none of these changed, there is no point in looking further into the changes in bank assets. The focus is on the period of 2007-2008, because the interest is on the first period of the crisis. Changes in bank deposits Appendix 11.1 shows the total level of customer deposits and interbank deposits. For consumer deposits, the level increased through 2008, but only the same or less than it did in 2007. Contrary to expectations, there is no evidence indicating a higher increase in preference for deposits than in the previous periods. For interbank deposits only the level in Norway and Finland increased through 2008. For all other countries the interbank deposit stagnated or decreased and is therefore more affected by the crisis than consumer deposits. Exactly as expected. The point is even clearer when compared to an otherwise constant increased in interbank deposits since 2005 until 2008. Combining the effect on consumer deposits and interbank deposits, we therefore see that the banks access to short-term capital increased less than it did in the previous years. We do only see an actual decrease for Denmark and maybe Germany. Thus, to the extent that a decrease in the trend has any impact on the asset level and allocation of banks, the impact is expected to be moderate. Changes in the level of bank assets Banks investment in securities, i.e. equity and bonds, follows a rather unstable increase each year until 2008. In 2008 a more systematic slowdown in a still positive trend appeared. For France and Germany value of securities even dropped (Appendix 11.1) 14. The level for bank loan assets stands out: It increased with an increasing trend for all countries through 2004 and 2005. In 2006 and 2007 the increase in level stagnated (the level increased with a constant trend) and in 2008 the increase in level slowed down to around 7% to -3 % (depending on the country). Thus, the slowdown (measured by the change in level, not the total level) began in 14 OECD does not provide bank asset data for 2009 yet. 81
2006, i.e. before any change in deposits. The change in bank loan assets approached each other for all countries, i.e. the change was more systematic than before. Thus the reduction in level trend from 2007 to 2008 was largest for the countries enjoying the highest increase in 2007. These countries are Denmark, Spain and Norway. Causality between changes in deposits and changes in external finance for NFCs So, we see a positive correlation between the combined effect of changes in the customer and interbank deposits and changes in security and bank loan levels. Since the signs of slowdown in bank loans happened already in 2006, other factors must have played a role than changes in deposits. This is not a claim, that a decrease in the trend of deposits does not impact banks investment in bank loans. However, the impact is not convincing. If we accept that banks reduced their holding (or slowed down their increase in holdings) of bank loans, it can happen through (i) reducing in new lending and (ii) redemption of loans. Both actions should be directly and fully observable on the NFCs net-transactions. Two exceptions exist: If the reduction of loans affects foreign debtors more than domestic debtors and if the reduction affects other debtors than NFCs, e.g. private debtors. We know that net-transactions of NFCs did not change significantly during the crisis and thus we can reject that banks changed their lending policy to NFCs - no matter the trend in deposits. Change in the trend of bank loans held by banks must then have been explained by either reductions of loans to foreigners, privates or revaluation. None of these are of any relevance to hypotheses I. Reduction of banks investment level in securities can happen by net-selling or revaluation. Net-selling is not necessarily observable on the NFCs net-transactions in securities, because securities can be sold on the secondary market. Only trading where the NFCs are directly involved is registered as net-transactions. Revaluation of existing securities puts banks in a passive position where they receive the market price but keep their quantity constant. With the data at hand it is not possible to tell what effect that caused a slowdown in security holdings. If it was the market value that changed, we would be able to see the effect on NFCs level of bonds and equity. However, since net-selling of securities held by banks all things equal also reduces the market price, it is not possible to separate the effect of value reductions due to other market players and value reductions due to active net-selling by banks. Thus I cannot conclude if NFCs partly experienced a reduction in their securities due to an active policy by the banks to increase their liquidity. 82
Further research In reality, the reaction of banks to the crisis probably happens in parallel to even the smallest signs of potential recessions. These reactions are exactly what stimulate further value reductions in the financial market. To assess whether lower increases or stagnation in deposits motivated banks to sell securities and whether this affected the value of NFCs securities would require monthly, preferable daily, observations where lagged values of the % in deposits are used as explanatory variables and held against the % in security values as stated on the asset side of banks balance sheets. The changes in security values would furthermore need to be separated into changes due to net-transactions and changes due to revaluation. The task is beyond the scope of this paper. Hypothesis II Effect 4 states that government bonds are expected to serve as an appealing alternative to deposit savings thus lowering the risk free interest rate premium, the effective bond rate and equity returns. The impact on equity is expected to be limited, as the interest rate premium accounts for a small fraction of the total cost of capital. Effect 12 adds the effect of expansionary monetary policy. This results in a higher demand of bonds which lowers the interest rate premium even more. The total effect is expected to be significant simply because the agenda of central banks was to stimulate investments and consumption. However, the effect is expected to be greatest for bond levels, then bank loans levels and the least affected capital source is expected to be equity. The effect is not expected until the central banks assess that the risk of real economic slowdown is present. Discussion of hypothesis II In order to address hypothesis II correctly, it first must be concluded if consumers changed their preferences for saving in banks during 2008 and 2009. If consumers changed preferences towards government bonds it should be reflected in the yield of these. The yield is measured as short-term interest rate on T-bills. Thus, this is the second point of interest. Then, potential discrepancy between movements in the yield of government bonds and saving deposits is discussed. In the end it is discussed what the consequences of changes in the monetary policy are for external finance of NFCs. Consumer deposits Graph 6.1 shows that the trend decreased to around 5 % during 2008 and almost stagnated through 2009. Not since 2003-2004 had the increase in deposits been so low. Thus customers 83
% Yield per annum % in customer deposits slowed down their increase in bank saving for one reason or another, but the deposits still increased with 7 %- 15 % for all other countries than Germany and Denmark. Graph 6.1 Yearly change in consumer deposits 27% 22% 17% 12% 7% 2% -3% % in consumer deposits Denm ark Finlan d France Germa ny Norwa y Spain Graph 6.1 use aggregated balance sheet information for all types of banks. Deposits are stated as a liability as quoted in current prices in millions of national currency. % Changes in deposits = (Dept-Dept.1)/Dept-1. More observations would have been preferred. Source: OECD financial statistics, also available in Appendix 12. Change in the short-term interest rate The short-term interest rate is derived from interbank lending rates and rates on Treasury bills. Banks lend via the central bank and the central bank issues bonds or bills to the public. The central bank charges a very little margin on top of its own interest expense and therefore these two rates are used indiscriminately. T-bills are, due to their low risk profile and short time to maturity good alternatives to storing money in saving accounts. Graph 6.2 Short-term interest rate on government bonds 8 7 6 5 4 3 2 1 0 Short-term interest rates Finland France United Kingdom United States Denmark Spain Germany Graph 6.2 shows the yield on short-term interest rates, quoted monthly. It is derived from a mix of 3-month interbank rates or T-bills. Source: OECD Financial Indicators (MEI). 84
Graph 6.2 above shows how short- term interest rate decreased severely and sharply in the end of 2008 for all countries and stayed low all through 2009. Causality between consumer preference for deposits and the short-term interest rate The trend in deposits and short-term interest rates are to some extent correlated: A drop in interest rate happens in parallel to a decrease in the trend of deposits just like the hypothesis claimed. However, the graph shows that correlation is not clear. For example, the trend in consumer deposits in 2006 dropped while interest rates increased. Customers are far from the only T-bill investors. Central banks are huge investors in own liabilities, i.e. they buy back their own debt, in order to decrease the interest rate. In that case the yield (interest rate) is a function of Central banks incentive to stimulate the real economy. As the drop in short-term interest rates happened so fast, severely and systematically it seems most likely that it is caused by monetary policy. The countries in the sample did not decrease their interest rate until the end of 2008. Even though we only have yearly data for customer deposits, the change in the trend through 2008 was so significant that it probably began early in the year. As this was before the interest rate fell dramatically, but at the same time as the rate stagnated, we can interpret this as a weak sign of savings being invested in T-bills. Other explanations for why consumer deposits decreased I would argue that a more likely reason for the decreasing trend in deposits is lower growth in the real economy: Growth in real GDP turned negative for all countries from mid 2008 to the beginning of 2009 (Appendix 11.2). Customer deposits continued to decrease through 2009 even though the yield on bonds became less attractive. Therefore it is more likely that changes in deposits are caused by low growth rates than withdrawal due to better investment options. The impact of lower risk free interest rate on the external finance of NFCs Above it is argued that expansionary monetary policy is conducted in all countries with the purpose to decrease interest rates. Lower yield raise the price on NFC bonds. In chapter 5 it was shown that the time dummy for bonds in 2009 increased. As the net-transactions showed no significant change in 2009 relative to 2008, the value increased due to a lower yield and this is indisputably related to a roughly 70 % decrease in the short-term interest rate. As the time dummy for bonds also includes long-term bonds and risk premiums related to NFC s, the yield on NFC bonds do not decrease with 70 %. The decrease in bank loan level is less in 85
2009 than in 2008 which could also be explained by a reduction in the risk free interest rate. Equity rebounded the most. However, this is probably more a consequence of better expectations to future cash flow than a decrease in short-term risk free interest rates, as the latter account for a smaller part of the total cost of capital. Sum up In total, there is no clear sign supporting Effect 4, i.e. lower interest rates on government bonds because savings are moved to investments in T-bills, and thus we reject this. On the other hand Effect 12, that is expansionary monetary policy, is present and we see clear sign of a rebound in bond prices when the short-term interest rates decreases. As expected the time dummies show that the signs are less obvious for bank loans and equity because other factors disturb the ability to isolate the effect of lower interest rates. Due to the valuation method of equity in contrast to bonds, we know that the effect per definition is smaller for equity. Hypothesis III Effect 5 concerns the impact of banks on the financial market through defaults in Asset Backed Securities. The overall level of NFC external finance will decrease: Partly because the credit risk premium increases and partly because banks sell securities, redeem loans and reduce new investments and lending to increase their cash holdings. For the remaining securities and bank loans, held by banks, high risk liquid assets are preferred. The risk premium for NFC securities has increased while they are more liquid than bank loans. This makes securities more appealing for banks. Thus, banks are expected to increase their holdings of securities at the expense of bank loans. The NFC equity and bond prices are expected to rebound, while bank loans are expected to decrease steadily. The change in security financing of NFCs, also depends on the behaviour of other security holders and their share of the securities market. Discussion of hypothesis III In reality banks might change their asset allocation strategy parallel to the first signs of financial distress. Lower liquidity happens as a consequence of a lower solvency degree and lower liquidity on remaining assets. Bank reaction to both lower solvency (that is higher risk taking) and lower liquidity is expected to happen in parallel to a change in solvency and liquidity. The reaction is, as stated above, expected to be changes in the asset allocation strategy of banks. Thus, the first point it to see if liquidity (including solvency) decreased in 2008 and the second point is to address if banks changed their asset allocation strategy 86
parallel to this. There is no reason to prove that NFC securities became more risky, because it is evident that their solvency decreased. Liquidity In appendix 11.3, EuroStat and the US Federal Reserve provide data of the solvency degree of FIs in each country. It has not been possible to find similar data only on commercial banks. Unfortunately so, since commercial banks are the interesting ones because they are the only banks granting loans. However, it is reasonable to believe that the financial system in general is so interdependent that systematic shocks to a large extent hit in the same way, though to different degrees. The solvency degree decreased in 2008 for FIs in all the countries (between 1.45 % and 15.63 %). The decrease continued in 2008 for Denmark, Finland, Norway and the US. The isolated consequence is lower liquidity, higher liquidity risk and higher risk preferences linked to banking activities. Whether the liquidity of the assets decreased depends on whether one believes securities were sold below their true value. I argue later that equity was sold below true value and this is a sign of lower liquidity. Thus bank liquidity fell as a consequence of lower solvency for some countries (which itself is a function of less profitable assets) and lower liquidity of equity investments. Reallocation of bank assets No matter if the focus is on loans, securities, interbank loans or reserves, the trend in the asset value of banks decreased from 2007(Appendix 11.3). The last year where data is available for all countries is 2008, but to support hypothesis III this is adequate. The total bank asset level did not decrease for other countries than Germany. The increase in securities was on average 10.22% in 2007 and 5.27 % in 2008. Bank loans increased on average 15.55 % in 2007 and 4.96 % in 2008 (Appendix 11.3). On average, bank loans and bond increased almost the same in 2008 and therefore there is no strong sign of a change in the respective position of the two assets. And yet: Bank investment in bank loans had increased with around 15 % in 2005-2007. For securities the trend change looks more moderate compared to the previous years. So it does in fact look a bit as if banks changed their strategy with respect to bank loans. This must not be confused with a decrease in bank loans, only a less steep increase. What we do not see, is a decrease in securities even though these are traded on the secondary market where especially equity values dropped. That the value of securities increased can 87
only be explained by a quantitative increase and not by revaluation of already existing securities. Unfortunately we can only guess if these were stocks or bonds. Since the credit risk increased for most underlying assets which banks invested in, the risk of securities also increased. This is in line with the expectations: Banks buy liquid and more risky securities. OECD s bank profitability statistics provide illustrations of the change in the asset portfolio of banks. As expected, banks countervailed higher liquidity risk in 2008 by increasing their reserves relative to other assets. The tendency is true for all countries except Denmark. Loans as a fraction of total assets only decreased slightly. Securities stagnated as a fraction of total assets for the US and Spain, they increased for Norway and decreased for Finland, France and Germany. Thus the picture is mixed. The reason can be that securities cover long- and short-term bonds as well as equity, all at different risk levels. We have no reason to expect all banks to have the same risk preferences and thus security portfolios. It is complicated unravel what really happened to banks in 2008 when it comes to how they reacted to lower equity value, possible credit tightening and lower solvency. On one hand, it is evident that both bank loans and securities increased in level, but less than in the previous year. On the other hand, securities share out of total assets remained constant and bank loans decreased slightly while reserves increased. Thus the overall asset level must have increased, which it did. For this to happen, banks must have borrowed more money, but acknowledged the necessity of placing more capital as highly liquid reserves. Just as expected. Whether the increase in securities was at the expense of bank loans is doubtful. The only weak signs of this could be the large difference between the increase in lending in 2005-2007 and the increase in lending in 2008. Otherwise there are no strong signs. Hypothesis IV Effect 7 states that a basic higher level of systematic risk occurs in market-based countries, because a higher degree of liberalization increases credit aggregates and thus enhances interdependence. This results in a larger rational change in risk perception for market-based countries. Evidence shows that investors assess expected returns and risks using few recent observations. Consequently, they put too little weight on the danger of low returns, as good times had lasted long when the crisis occurred. Since returns before the crisis were expected to be higher in high risk (market-based) countries, so was the miscalculation of downside risk. The effect was a larger increase in required risk premium for all financial assets. Furthermore 88
Effect 8 emphasises that especially the required risk premium (and thus value reduction) associated to equity would increase. The required risk premium would increase less for bonds and least for bank loans. As long as the change is rational it should not change NFC s preferred mix of new external finance. However, it can impact the amount of profitable investments thus resulting in lower net-transactions for all types of finance. Discussion of hypothesis IV The role of hypothesis IV In hypothesis I-III it was stated how we expect a given effect to be more severe in marketbased countries due to their higher level of systematic risk. Still, the precise reaction of the two systems was not concluded upon. Instead it was examined if the effect was present at all. The reason why the reaction was not examined is the following: If we actually found indication of market-based countries being more risky and more exposed to both rational and irrational changes in perceived risk, there is no reason to prove which system that was most exposed to the effects described in hypotheses I III. The effects of financial distress, in all its aspects, would be more severe in market-based countries and therefore financial assets would be affected more (in many different ways). As equity per definition is more risky than debt it will be the most affected type of external finance. Whether the impact of an effect is largest on bonds or bank loans depends on the effect. Thus there is no reason to examine more than once what type of system is more risky and where the perception of risk changes the most. Hypothesis IV addresses if the change in perceived risk was larger in market-based countries and if such change affects the value of equity more than the value of bonds. The reactions of market- and bank-based systems to hypothesis I-III would automatically be derived from the conclusion of which system that is the most risk ones. Changes in perceived risk Perceived systematic risk will impact the risk premium of equity, bonds and bank loans. The change in risk premium will be different for equity, bank loans and bonds. A certain change in perceived risk will impact the yield on bonds more than required interest on bank loans, and it will impact the cost of capital for equity the least. Equity is as a function of both a risk premium rand expected cash flow. During the crisis both parameters probably changed at the same time and therefore it is difficult to isolate the effect of changes in perceived risk. The value of bonds is a function of the perceived risk and the risk free interest rate. We know that as the risk free interest rate decreases so does the yield. Therefore the value of 89
bonds increases. Any decrease in bond value (or lower increase) must therefore be a function of an increased risk premium, especially as net-transactions appear insignificant. Thus comparing the change in the value of bonds in bank- and market-based systems should reveal which system experienced the highest change in perceived risk. The result can then be transferred to the additional risk premium for bank loans and equity (ignoring irrational responses for a second). If we see an additional risk premium on bonds, it means per definition that the equity premium on the same underlying asset have increased more. Consequently, the value of equity has decreased more. If the risk premium is higher for bonds in market-based countries, equity in market-based countries will drop the most. The focus is on 2008 when the financial crisis hit globally. The marginal contribution factors for bonds proved that the countries experiencing the smallest change in market value through 2008 were the market-based countries (see Appendix 8). In addition there is no distinct change in the increase compared to the previous year. The major decrease in trend happened from 2005 to 2007 and here the drops were most significant for bank-based countries. Thus, first of all no distinct reaction to the crisis appears. Secondly, to the extent something occurred it was before the crisis and it did not result in an increase in the risk premium. This is in sharp contrast to any of my expectations and I therefore reject hypothesis IV. One caution must be made: The link between risk premium on bonds and risk premium on equity is rational founded. Thus, it is possible that the risk premium on equity increased due to irrational changes in perceived risk. Also, there is no doubt that expansionary monetary policy can have increased the value of bonds to such an extent that any increase in risk premiums is neutralized. Thus, even though changes in bond prices is the best way to measure increased perceived risk, it might not be sufficient. Possible reasons for lack of evidence That I find no evidence to more systematic risk in market-based countries is interesting, but what is more interesting is why such outcome occurred. The first conclusion is that bonds in market-based countries from 2005 to 2009 were less volatile than bonds in market-based economies. Secondly, they did not drop more severely in 2008, i.e. their change in perceived risk was not larger. The second point can easily be affected by expansionary monetary policy blurring the effect of the raise in the risk premium. However, different degrees of monetary policy cannot explain why market-based countries reacted less to the crisis: Once the US 90
central bank lowers the interest rate, central banks in the rest of the world usually follow in order to keep the exchange rates and competitive strength constant. It is now the time to turn the attention towards the relation between systematic risk, as a function of interdependence, and changes in perceived risk. I argued that the more interdependent nature of market-based financial systems increases the level of systematic risk more when a shock that is big enough occurs. In contrast interdependence was expected to decrease the systematic risk, i.e. worked as a risk absorber, when defaults or other large changes only are moderate. The last point has been ignored throughout the whole paper because I assumed the crisis in 2007-2009 to be so severe that interdependence would only intensify the degree of systematic risk. Nevertheless it appears as if it could be part of the explanation to why market-based countries experience less volatility. That bank-based countries were more volatile than expected can be explained as followed: It was shown earlier that a certain degree of negative correlation existed between the degree of B/TL and the % in bond level and bank loan level measured in real 2003 prices. Thus, it appears as if a general growth in credit aggregates were present for bank-based countries. Form the values in Appendix 1 it is evident that from 2003-2008 the three most geared countries were bank-based. This means, that the countries using bank loans most heavily were in fact the ones lending the most. The more one lend the more interdependent the economy. This does not mean that liberalized countries open up for more gearing as claimed in hypotheses IV. It only means that the bank-based financial system perhaps for one reason or another provides an even more attractive environment for lending. Systematic risk increase as a consequence for more gearing, so higher gearing could be the explanation to why the volatility is larger in bank-based economies. Hypothesis V Effect 10 argues that panics stemming from changes in perceived credit risk enhance the liquidity risk of the financial markets. This enhances the liquidity premium and decreases the price even further than what the change in credit risk did. The effect is expected to be largest for: c. Financial assets subject to high systematic risk; that is assets in market-based countries and especially equity and then bonds. The probability of panics is expected to increase as a function of systematic risk, because high systematic risk results in a 91
larger percentage loss of the portfolio and this hurts more. Investors sell to avoid further loses. d. Liquid markets where signalling plays a larger role. Reliance on signalling can reinforce irrational volatility and thereby increase liquidity risk. If the market, for some reason, loses its confidence in the price it will drop heavily because; (i) many investors must agree for a price to drop heavily and thus there must be a good reason for it. (ii) If investors realize that irrational price drops are a consequence of the first price drop, the price will drop even further. As liquidity is expected to be larger in market-based economies so is the effect. Discussion of hypothesis V Examination of hypothesis Va and Vb are both based on movements in the marginal contribution factors. The marginal contribution factors measure changes in level over time conditional on the capital structure. Since the capital structure is what distinguishes bank- and market-based countries, the factor enables us to see which of the two systems had the stronger reaction. Panics are seen as extreme and irrational value movements, disregarding what the underlying reason for panics is. Therefore the effect of panics related to liquidity and the effect of panics related to systematic risk are inseparably measured by changes in value over time. Systematic risk in market- and bank-based countries Based on the marginal contribution factors, hypothesis IV concluded that there are no signs during the crisis of higher systematic risk for market-based countries. Therefore panics, as a function of systematic risk, are not more likely in these countries. It was concluded that the underlying reason must be that the market-based systems do not possess a higher degree of systematic risk (in spite of higher interdependence between market actors). Maybe, the bankbased countries are more interdependent than what was first assumed. In any case, marketbased countries do not have stronger signs of panics and thus increased liquidity premium and value drops. Liquidity in market- and bank-based countries Market-based countries were expected to be the most liquid ones. The consequence of this is the same, namely larger value drop when the first reductions are as severe as they appear to be. The conclusion is the same: The value of securities does not decrease more in market- 92
based countries and thus there is no evidence of market-based financial markets being more liquid. Note that this argument assumes that relationship between liquidity and panics holds. However, other authors have described that liquidity, just as interdependence, can stabilize the market value of financial assets as long as the shock it not too big. As it was mentioned in hypothesis IV, this can be one possible explanation. Another could be that the drop of roughly 85 % in equity depending on the specific country is indeed as sign of panic (Appendix 11.4). If so, the point is probably that the security market are just as liquid in the bank-based systems as in the market-based systems and we witnessed panics in both systems. Explanation of why bank-based countries could be just as liquid and risky as market-based countries. A precondition for market efficiency is liquidity. Thus market efficiency requires a sufficient high number of market actors. The security markets in the bank-based countries of continental Europe could be large enough to be categorized as efficient. One should not forget that we are comparing countries that all use securities to a great extent and therefore it is not a question of being liquid or not, but instead a question of how liquid the financial markets are. In addition advanced information tools and real time prices have increased market efficiency all over the world no matter the size of the market. Furthermore better information systems and international regulation have also enabled more international trade and this has increased the interdependence between investors in bank- and market-based economies. Thus, let us loosen up one assumption, namely that creditors are from the same country as the NFC. Defaults in a domestic market hits creditors globally and therefore we cannot keep systematic risk within the domestic borders. Since we now believe the secondary market in bank-based economies to be as efficient as in market-based economies, the two kinds of economies might share most premises for panics. This could in fact be the reason why bank-based countries panicked at least as much as market-based countries. But not why they panicked more. The discussion of hypothesis IV clarifies this phenomenon. Hypothesis VI Effect 6 argues against Effect 5 while introducing the role of irrational behaviour. One thing is that banks might increase their preferred risk level. Another thing is if the market volatility is based on irrational reactions. Under such circumstances profit can be better earned by using direct screening and monitoring, that is by granting loans opposed to investing in securities. 93
Effect 11 suggests that NFCs for the exact same reason prefer loans since the price of these allows for a more rational risk assessment 15 and thus more profitable projects. The risk of NFCs conducting moral hazard is less severe because the interdependence between the bank and the NFC is greater. This and a larger interest in keeping the NFCs running motivate banks to grant loans. The effect is expected to be largest in market-based countries where panics are expected to be more severe. Discussion of hypothesis VI The discussion requires that we look at NFC preferences for new financing as well as bank preferences for new investments. The mix of new financing by NFCs is an equilibrium between the type of finance that NFCs prefer and what they are offered. If panics affected the price of external finance we would see the largest drop in the market mostly affected by panics. Equity would be more affected than bonds and bank loans, because it is more risky and panics are more likely and severe the more risky the financial assets are. Since I am only interested in changes to banks investment preferences to the extent it affects NFCs ability to receive new external finance, I only look at new transactions for NFCs. Irrational behaviour in bank- and market-based countries I found no indication of larger systematic risk in the market-based countries. Thus there is no reason why panics should be more severe in these countries and therefore why the estimated WACC 16 of NFCs should be larger in market-marked countries. This is not to claim that panics do not occur, they are just less severe in market-based countries. The market value of equity decreased tremendously for all countries from mid 2008- around 87 % (Appendix 11.4). Since the value of equity rationally is supposed to reflect all future income for a company that lives forever, intuitively it seems irrational to decrease the value with around 87 % and this is exactly why I postulate that panics occurred. One other explanation to a drop of 87 % could be that the prices were grossly overvalued and in reality this also plays a role if nothing else then due to miscalculation of risk. Net-transactions According to the Regression II output, NFCs do not change their mix of new external finance significantly at any point in time. Equity should be the type of finance that is affected most by panics. Appendix 7 shows net-transactions of equity and there is almost no trend indicating 15 Assuming that irrational risk assessment results in a higher discount rate than rational assessment. 16 Weighted Average Cost of Capital 94
this. Finland and the US are the only countries who show any sign of decrease in net-issuing during the crisis. The insignificance of Regression II suggests that panics did not occur. If they did, the cost of capital would have been too expensive and we would see decrease in gross transactions of equity. Parallel, the value of net-transaction should decrease even further if the price of equity is too low, because companies with adequate capital would purchase own stocks. Again we see no clear signs of this. Thus on one hand we have clear signs of panics in the market price. On the other hand such panics should be mostly reflected in net-transactions for equity, but there are no significant changes over time. Therefore, the first conclusion is that panics did not have impact on NFCs mix of new financing. The explanation to this could be the following: Explanation of why panics can occur without impact on NFCs mix of new finance An explanation could be that stock issuing is subject to great signalling value. Issuing new stocks could be interpreted as an optimistic sign in the middle of a recession and thus the price of these and investors cost of capital could be less than what we would otherwise expect based on market reactions and valuation. In this case, net-transactions would be more constant, because investors separate new issuing from the general market assessment. Hypothesis VII Effect 13 suggests that at a certain risk level, NFCs estimate fewer projects to have a positive NPV, because the prospect of income in the following years are lower than before the crisis. This will reduce net-transactions for all types of finance and the market price of equity. Discussion of hypothesis VII Regression II showed that the mix of new external finance did not change over the years and when the reason was examined in chapter 5.2 it was shown that none of the underlying time series changes systematically over time. Thus, NFCs did not decrease their new financing for any type of external financial source (Appendix 7). The real economy did decrease in the end of 2008 and beginning of 2009 and it hardly grew in the rest of 2009 (Appendix 11.2). This impacts the price of equity, because the slowdown was unexpected. With recessions follow lower prices and rejected investments can be costly both in terms of supply costs and lower revenue in the future. Thus, if the companies have the capital to invest, they would do so. Still, it is expectable that NFCs in general felt a decrease in revenue. Lower revenue all things equal leads to lower profit and thus a lower ability to 95
finance projects with internal capital. Internal capital is the preferred capital source to finance new investments. Let us assume that the NFCs did decrease their investments to some extent. For the projects that still need finance, a smaller degree is financed by internal capital and a larger degree by external finance. Thus, gross transactions on one hand decrease as all investments decrease, but on the other hand a higher fraction of the remaining investments are financed externally. Therefore the decrease in investments is not measurable by looking at external finance, but only by looking at internal finance. This paper provides no further evidence on this argument, but it is evident that companies prefer internal financing and only shift to external finance in absence of internal finance. Thus, based on the available data we cannot conclude that NFCs invested differently under the crisis. In the same manner it is not evident that a stable level of net-transactions is equivalent to a stable level of investments, as the recession probably reduced internal capital and therefore forced the NFCs to shift at least part of their capital preferences to external finance. 96
7 Conclusion This paper addresses changes in external finance for non-financial corporations during the recent financial crisis. Secondly, it addresses how these changes differed between bank-based and market-based financial systems. Non-financial corporations are the main drivers of any economy and their ability to lend at a profitable rate is crucial to overcome the crisis. External finance is defined as the balance value of bonds, equity and bank loans. First, the expectation to changes in external finance was formulated in 7 hypotheses. Secondly, the actual changes were empirically examined. I distinguished between two types of changes: Changes in level (measured as the balance value of liabilities) and changes in the mix of new finance (measured through net-transactions). The hypotheses claimed that credit tightening would force banks to sell securities and decrease their holding in bank loans from 2008 and forward. Depositors were expected to move their savings to investments in T-bills. Defaults in the Asset Backed Security market were expected to reduce the liquidity and increase liquidity risk for banks globally with the consequence of banks selling assets and transforming the remaining assets to high risk liquid assets. All the consequences just mentioned were expected to be larger in countries with a market-based financial system because a higher level of systematic risk was expected in these countries. The increase in perceived risk, both rationally and irrationally founded, were for the same reason expected to be higher in market-based countries and therefore value reductions in all types of external finance should be larger especially for equity. Irrational behaviour in the secondary market should make lending and borrowing through bank loans more attractive. Finally, NFC need for new external finance and the value of NFC equity should be reduced because prospects of future income decreased. Expansionary monetary policy would increase the value of especially bonds, but also bank-loans and equity. This should countervail increased credit risk premiums. The empirical findings were as expected by the hypotheses if we distinguish between bonds, equity and bank loans: The value of bonds never decreased, but it almost stagnated through 2008. The most obvious reason is monetary policy. Equity decreased the most through 2008, just as expected and rebounded already in 2009. The rebound could support enhanced investor preferences for high risk liquid assets and realization of panic in 2008. Bank loans already showed signs of slowdown in 2006 and its value decreased in 2008 and 2009. Decreases in bank loans were expected, as a consequence of lower liquidity and higher liquidity risk. As 97
we see no significant change in net-transaction of bank loans, the decrease in level is mostly due to revaluation and thus a higher perception of liquidity risk. The empirical findings were unexpected by the hypotheses when comparing the change in external finance of countries with bank- and market-based financial systems. Countries with bank-based financial systems were more volatile. They experienced a larger value reduction of securities in 2007-2008 and a larger increase in 2009. The underlying reason could be that bank-based economies for one reason or another actually provide a more attractive environment for lending thus increasing systematic risk and the magnitude of panics to a level above that of countries with a market-based financial system. Due to the weight of bank loans in bank-based economies, the total increase in external finance was less for these countries in 2009, while they still experienced the largest total drop in 2008. In addition it was unexpected that we saw no change in the mix of new finance. This argued against an irrational high risk premium on securities which were otherwise expected to result in NFCs shifting their focus to bank loan financing. Closer examination revealed that not only did the mix not change, but the level of net-transactions did not change significantly either. There could be several reasons for this. Two of the most important are that (i) prices fell, so for those with spare capital 2008-2009 were good years and for those who suffered under financial distress investments fell and (ii) it was only possible to find quarterly net information where monthly gross-transactions had been preferred. It should be emphasised that this paper empirically focuses on the change in level and mix of new finance. Secondly it focuses on the underlying reasons for why such results occurred. Within the limits of a master thesis, it gives concrete answers to how external finance for NFCs changed and which type of financial system that was most affected by the financial crisis. The results support some of the hypotheses and rejected others.. It would be reasonable to conduct further research that strengthens and deepens the findings of the thesis.. I would encourage other researcher to look deeper into the relation between the reaction of financial systems to shock and each of the 7 hypotheses. 98
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Appendix 1 Categorization of countries Data source: - For all countries excl. the US: OECD Statistics, S11, Nonfinancial corporations - For the US: The federal reserve statistics, Nonfarm nonfinancial corporate business, Z1 flow of funds Measure - National currency, current prices, millions Frequency - Annual Period 1996 1997 1998 1999 2000 2001 2002 Country Transaction Austria SAF3LI: Securities other than shares 6.434 11.640 12.244 15.156 17.329 16.532 16.272 SAF4LI: Loans 104.364 111.161 117.674 128.196 145.323 154.041 156.582 SAF5LI: Shares and other equity 59.512 66.557 72.600 81.438 89.479 95.860 104.594 Belgium SAF3LI: Securities other than shares 11.563 11.722 9.681 12.154 14.274 21.738 23.227 SAF4LI: Loans 116.282 127.672 141.164 154.425 170.101 175.139 179.656 SAF5LI: Shares and other equity 163.743 190.035 286.453 341.737 372.532 394.399 298.512 Denmark SAF3LI: Securities other than shares 52.242 68.004 75.029 68.403 77.763 88.715 107.806 SAF4LI: Loans 608.860 653.765 618.626 714.689 835.602 990.300 932.394 SAF5LI: Shares and other equity 410.675 524.842 583.405 779.352 932.078 875.409 740.632 Finland SAF3LI: Securities other than shares 5.326 6.057 5.740 7.842 11.068 13.259 14.823 SAF4LI: Loans 56.094 55.736 58.509 65.949 77.122 77.668 79.238 SAF5LI: Shares and other equity 75.335 91.517 151.382 364.844 344.897 253.714 183.326 France SAF3LI: Securities other than shares 157.713 165.865 182.611 219.246 250.796 318.379 305.823 SAF4LI: Loans 564.888 580.878 577.603 622.928 707.066 759.094 785.802 SAF5LI: Shares and other equity 742.809 917.809 1.231.384 2.031.862 2.052.313 1.644.879 1.385.355 Germany SAF3LI: Securities other than shares 52.207 48.322 46.233 43.811 52.634 62.759 70.948 SAF4LI: Loans 955.170 1.011.029 1.073.210 1.125.991 1.313.825 1.389.202 1.460.676 SAF5LI: Shares and other equity 1.064.355 1.288.735 1.555.014 1.955.564 1.868.237 1.812.064 1.285.958 Greece SAF3LI: Securities other than shares 730 879 1.212 829 4.588 5.263 5.552 SAF4LI: Loans 29.938 33.376 39.290 45.121 53.078 62.597 67.305 SAF5LI: Shares and other equity 24.100 38.590 72.240 149.325 93.045 71.917 57.880 Hungary SAF3LI: Securities other than shares 102.814 113.216 113.087 110.009 107.871 130.206 172.388 SAF4LI: Loans 2.493.694 3.293.102 3.995.793 5.064.123 7.546.721 8.209.260 9.178.956 1 of 31
Appendix 1 Categorization of countries SAF5LI: Shares and other equity 5.635.542 8.071.257 8.827.450 12.434.582 15.972.571 21.938.890 23.180.010 Italy SAF3LI: Securities other than shares 19.982 20.883 24.169 19.880 20.640 40.208 44.162 SAF4LI: Loans 495.107 509.590 525.936 576.766 645.014 684.340 729.960 SAF5LI: Shares and other equity 436.050 543.660 662.375 860.243 864.643 833.589 862.970 Mexico SAF3LI: Securities other than shares.. 174.671 227.091 237.251 277.634 265.331 296.316 SAF4LI: Loans.. 906.308 562.496 551.255 501.643 440.417 584.041 SAF5LI: Shares and other equity.. 1.262.469 907.366 1.460.336 1.203.021 1.157.600 1.079.221 Netherlands SAF3LI: Securities other than shares 14.258 14.804 18.602 25.395 48.464 55.610 56.385 SAF4LI: Loans 256.280 267.629 291.201 330.142 367.452 384.744 395.117 SAF5LI: Shares and other equity 354.262 457.031 503.411 670.160 650.491 591.144 484.039 Norway SAF3LI: Securities other than shares 88.530 105.941 124.636 155.933 213.845 201.878 210.453 SAF4LI: Loans 440.590 529.779 601.764 672.072 739.506 818.579 813.160 SAF5LI: Shares and other equity 509.678 607.780 550.042 717.540 817.243 902.179 790.130 Poland SAF3LI: Securities other than shares 1.791 2.573 6.864 15.301 20.514 23.245 23.814 SAF4LI: Loans 81.917 99.763 137.286 160.783 188.484 201.095 236.883 SAF5LI: Shares and other equity 242.345 252.421 307.731 320.008 320.966 260.469 297.789 Portugal SAF3LI: Securities other than shares 7.255 7.629 9.203 11.428 11.968 13.915 16.351 SAF4LI: Loans 40.426 76.201 89.048 97.409 112.220 128.411 131.785 SAF5LI: Shares and other equity 83.265 118.318 134.354 144.093 146.702 139.047 133.827 Slovak Republic SAF3LI: Securities other than shares 2.586 4.052 4.185 4.489 4.907 4.562 4.719 SAF4LI: Loans 8.722 10.427 11.132 11.023 7.895 8.634 8.612 SAF5LI: Shares and other equity 3.881 5.011 5.245 6.030 9.353 11.725 9.755 Spain SAF3LI: Securities other than shares 18.420 17.080 16.301 17.853 14.499 14.362 11.874 SAF4LI: Loans 192.848 213.787 246.073 295.050 364.420 435.168 492.508 SAF5LI: Shares and other equity 291.717 362.986 508.620 601.967 604.600 639.538 594.514 Sweden SAF3LI: Securities other than shares 118.328 162.250 169.146 193.870 248.207 274.242 324.546 SAF4LI: Loans 1.298.515 1.457.135 1.589.560 1.691.192 1.831.880 2.062.261 2.142.271 SAF5LI: Shares and other equity 1.753.556 2.218.668 2.461.224 3.406.592 3.408.608 2.935.163 2.385.885 The US Securities other than shares 5.047.004 5.474.725 6.132.142 6.849.787 7.528.680 8.019.791 8.282.405 Loans 1.119.938 1.192.873 1.314.843 1.427.935 1.539.638 1.532.046 1.422.108 Shares and other equity 6.880.985 8.019.396 10.002.025 12.433.052 14.214.658 10.682.972 8.996.720 2 of 31
Appendix 1 Categorization of countries Data source: - For all countries excl the US: OECD statistics, S11, Non-financial corporations - For the US: The federal reserve statistics, Nonfarm nonfinancial corporate business, Z1 flow of funds Measure - National currency, current prices, millions Frequency - Annual Period 2003 2004 2005 2006 2007 2008 Country Transaction Austria SAF3LI: Securities other than shares 20.244 23.622 26.020 26.477 30.254 33.092 SAF4LI: Loans 159.737 157.373 165.167 173.815 190.498 203.389 SAF5LI: Shares and other equity 109.960 129.142 209.176 244.039 283.628 249.101 Belgium SAF3LI: Securities other than shares 31.250 37.226 29.461 28.698 28.986 36.956 SAF4LI: Loans 175.156 185.593 185.488 183.276 234.130 222.806 SAF5LI: Shares and other equity 341.404 451.324 541.123 680.229 700.102 550.566 Denmark SAF3LI: Securities other than shares 106.468 138.381 137.028 133.340 111.371 97.226 SAF4LI: Loans 918.286 978.451 1.172.195 1.377.460 1.476.039 1.636.035 SAF5LI: Shares and other equity 801.841 988.972 1.343.006 1.535.152 1.634.051 1.160.948 Finland SAF3LI: Securities other than shares 17.827 18.484 22.477 22.081 23.486 25.695 SAF4LI: Loans 80.328 78.735 82.695 91.116 98.098 113.079 SAF5LI: Shares and other equity 185.984 189.554 229.988 259.246 307.255 188.859 France SAF3LI: Securities other than shares 336.716 337.741 350.483 364.857 344.685 358.859 SAF4LI: Loans 766.941 817.699 896.146 994.247 1.151.223 1.277.477 SAF5LI: Shares and other equity 1.625.457 1.849.444 2.232.883 2.764.554 2.949.188 1.998.025 Germany SAF3LI: Securities other than shares 98.959 103.408 106.640 108.992 118.666 136.951 SAF4LI: Loans 1.451.547 1.358.263 1.361.471 1.444.934 1.507.880 1.586.088 SAF5LI: Shares and other equity 1.497.413 1.578.605 1.741.330 1.973.589 2.227.816 1.640.623 Greece SAF3LI: Securities other than shares 5.367 7.894 10.987 15.682 19.903 25.457 SAF4LI: Loans 74.899 79.484 88.850 97.288 109.899 123.789 SAF5LI: Shares and other equity 71.951 86.481 114.898 132.701 149.117 63.566 Hungary SAF3LI: Securities other than shares 254.566 217.601 386.825 411.285 460.350 563.417 SAF4LI: Loans 11.119.065 11.910.247 14.735.013 16.871.900 20.395.609 26.006.705 3 of 31
Appendix 1 Categorization of countries SAF5LI: Shares and other equity 24.316.043 25.816.826 34.592.690 38.017.986 46.631.405 52.707.075 Italy SAF3LI: Securities other than shares 47.970 53.022 47.671 52.897 63.796 60.307 SAF4LI: Loans 781.939 820.496 885.370 965.324 1.091.111 1.194.111 SAF5LI: Shares and other equity 844.469 953.357 988.996 1.053.955 1.105.097 952.463 Mexico SAF3LI: Securities other than shares 340.766 335.793 371.786 386.059 435.511 498.588 SAF4LI: Loans 495.665 497.673 533.146 640.532 798.646 945.843 SAF5LI: Shares and other equity 1.376.927 1.916.618 2.543.771 3.771.498 4.340.886 3.220.900 Netherlands SAF3LI: Securities other than shares 52.711 47.739 47.936 45.388 44.511 53.742 SAF4LI: Loans 410.186 415.998 438.094 457.716 469.675 477.721 SAF5LI: Shares and other equity 498.705 513.872 624.299 681.501 746.564 577.391 Norway SAF3LI: Securities other than shares 206.026 193.038 206.201 231.968 258.641 255.233 SAF4LI: Loans 805.563 886.826 1.043.395 1.199.627 1.461.885 1.793.499 SAF5LI: Shares and other equity 893.363 1.165.019 1.552.168 1.994.446 2.159.416 1.462.921 Poland SAF3LI: Securities other than shares 24.968 23.872 24.347 22.852 27.506 37.075 SAF4LI: Loans 252.607 241.341 246.126 294.614 347.983 457.802 SAF5LI: Shares and other equity 352.060 418.740 515.783 640.815 776.197 711.409 Portugal SAF3LI: Securities other than shares 15.210 16.864 23.707 27.265 33.390 39.206 SAF4LI: Loans 140.032 138.358 142.653 150.986 165.176 183.182 SAF5LI: Shares and other equity 144.898 157.600 166.214 177.560 197.709 170.119 Slovak Republic SAF3LI: Securities other than shares 4.859 4.878 4.821 4.813 4.807 4.724 SAF4LI: Loans 10.905 10.934 11.112 12.964 15.370 18.921 SAF5LI: Shares and other equity 11.856 14.420 20.444 25.600 27.138 28.820 Spain SAF3LI: Securities other than shares 10.427 11.105 9.902 13.946 14.894 24.622 SAF4LI: Loans 568.915 650.928 799.368 1.027.448 1.216.442 1.307.970 SAF5LI: Shares and other equity 720.706 825.977 950.476 1.133.187 1.260.645 998.323 Sweden SAF3LI: Securities other than shares 390.245 359.181 377.270 371.570 462.939 663.452 SAF4LI: Loans 2.155.572 2.149.555 2.404.532 2.553.887 3.127.017 3.632.223 SAF5LI: Shares and other equity 2.790.019 3.321.369 4.102.667 4.828.214 4.838.017 3.928.536 The US Securities other than shares 8.526.704 8.822.724 9.288.554 10.028.860 11.048.377 11.946.687 Loans 1.337.215 1.320.127 1.411.132 1.519.349 1.787.071 2.059.261 Shares and other equity 9.416.529 11.322.340 12.402.455 13.808.739 15.814.755 12.873.781 4 of 31
Data source: Measure: Underlying scale: Appendix 1 Categorization of countries - The tables above - Capital structure (D), i.e. D = (bonds+ equity)/bank loans - Annual, national currency, current prices, millions Period 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Average D USA 10,65 11,31 12,27 13,50 14,12 12,21 12,15 13,42 15,26 15,37 15,69 15,03 12,05 13,31 Finland 1,44 1,75 2,69 5,65 4,62 3,44 2,50 2,54 2,64 3,05 3,09 3,37 1,90 2,97 Belgium 1,51 1,58 2,10 2,29 2,27 2,38 1,79 2,13 2,63 3,08 3,87 3,11 2,64 2,41 France 1,59 1,87 2,45 3,61 3,26 2,59 2,15 2,56 2,67 2,88 3,15 2,86 1,84 2,58 Poland 2,98 2,56 2,29 2,09 1,81 1,41 1,36 1,49 1,83 2,19 2,25 2,31 1,63 2,02 Hungary 2,30 2,49 2,24 2,48 2,13 2,69 2,54 2,21 2,19 2,37 2,28 2,31 2,05 2,33 Great Britain 3,46 3,74 3,67 4,26 4,01 3,12 2,32 2,47 2,35 2,35 2,22 2,20 1,58 2,91 Slovak Republic 0,74 0,87 0,85 0,95 1,81 1,89 1,68 1,53 1,77 2,27 2,35 2,08 1,77 1,58 Sweden 1,44 1,63 1,65 2,13 2,00 1,56 1,27 1,48 1,71 1,86 2,04 1,70 1,26 1,67 Netherlands 1,44 1,76 1,79 2,11 1,90 1,68 1,37 1,34 1,35 1,53 1,59 1,68 1,32 1,61 Norway 1,36 1,35 1,12 1,30 1,39 1,35 1,23 1,36 1,53 1,69 1,86 1,65 0,96 1,40 Austria 0,63 0,70 0,72 0,75 0,73 0,73 0,77 0,82 0,97 1,42 1,56 1,65 1,39 0,99 Germany 1,17 1,32 1,49 1,78 1,46 1,35 0,93 1,10 1,24 1,36 1,44 1,56 1,12 1,33 Greece 0,83 1,18 1,87 3,33 1,84 1,23 0,94 1,03 1,19 1,42 1,53 1,54 0,72 1,43 Portugal 2,24 1,65 1,61 1,60 1,41 1,19 1,14 1,14 1,26 1,33 1,36 1,40 1,14 1,42 Denmark 0,76 0,91 1,06 1,19 1,21 0,97 0,91 0,99 1,15 1,26 1,21 1,18 0,77 1,04 Italy 0,92 1,11 1,31 1,53 1,37 1,28 1,24 1,14 1,23 1,17 1,15 1,07 0,85 1,18 Spain 1,61 1,78 2,13 2,10 1,70 1,50 1,23 1,29 1,29 1,20 1,12 1,05 0,78 1,44 Average 2,09 2,22 2,42 2,97 2,79 2,42 2,14 2,28 2,53 2,74 2,86 2,75 2,06 2,48 5 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Sources to the national Data: Norway (NO): Statistics Norway (www.statbank.ssb.no/statistikkbanken) National economy and external trade Financial accounts quarterly Table 06711: Debtor: Creditor: sum all sectors, Non financial Corporations incl. Reconciliation, Financial instrument: Bonds, Loans, Shares and other equity, Quater: 2003 k1-2009k3 Denmark (DK): Danmarks Nationalbank via www.nationalbanken.dk database finansielle konti Sector: 11, Data: Ultimostatuskonto, Balancepost: Passiver, Instrument: 300, 331, 332, 400, 410, 420, 500, 511, 516 and 700, Konsolidering: Ukonsolieret, Kvartal: 2003 k1-2009k3 Spain(ES): Banco de España via http://www.bde.es Statistics Financial Accounts of the Spanish Economy Chapter 2. Financial Accounts 2.6.a See the historical series England (UK): Office for National Statistics (www.statistics.gov.uk/statbase) Access time series data Access individual series Financial Statistics Consistent Financial Balance Sheets: Private non-financial Corporations NKZA, NKZM, NLBU, NOPI France (FR): Banque de France Statistiques et enquêtes Base de données Comptes nationaux financiers Comptes Financiers Trimestriels des Agents Non Financiers et des Assurances Sociétés non financières Encours Equity: MU.Q.P.110000.511100.98RET0.T.E.F.A, Bank loans: MN.Q.P.110000.410000.120000.T.E.F.A, MN.Q.P.110000.420000.120000.T.E.F.A, MN.Q.P.110000.400000.201200.T.E.F.A, Bonds : MN.Q.P.110000.332000.980000.T.E.F.A. German (Ger): Bundesbank ( www.bundesbank.de) Statistics Overview of the time series Other economic data Financial Accounts for Germany Stock Liabilities Consolidated CEBA10, CEBB10, CEBH10, CEBK10, CEB41Q. United States of America (US): The Federal Reserve (www.federalreserve.gov) Economic Research and Data Statistical Releases and Historical Data Flow of Funds Accounts of the United States- Z.1 Data download program B. 102(Q) Balance Sheet of Nonfarm Nonfinancial Corporate Finland (FI): Bank of Finland (www.bof.fi) Statistics Financial accounts Table s Liabilities on the quarterly basis Non-financial corporations 1 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Variable description: all variables stem from NFC s financial liabilities as stated on the balance sheet accounts, i.e. they are quoted in levels. B, Domestic currency E, Domestic currency BL, Domestic currency Exchange rate (Local/$) Index inflation(100=2003)q1)/100 B real, $ (Bond level quoted in constant 2003 dollars) = (B / exch. Rate)* (Index inflation (100=2003Q1)/100) E real, $ (Equity level quoted in constant 2003 dollars) = (E/ exch. Rate)* (Index inflation (100=2003Q1)/100) BL real, $ (Bank Loan level quoted in constant 2003 dollars) = (BL / exch. Rate)* (Index inflation (100=2003Q1)/100) Total $ (Total level quoted in constant 2003 dollars) = (Total / exch. Rate)* (Index inflation (100=2003Q1)/100) Log(B real, $) = f(b real, $) Log(E real, $) = f(e real, $) Lag1(BL real /TL real ) T = (BL real /TL real ) T-1 Lag1(Log(B real /TL real )) T = Log(B real /TL real ) T-1 Lag1(Log(E real /TL real )) T = Log(E real /TL real ) T-1 2 of 31
Appendix 2 Observations for each variable in the SAS regression 1 All values are presented in millions Time B Current and domestic prices E Current and domestic prices BL Current and domestic prices Exchange rate (Dom. Curr./$) Inflation (Index 2003, Q1=1) B Real 2003 prices E Real 2003 prices BL Real 2003 prices Norway 2003K2 230.998 1.115.032 1.410.191 7,25 1 31.862 153.798 194.509 380.168 Norway 2003K3 226.365 1.143.816 1.378.524 7,02 0,98 31.601 159.678 192.444 383.722 Norway 2003K4 225.737 1.201.094 1.350.143 6,60 0,98 33.519 178.344 200.476 412.339 Norway 2004K1 222.777 1.295.647 1.410.900 6,90 0,99 31.964 185.897 202.433 420.294 Norway 2004K2 235.810 1.331.067 1.478.895 6,94 0,99 33.639 189.878 210.966 434.483 Norway 2004K3 231.353 1.373.556 1.515.182 6,72 0,99 34.083 202.354 223.219 459.656 Norway 2004K4 213.885 1.557.862 1.560.902 6,03 1 35.470 258.352 258.856 552.678 Norway 2005K1 215.812 1.656.257 1.585.987 6,32 1 34.147 262.066 250.947 547.161 Norway 2005K2 225.076 1.774.556 1.628.346 6,54 1,01 34.759 274.052 251.472 560.284 Norway 2005K3 225.819 1.987.527 1.649.607 6,54 1,01 34.874 306.942 254.756 596.572 Norway 2005K4 232.114 2.058.410 1.704.188 6,76 1,02 35.023 310.588 257.141 602.752 Norway 2006K1 229.461 2.300.136 1.786.018 6,58 1,02 35.570 356.556 276.860 668.986 Norway 2006K2 235.345 2.272.839 1.852.508 6,24 1,03 38.847 375.164 305.783 719.794 Norway 2006K3 256.716 2.253.165 1.922.574 6,50 1,04 41.075 360.506 307.612 709.193 Norway 2006K4 253.779 2.720.930 1.943.206 6,25 1,04 42.229 452.763 323.349 818.341 Norway 2007K1 269.301 2.804.788 2.082.129 6,09 1,03 45.547 474.373 352.150 872.070 Norway 2007K2 284.220 2.983.644 2.214.301 5,90 1,04 50.100 525.930 390.317 966.348 Total Real 2003 prices Norway 2007K3 279.568 2.944.358 2.267.918 5,44 1,04 53.447 562.892 433.573 1.049.911 Norway 2007K4 282.211 2.999.380 2.430.322 5,40 1,05 54.874 583.213 472.563 1.110.650 Norway 2008K1 282.870 2.644.207 2.569.251 5,09 1,06 58.908 550.660 535.050 1.144.618 Norway 2008K2 284.919 2.801.110 2.631.148 5,08 1,07 60.012 589.998 554.198 1.204.209 3 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Norway 2008K3 288.614 2.331.685 2.742.215 5,82 1,08 53.557 432.684 508.865 995.106 Norway 2008K4 284.142 2.084.206 2.914.066 6,99 1,09 44.308 325.005 454.411 823.724 Norway 2009K1 327.507 2.023.303 2.929.747 6,38 1,09 55.953 345.674 500.537 902.164 Norway 2009K2 395.987 2.149.042 2.933.368 6,16 1,1 70.712 383.758 523.816 978.285 Norway 2009K3 393.098 2.233.905 2.851.701 6,16 1,1 70.196 398.912 509.232 978.340 DK 2003K2 103.277 1.086.407 1.034.840 6,50 1 15.889 167.140 159.206 342.234 DK 2003K3 111.809 1.142.079 1.018.636 6,37 1 17.552 179.290 159.911 356.754 DK 2003K4 107.783 1.120.410 1.000.698 6,23 1 17.301 179.841 160.626 357.767 DK 2004K1 111.956 1.259.482 1.038.125 6,09 1,01 18.567 208.880 172.169 399.616 DK 2004K2 121.869 1.293.223 1.062.392 6,11 1,01 20.145 213.773 175.616 409.535 DK 2004K3 126.307 1.338.355 1.074.155 5,99 1,01 21.297 225.666 181.118 428.081 DK 2004K4 141.975 1.437.928 1.045.698 5,87 1,02 24.691 250.074 181.861 456.626 DK 2005K1 133.524 1.619.211 1.088.026 5,74 1,02 23.727 287.734 193.343 504.804 DK 2005K2 144.924 1.870.042 1.130.019 6,16 1,03 24.232 312.686 188.948 525.866 DK 2005K3 143.217 2.041.190 1.152.777 6,10 1,03 24.183 344.660 194.649 563.492 DK 2005K4 142.493 2.145.679 1.208.822 6,32 1,04 23.448 353.086 198.920 575.455 DK 2006K1 127.199 2.517.795 1.292.923 6,16 1,04 21.475 425.082 218.286 664.843 DK 2006K2 144.920 2.410.248 1.301.244 5,86 1,05 25.967 431.870 233.158 690.995 DK 2006K3 144.462 2.818.457 1.351.372 5,89 1,05 25.753 502.441 240.907 769.101 DK 2006K4 139.360 3.007.101 1.394.037 5,66 1,05 25.853 557.854 258.611 842.319 DK 2007K1 138.924 3.043.337 1.446.400 5,59 1,06 26.343 577.091 274.273 877.707 DK 2007K2 132.139 3.377.745 1.457.444 5,51 1,07 25.660 655.932 283.025 964.617 DK 2007K3 120.115 3.232.799 1.462.618 5,25 1,06 24.252 652.718 295.310 972.279 DK 2007K4 118.184 2.810.053 1.512.569 5,07 1,08 25.175 598.591 322.204 945.971 DK 2008K1 116.294 2.595.178 1.560.827 4,71 1,09 26.913 600.583 361.210 988.706 DK 2008K2 114.962 2.680.973 1.623.240 4,70 1,11 27.151 633.166 383.361 1.043.678 DK 2008K3 112.839 2.293.898 1.652.240 5,21 1,11 24.041 488.719 352.013 864.772 4 of 31
Appendix 2 Observations for each variable in the SAS regression 1 DK 2008K4 108.574 1.718.195 1.692.463 5,24 1,11 23.021 364.316 358.860 746.198 DK 2009K1 107.329 1.547.864 1.692.139 5,26 1,11 22.649 326.641 357.086 706.376 DK 2009K2 118.404 1.797.883 1.665.499 5,26 1,12 25.211 382.819 354.631 762.662 DK 2009K3 122.330 1.853.784 1.637.393 5,08 1,12 26.970 408.708 361.000 796.679 Spain 2003K2 12.762 749.125 648.011 1,14 1 11.195 657.127 568.430 1.236.753 Spain 2003K3 12.697 745.352 662.205 1,16 1,01 11.055 648.970 576.575 1.236.601 Spain 2003K4 11.936 815.085 694.317 1,20 1,03 10.245 699.615 595.955 1.305.815 Spain 2004K1 12.965 841.364 712.886 1,22 1,02 10.839 703.435 596.020 1.310.294 Spain 2004K2 12.661 848.090 741.049 1,21 1,05 10.987 735.946 643.059 1.389.992 Spain 2004K3 12.528 854.265 759.449 1,24 1,05 10.609 723.370 643.082 1.377.060 Spain 2004K4 11.797 928.686 790.804 1,36 1,06 9.195 723.829 616.362 1.349.386 Spain 2005K1 12.849 967.609 821.144 1,29 1,06 10.559 795.089 674.738 1.480.386 Spain 2005K2 12.887 1.030.737 862.791 1,20 1,08 11.598 927.663 776.512 1.715.773 Spain 2005K3 12.043 1.125.563 890.153 1,20 1,08 10.838 1.013.007 801.138 1.824.983 Spain 2005K4 11.036 1.132.008 939.520 1,17 1,1 10.376 1.064.281 883.309 1.957.966 Spain 2006K1 11.404 1.226.915 1.001.626 1,21 1,1 10.367 1.115.378 910.569 2.036.314 Spain 2006K2 11.271 1.231.722 1.060.263 1,27 1,12 9.940 1.086.243 935.035 2.031.218 Spain 2006K3 14.298 1.287.632 1.105.229 1,20 1,12 13.345 1.201.790 1.031.547 2.246.682 Spain 2006K4 14.514 1.381.945 1.170.566 1,30 1,13 12.616 1.201.229 1.017.492 2.231.337 Spain 2007K1 13.424 1.444.969 1.216.839 1,33 1,13 11.405 1.227.680 1.033.856 2.272.941 Spain 2007K2 13.848 1.464.041 1.284.474 1,35 1,15 11.797 1.247.146 1.094.182 2.353.125 Spain 2007K3 15.388 1.458.110 1.321.005 1,41 1,15 12.551 1.189.239 1.077.416 2.279.206 Spain 2007K4 15.748 1.458.436 1.360.928 1,47 1,17 12.534 1.160.796 1.083.188 2.256.518 Spain 2008K1 18.927 1.305.612 1.372.379 1,58 1,18 14.135 975.077 1.024.942 2.014.154 Spain 2008K2 17.182 1.207.797 1.413.126 1,57 1,2 13.133 923.157 1.080.096 2.016.386 Spain 2008K3 19.171 1.082.462 1.439.399 1,43 1,2 16.088 908.360 1.207.887 2.132.335 Spain 2008K4 25.415 990.781 1.456.681 1,39 1,2 21.941 855.350 1.257.567 2.134.858 5 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Spain 2009K1 28.230 856.697 1.469.229 1,41 1,18 23.625 716.952 1.229.567 1.970.145 Spain 2009K2 25.518 932.897 1.460.796 1,41 1,2 21.717 793.955 1.243.231 2.058.903 Spain 2009K3 24.859 1.022.617 1.452.447 1,46 1,19 20.262 833.503 1.183.843 2.037.608 England 2003K2 306.459 1.269.019 724.746 1,65 1 185.733 769.102 439.240 1.394.075 England 2003K3 311.718 1.307.074 749.749 1,67 1,01 188.524 790.506 453.441 1.432.471 England 2003K4 306.189 1.411.701 728.513 1,78 1,01 173.736 801.021 413.370 1.388.128 England 2004K1 305.517 1.412.703 729.092 1,83 1,01 168.619 779.689 402.395 1.350.702 England 2004K2 299.436 1.440.290 744.675 1,81 1,02 168.743 811.655 419.651 1.400.049 England 2004K3 290.984 1.456.614 798.549 1,79 1,02 165.812 830.026 455.039 1.450.877 England 2004K4 298.355 1.521.507 815.396 1,93 1,03 159.226 811.996 435.160 1.406.381 England 2005K1 300.637 1.559.950 863.058 1,88 1,03 164.711 854.653 472.846 1.492.210 England 2005K2 319.908 1.616.798 880.679 1,79 1,04 185.868 939.369 511.679 1.636.916 England 2005K3 332.190 1.716.218 907.794 1,76 1,05 198.182 1.023.880 541.582 1.763.643 England 2005K4 348.725 1.765.299 922.523 1,72 1,05 212.884 1.077.653 563.168 1.853.706 England 2006K1 353.513 1.885.920 977.071 1,73 1,05 214.560 1.144.634 593.020 1.952.213 England 2006K2 348.493 1.846.215 979.377 1,83 1,06 201.859 1.069.392 567.289 1.838.541 England 2006K3 355.650 1.857.947 1.041.342 1,87 1,05 199.697 1.043.232 584.711 1.827.640 England 2006K4 378.888 1.943.853 1.063.737 1,90 1,06 211.380 1.084.465 593.453 1.889.298 England 2007K1 355.334 1.991.240 1.047.878 1,95 1,07 194.978 1.092.629 574.989 1.862.597 England 2007K2 352.206 2.056.980 1.067.054 2,00 1,08 190.191 1.110.769 576.209 1.877.170 England 2007K3 357.315 2.046.996 1.098.327 2,03 1,08 190.099 1.089.042 584.332 1.863.472 England 2007K4 377.967 2.062.193 1.124.874 2,00 1,1 207.882 1.134.206 618.681 1.960.769 England 2008K1 371.102 1.903.025 1.161.508 1,98 1,1 206.168 1.057.236 645.282 1.908.686 England 2008K2 366.387 1.870.242 1.139.765 1,99 1,11 204.367 1.043.200 635.748 1.883.315 England 2008K3 393.401 1.654.316 1.136.628 1,79 1,11 243.953 1.025.861 704.836 1.974.650 England 2008K4 386.515 1.552.222 1.254.183 1,45 1,11 295.884 1.188.253 960.099 2.444.235 England 2009K1 362.425 1.475.643 1.207.130 1,60 1,11 251.432 1.023.727 837.446 2.112.606 6 of 31
Appendix 2 Observations for each variable in the SAS regression 1 England 2009K2 407.928 1.571.188 1.117.794 1,65 1,11 274.424 1.056.981 751.971 2.083.376 England 2009K3 480.971 1.775.417 1.129.919 1,61 1,11 331.601 1.224.045 779.012 2.334.659 France 2003K2 312.012 721.434 546.167 1,14 1 273.695 632.837 479.094 1.385.625 France 2003K3 313.513 742.335 537.622 1,16 1,01 272.973 646.343 468.102 1.387.418 France 2003K4 322.373 817.860 534.661 1,20 1,01 271.331 688.366 450.006 1.409.702 France 2004K1 318.094 842.028 535.512 1,22 1,02 265.947 703.991 447.723 1.417.661 France 2004K2 321.722 824.615 546.245 1,21 1,03 273.863 701.945 464.985 1.440.793 France 2004K3 323.361 785.421 550.674 1,24 1,03 268.598 652.406 457.415 1.378.419 France 2004K4 319.807 835.100 564.501 1,36 1,04 244.558 638.606 431.677 1.314.841 France 2005K1 338.186 899.651 563.996 1,29 1,04 272.646 725.300 454.694 1.452.641 France 2005K2 350.975 940.436 578.315 1,20 1,05 307.103 822.882 506.026 1.636.010 France 2005K3 347.482 1.045.742 582.261 1,20 1,05 304.047 915.024 509.478 1.728.549 France 2005K4 332.043 1.120.668 605.615 1,17 1,06 300.825 1.015.306 548.677 1.864.808 France 2006K1 326.800 1.257.423 623.819 1,21 1,06 286.288 1.101.544 546.486 1.934.318 France 2006K2 330.000 1.208.086 638.417 1,27 1,07 278.031 1.017.836 537.879 1.833.747 France 2006K3 330.643 1.256.639 644.114 1,20 1,07 294.823 1.120.503 574.335 1.989.661 France 2006K4 333.964 1.366.450 658.194 1,30 1,07 274.878 1.124.693 541.744 1.941.316 France 2007K1 338.694 1.434.737 670.322 1,33 1,07 272.483 1.154.262 539.282 1.966.027 France 2007K2 348.158 1.558.885 694.361 1,35 1,09 281.105 1.258.655 560.632 2.100.393 France 2007K3 339.389 1.480.385 715.025 1,41 1,09 262.365 1.144.411 552.750 1.959.525 France 2007K4 306.399 1.482.903 739.093 1,47 1,1 229.278 1.109.655 553.063 1.891.996 France 2008K1 294.464 1.238.457 761.679 1,58 1,11 206.870 870.055 535.104 1.612.029 France 2008K2 307.336 1.174.542 779.718 1,57 1,13 221.204 845.371 561.198 1.627.773 France 2008K3 310.776 1.043.756 795.300 1,43 1,13 245.578 824.786 628.454 1.698.818 France 2008K4 320.716 877.600 807.374 1,39 1,12 258.419 707.131 650.546 1.616.096 France 2009K1 333.134 1.058.261 811.399 1,41 1,12 264.617 840.604 644.516 1.749.737 France 2009K2 352.349 1.161.404 798.361 1,41 1,12 279.880 922.534 634.159 1.836.573 7 of 31
Appendix 2 Observations for each variable in the SAS regression 1 France 2009K3 363.711 1.389.244 789.024 1,46 1,12 279.011 1.065.721 605.279 1.950.011 Germany 2003K2 96.600 1.352.600 1.355.500 1,14 1 84.737 1.186.491 1.189.035 2.460.263 Germany 2003K3 96.000 1.370.700 1.345.500 1,16 1 82.759 1.181.638 1.159.914 2.424.310 Germany 2003K4 98.900 1.497.400 1.328.600 1,20 1 82.417 1.247.833 1.107.167 2.437.417 Germany 2004K1 98.700 1.508.300 1.299.500 1,22 1,01 81.711 1.248.675 1.075.816 2.406.201 Germany 2004K2 98.800 1.541.200 1.294.800 1,21 1,02 83.286 1.299.193 1.091.484 2.473.964 Germany 2004K3 102.300 1.515.800 1.269.400 1,24 1,02 84.150 1.246.868 1.044.184 2.375.202 Germany 2004K4 103.400 1.578.600 1.251.800 1,36 1,02 77.550 1.183.950 938.850 2.200.350 Germany 2005K1 109.600 1.609.700 1.259.700 1,29 1,03 87.510 1.285.264 1.005.807 2.378.581 Germany 2005K2 111.000 1.654.000 1.263.000 1,20 1,03 95.275 1.419.683 1.084.075 2.599.033 Germany 2005K3 110.300 1.706.700 1.258.100 1,20 1,04 95.593 1.479.140 1.090.353 2.665.087 Germany 2005K4 106.700 1.741.300 1.262.700 1,17 1,05 95.756 1.562.705 1.133.192 2.791.654 Germany 2006K1 113.100 1.880.500 1.280.800 1,21 1,05 98.145 1.631.839 1.111.438 2.841.421 Germany 2006K2 119.100 1.831.800 1.294.200 1,27 1,06 99.406 1.528.904 1.080.198 2.708.509 Germany 2006K3 115.500 1.847.700 1.326.400 1,20 1,06 102.025 1.632.135 1.171.653 2.905.813 Germany 2006K4 109.000 1.973.600 1.333.400 1,30 1,06 88.877 1.609.243 1.087.234 2.785.354 Germany 2007K1 112.900 2.057.300 1.350.300 1,33 1,07 90.829 1.655.121 1.086.332 2.832.282 Germany 2007K2 112.200 2.189.100 1.368.000 1,35 1,08 89.760 1.751.280 1.094.400 2.935.440 Germany 2007K3 112.400 2.199.000 1.375.700 1,41 1,08 86.094 1.684.340 1.053.728 2.824.162 Germany 2007K4 118.700 2.227.900 1.394.600 1,47 1,09 88.016 1.651.980 1.034.091 2.774.087 Germany 2008K1 128.300 1.992.100 1.390.900 1,58 1,1 89.323 1.386.905 968.348 2.444.576 Germany 2008K2 118.500 1.939.300 1.416.500 1,57 1,11 83.780 1.371.097 1.001.475 2.456.352 Germany 2008K3 126.500 1.828.300 1.442.100 1,43 1,12 99.077 1.431.955 1.129.477 2.660.509 Germany 2008K4 137.000 1.640.700 1.474.200 1,39 1,11 109.403 1.310.199 1.177.239 2.596.841 Germany 2009K1 129.200 1.494.700 1.493.900 1,41 1,11 101.711 1.176.679 1.176.049 2.454.438 Germany 2009K2 124.500 1.629.700 1.476.600 1,41 1,11 98.011 1.282.955 1.162.430 2.543.396 Germany 2009K3 132.800 1.726.000 1.459.000 1,46 1,11 100.964 1.312.233 1.109.240 2.522.437 8 of 31
Appendix 2 Observations for each variable in the SAS regression 1 US 2003K2 3.097.200 8.820.300 766.200 1,00 1 3.097.200 8.820.300 766.200 12.683.700 US 2003K3 3.122.900 9.042.000 756.328 1,00 1,01 3.154.129 9.132.420 763.892 13.050.441 US 2003K4 3.119.400 10.139.500 769.306 1,00 1,02 3.181.788 10.342.290 784.692 14.308.770 US 2004K1 3.158.200 10.206.600 774.012 1,00 1,02 3.221.364 10.410.732 789.492 14.421.588 US 2004K2 3.168.100 10.300.600 787.992 1,00 1,03 3.263.143 10.609.618 811.632 14.684.393 US 2004K3 3.189.400 9.907.300 792.373 1,00 1,04 3.316.976 10.303.592 824.068 14.444.636 US 2004K4 3.218.600 10.846.900 823.988 1,00 1,04 3.347.344 11.280.776 856.947 15.485.067 US 2005K1 3.240.700 10.639.100 850.915 1,00 1,05 3.402.735 11.171.055 893.461 15.467.251 US 2005K2 3.249.100 10.629.600 883.962 1,00 1,06 3.444.046 11.267.376 937.000 15.648.422 US 2005K3 3.281.000 11.005.500 892.770 1,00 1,07 3.510.670 11.775.885 955.263 16.241.818 US 2005K4 3.277.300 11.010.412 932.500 1,00 1,08 3.539.484 11.891.245 1.007.099 16.437.828 US 2006K1 3.344.400 12.879.300 949.418 1,00 1,09 3.645.396 14.038.437 1.034.865 18.718.698 US 2006K2 3.404.300 13.341.800 1.002.285 1,00 1,1 3.744.730 14.675.980 1.102.514 19.523.224 US 2006K3 3.438.500 13.698.900 993.103 1,00 1,11 3.816.735 15.205.779 1.102.344 20.124.858 US 2006K4 3.523.600 14.048.500 1.047.177 1,00 1,11 3.911.196 15.593.835 1.162.366 20.667.397 US 2007K1 3.608.600 14.345.900 1.106.420 1,00 1,12 4.041.632 16.067.408 1.239.190 21.348.230 US 2007K2 3.737.600 15.738.400 1.190.950 1,00 1,13 4.223.488 17.784.392 1.345.773 23.353.653 US 2007K3 3.768.400 15.833.100 1.281.090 1,00 1,14 4.295.976 18.049.734 1.460.442 23.806.152 US 2007K4 3.842.600 15.242.900 1.347.295 1,00 1,15 4.418.990 17.529.335 1.549.389 23.497.714 US 2008K1 3.934.800 13.733.300 1.359.641 1,00 1,16 4.564.368 15.930.628 1.577.183 22.072.179 US 2008K2 4.024.300 13.778.700 1.380.837 1,00 1,17 4.708.431 16.121.079 1.615.580 22.445.090 US 2008K3 4.054.100 12.267.600 1.392.950 1,00 1,19 4.824.379 14.598.444 1.657.611 21.080.434 US 2008K4 4.088.100 10.055.000 1.369.930 1,00 1,19 4.864.839 11.965.450 1.630.216 18.460.505 US 2009K1 4.208.700 9.024.800 1.347.154 1,00 1,19 5.008.353 10.739.512 1.603.113 17.350.978 US 2009K2 4.288.700 10.412.300 1.340.392 1,00 1,18 5.060.666 12.286.514 1.581.663 18.928.843 US 2009K3 4.345.000 11.977.900 1.329.512 1,00 1,18 5.127.100 14.133.922 1.568.824 20.829.846 Finland 2003K2 22.427 118.617 37.511 1,14 1 19.673 104.050 32.904 156.627 9 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Finland 2003K3 21.736 119.787 38.662 1,16 1 18.738 103.265 33.329 155.331 Finland 2003K4 21.031 126.874 39.812 1,20 1 17.525 105.729 33.177 156.431 Finland 2004K1 19.779 139.516 41.792 1,22 1 16.212 114.357 34.256 164.825 Finland 2004K2 20.444 119.537 43.773 1,21 1 16.896 98.791 36.176 151.863 Finland 2004K3 19.457 116.060 45.753 1,24 1 15.691 93.597 36.897 146.185 Finland 2004K4 18.807 124.639 47.733 1,36 1,01 13.967 92.563 35.449 141.978 Finland 2005K1 19.594 131.583 47.216 1,29 1 15.189 102.002 36.601 153.793 Finland 2005K2 20.967 145.314 46.698 1,20 1,01 17.647 122.306 39.304 179.257 Finland 2005K3 21.148 156.293 46.181 1,20 1,01 17.800 131.547 38.869 188.215 Finland 2005K4 20.945 160.041 45.663 1,17 1,02 18.260 139.523 39.809 197.591 Finland 2006K1 21.985 184.046 46.604 1,21 1,02 18.533 155.146 39.286 212.965 Finland 2006K2 23.738 168.369 47.545 1,27 1,03 19.252 136.551 38.560 194.363 Finland 2006K3 23.177 170.239 48.485 1,20 1,02 19.700 144.703 41.212 205.616 Finland 2006K4 22.735 183.990 49.426 1,30 1,03 18.013 145.777 39.161 202.950 Finland 2007K1 24.143 195.832 52.045 1,33 1,03 18.697 151.659 40.305 210.662 Finland 2007K2 24.886 220.724 54.663 1,35 1,04 19.172 170.039 42.111 231.322 Finland 2007K3 24.885 234.081 57.282 1,41 1,04 18.355 172.655 42.250 233.260 Finland 2007K4 23.238 224.286 59.900 1,47 1,05 16.599 160.204 42.786 219.589 Finland 2008K1 21.143 187.238 63.089 1,58 1,07 14.318 126.800 42.725 183.844 Finland 2008K2 20.504 161.642 67.528 1,57 1,08 14.105 111.193 46.452 171.750 Finland 2008K3 19.652 127.401 71.966 1,43 1,09 14.979 97.110 54.855 166.944 Finland 2008K4 18.474 98.450 76.404 1,39 1,09 14.487 77.202 59.914 151.603 Finland 2009K1 21.322 83.745 76.404 1,41 1,09 16.483 64.739 59.064 140.286 Finland 2009K2 23.258 105.555 76.404 1,41 1,1 18.145 82.348 59.606 160.099 Finland 2009K3 23.875 116.650 76.404 1,46 1,1 17.988 87.887 57.565 163.440 10 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Time log (B real $ ) log (E real $ ) lag1(bl/tl) real$ lag1 (log(b/tl) real $ Lag(log(E/TL) real $ Norway 2003K2 4,503 5,187 0,523-1,065-0,408 Norway 2003K3 4,500 5,203 0,512-1,077-0,393 Norway 2003K4 4,525 5,251 0,502-1,084-0,381 Norway 2004K1 4,505 5,269 0,486-1,090-0,364 Norway 2004K2 4,527 5,278 0,482-1,119-0,354 Norway 2004K3 4,533 5,306 0,486-1,111-0,359 Norway 2004K4 4,550 5,412 0,486-1,130-0,356 Norway 2005K1 4,533 5,418 0,468-1,193-0,330 Norway 2005K2 4,541 5,438 0,459-1,205-0,320 Norway 2005K3 4,543 5,487 0,449-1,207-0,311 Norway 2005K4 4,544 5,492 0,427-1,233-0,289 Norway 2006K1 4,551 5,552 0,427-1,236-0,288 Norway 2006K2 4,589 5,574 0,414-1,274-0,273 Norway 2006K3 4,614 5,557 0,425-1,268-0,283 Norway 2006K4 4,626 5,656 0,434-1,237-0,294 Norway 2007K1 4,658 5,676 0,395-1,287-0,257 Norway 2007K2 4,700 5,721 0,404-1,282-0,264 Norway 2007K3 4,728 5,750 0,404-1,285-0,264 Norway 2007K4 4,739 5,766 0,413-1,293-0,271 Norway 2008K1 4,770 5,741 0,425-1,306-0,280 Norway 2008K2 4,778 5,771 0,467-1,288-0,318 Norway 2008K3 4,729 5,636 0,460-1,302-0,310 Norway 2008K4 4,646 5,512 0,511-1,269-0,362 Norway 2009K1 4,748 5,539 0,552-1,269-0,404 Norway 2009K2 4,849 5,584 0,555-1,207-0,417 11 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Norway 2009K3 4,846 5,601 0,535-1,141-0,406 DK 2003K2 4,201 5,223 0,477-1,325-0,323 DK 2003K3 4,244 5,254 0,465-1,333-0,311 DK 2003K4 4,238 5,255 0,448-1,308-0,299 DK 2004K1 4,269 5,320 0,449-1,316-0,299 DK 2004K2 4,304 5,330 0,431-1,333-0,282 DK 2004K3 4,328 5,353 0,429-1,308-0,282 DK 2004K4 4,393 5,398 0,423-1,303-0,278 DK 2005K1 4,375 5,459 0,398-1,267-0,261 DK 2005K2 4,384 5,495 0,383-1,328-0,244 DK 2005K3 4,384 5,537 0,359-1,336-0,226 DK 2005K4 4,370 5,548 0,345-1,367-0,213 DK 2006K1 4,332 5,628 0,346-1,390-0,212 DK 2006K2 4,414 5,635 0,328-1,491-0,194 DK 2006K3 4,411 5,701 0,337-1,425-0,204 DK 2006K4 4,413 5,747 0,313-1,475-0,185 DK 2007K1 4,421 5,761 0,307-1,513-0,179 DK 2007K2 4,409 5,817 0,312-1,523-0,182 DK 2007K3 4,385 5,815 0,293-1,575-0,167 DK 2007K4 4,401 5,777 0,304-1,603-0,173 DK 2008K1 4,430 5,779 0,341-1,575-0,199 DK 2008K2 4,434 5,802 0,365-1,565-0,216 DK 2008K3 4,381 5,689 0,367-1,585-0,217 DK 2008K4 4,362 5,561 0,407-1,556-0,248 DK 2009K1 4,355 5,514 0,481-1,511-0,311 DK 2009K2 4,402 5,583 0,506-1,494-0,335 DK 2009K3 4,431 5,611 0,465-1,481-0,299 12 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Spain 2003K2 4,049 5,818 0,479-2,017-0,291 Spain 2003K3 4,044 5,812 0,460-2,043-0,275 Spain 2003K4 4,011 5,845 0,466-2,049-0,280 Spain 2004K1 4,035 5,847 0,456-2,105-0,271 Spain 2004K2 4,041 5,867 0,455-2,082-0,270 Spain 2004K3 4,026 5,859 0,463-2,102-0,276 Spain 2004K4 3,964 5,860 0,467-2,113-0,280 Spain 2005K1 4,024 5,900 0,457-2,167-0,271 Spain 2005K2 4,064 5,967 0,456-2,147-0,270 Spain 2005K3 4,035 6,006 0,453-2,170-0,267 Spain 2005K4 4,016 6,027 0,439-2,226-0,256 Spain 2006K1 4,016 6,047 0,451-2,276-0,265 Spain 2006K2 3,997 6,036 0,447-2,293-0,261 Spain 2006K3 4,125 6,080 0,460-2,310-0,272 Spain 2006K4 4,101 6,080 0,459-2,226-0,272 Spain 2007K1 4,057 6,089 0,456-2,248-0,269 Spain 2007K2 4,072 6,096 0,455-2,299-0,268 Spain 2007K3 4,099 6,075 0,465-2,300-0,276 Spain 2007K4 4,098 6,065 0,473-2,259-0,283 Spain 2008K1 4,150 5,989 0,480-2,255-0,289 Spain 2008K2 4,118 5,965 0,509-2,154-0,315 Spain 2008K3 4,206 5,958 0,536-2,186-0,339 Spain 2008K4 4,341 5,932 0,566-2,122-0,371 Spain 2009K1 4,373 5,855 0,589-1,988-0,397 Spain 2009K2 4,337 5,900 0,624-1,921-0,439 Spain 2009K3 4,307 5,921 0,604-1,977-0,414 England 2003K2 5,269 5,886 0,329-0,866-0,272 13 of 31
Appendix 2 Observations for each variable in the SAS regression 1 England 2003K3 5,275 5,898 0,315-0,875-0,258 England 2003K4 5,240 5,904 0,317-0,881-0,258 England 2004K1 5,227 5,892 0,298-0,903-0,239 England 2004K2 5,227 5,909 0,298-0,904-0,239 England 2004K3 5,220 5,919 0,300-0,919-0,237 England 2004K4 5,202 5,910 0,314-0,942-0,243 England 2005K1 5,217 5,932 0,309-0,946-0,239 England 2005K2 5,269 5,973 0,317-0,957-0,242 England 2005K3 5,297 6,010 0,313-0,945-0,241 England 2005K4 5,328 6,032 0,307-0,949-0,236 England 2006K1 5,332 6,059 0,304-0,940-0,236 England 2006K2 5,305 6,029 0,304-0,959-0,232 England 2006K3 5,300 6,018 0,309-0,959-0,235 England 2006K4 5,325 6,035 0,320-0,962-0,244 England 2007K1 5,290 6,038 0,314-0,951-0,241 England 2007K2 5,279 6,046 0,309-0,980-0,232 England 2007K3 5,279 6,037 0,307-0,994-0,228 England 2007K4 5,318 6,055 0,314-0,991-0,233 England 2008K1 5,314 6,024 0,316-0,975-0,238 England 2008K2 5,310 6,018 0,338-0,967-0,257 England 2008K3 5,387 6,011 0,338-0,965-0,257 England 2008K4 5,471 6,075 0,357-0,908-0,284 England 2009K1 5,400 6,010 0,393-0,917-0,313 England 2009K2 5,438 6,024 0,396-0,924-0,315 England 2009K3 5,521 6,088 0,361-0,880-0,295 France 2003K2 5,437 5,801 0,377-0,692-0,377 France 2003K3 5,436 5,810 0,346-0,704-0,340 14 of 31
Appendix 2 Observations for each variable in the SAS regression 1 France 2003K4 5,433 5,838 0,337-0,706-0,332 France 2004K1 5,425 5,848 0,319-0,716-0,311 France 2004K2 5,438 5,846 0,316-0,727-0,304 France 2004K3 5,429 5,815 0,323-0,721-0,312 France 2004K4 5,388 5,805 0,332-0,710-0,325 France 2005K1 5,436 5,861 0,328-0,730-0,314 France 2005K2 5,487 5,915 0,313-0,727-0,302 France 2005K3 5,483 5,961 0,309-0,727-0,298 France 2005K4 5,478 6,007 0,295-0,755-0,276 France 2006K1 5,457 6,042 0,294-0,792-0,264 France 2006K2 5,444 6,008 0,283-0,830-0,245 France 2006K3 5,470 6,049 0,293-0,819-0,256 France 2006K4 5,439 6,051 0,289-0,829-0,249 France 2007K1 5,435 6,062 0,279-0,849-0,237 France 2007K2 5,449 6,100 0,274-0,858-0,231 France 2007K3 5,419 6,059 0,267-0,873-0,222 France 2007K4 5,360 6,045 0,282-0,873-0,234 France 2008K1 5,316 5,940 0,292-0,917-0,232 France 2008K2 5,345 5,927 0,332-0,892-0,268 France 2008K3 5,390 5,916 0,345-0,867-0,285 France 2008K4 5,412 5,849 0,370-0,840-0,314 France 2009K1 5,423 5,925 0,403-0,796-0,359 France 2009K2 5,447 5,965 0,368-0,820-0,318 France 2009K3 5,446 6,028 0,345-0,817-0,299 Germany 2003K2 4,928 6,074 0,510-1,485-0,340 Germany 2003K3 4,918 6,072 0,483-1,463-0,317 Germany 2003K4 4,916 6,096 0,478-1,467-0,312 15 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Germany 2004K1 4,912 6,096 0,454-1,471-0,291 Germany 2004K2 4,921 6,114 0,447-1,469-0,285 Germany 2004K3 4,925 6,096 0,441-1,473-0,280 Germany 2004K4 4,890 6,073 0,440-1,451-0,280 Germany 2005K1 4,942 6,109 0,427-1,453-0,269 Germany 2005K2 4,979 6,152 0,423-1,434-0,267 Germany 2005K3 4,980 6,170 0,417-1,436-0,263 Germany 2005K4 4,981 6,194 0,409-1,445-0,256 Germany 2006K1 4,992 6,213 0,406-1,465-0,252 Germany 2006K2 4,997 6,184 0,391-1,462-0,241 Germany 2006K3 5,009 6,213 0,399-1,435-0,248 Germany 2006K4 4,949 6,207 0,403-1,455-0,251 Germany 2007K1 4,958 6,219 0,390-1,496-0,238 Germany 2007K2 4,953 6,243 0,384-1,494-0,233 Germany 2007K3 4,935 6,226 0,373-1,515-0,224 Germany 2007K4 4,945 6,218 0,373-1,516-0,224 Germany 2008K1 4,951 6,142 0,373-1,499-0,225 Germany 2008K2 4,923 6,137 0,396-1,437-0,246 Germany 2008K3 4,996 6,156 0,408-1,467-0,253 Germany 2008K4 5,039 6,117 0,425-1,429-0,269 Germany 2009K1 5,007 6,071 0,453-1,375-0,297 Germany 2009K2 4,991 6,108 0,479-1,383-0,319 Germany 2009K3 5,004 6,118 0,457-1,414-0,297 US 2003K2 6,491 6,945 0,067-0,576-0,175 US 2003K3 6,499 6,961 0,060-0,612-0,158 US 2003K4 6,503 7,015 0,059-0,617-0,155 US 2004K1 6,508 7,017 0,055-0,653-0,141 16 of 31
Appendix 2 Observations for each variable in the SAS regression 1 US 2004K2 6,514 7,026 0,055-0,651-0,142 US 2004K3 6,521 7,013 0,055-0,653-0,141 US 2004K4 6,525 7,052 0,057-0,639-0,147 US 2005K1 6,532 7,048 0,055-0,665-0,138 US 2005K2 6,537 7,052 0,058-0,658-0,141 US 2005K3 6,545 7,071 0,060-0,657-0,143 US 2005K4 6,549 7,075 0,059-0,665-0,140 US 2006K1 6,562 7,147 0,061-0,667-0,141 US 2006K2 6,573 7,167 0,055-0,711-0,125 US 2006K3 6,582 7,182 0,056-0,717-0,124 US 2006K4 6,592 7,193 0,055-0,722-0,122 US 2007K1 6,607 7,206 0,056-0,723-0,122 US 2007K2 6,626 7,250 0,058-0,723-0,123 US 2007K3 6,633 7,256 0,058-0,743-0,118 US 2007K4 6,645 7,244 0,061-0,744-0,120 US 2008K1 6,659 7,202 0,066-0,726-0,127 US 2008K2 6,673 7,207 0,071-0,684-0,142 US 2008K3 6,683 7,164 0,072-0,678-0,144 US 2008K4 6,687 7,078 0,079-0,640-0,160 US 2009K1 6,700 7,031 0,088-0,579-0,188 US 2009K2 6,704 7,089 0,092-0,540-0,208 US 2009K3 6,710 7,150 0,084-0,573-0,188 Finland 2003K2 4,294 5,017 0,218-0,876-0,188 Finland 2003K3 4,273 5,014 0,210-0,901-0,178 Finland 2003K4 4,244 5,024 0,215-0,919-0,177 Finland 2004K1 4,210 5,058 0,212-0,951-0,170 Finland 2004K2 4,228 4,995 0,208-1,007-0,159 17 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Finland 2004K3 4,196 4,971 0,238-0,954-0,187 Finland 2004K4 4,145 4,966 0,252-0,969-0,194 Finland 2005K1 4,182 5,009 0,250-1,007-0,186 Finland 2005K2 4,247 5,087 0,238-1,005-0,178 Finland 2005K3 4,250 5,119 0,219-1,007-0,166 Finland 2005K4 4,261 5,145 0,207-1,024-0,156 Finland 2006K1 4,268 5,191 0,201-1,034-0,151 Finland 2006K2 4,284 5,135 0,184-1,060-0,138 Finland 2006K3 4,294 5,160 0,198-1,004-0,153 Finland 2006K4 4,256 5,164 0,200-1,019-0,153 Finland 2007K1 4,272 5,181 0,193-1,052-0,144 Finland 2007K2 4,283 5,231 0,191-1,052-0,143 Finland 2007K3 4,264 5,237 0,182-1,082-0,134 Finland 2007K4 4,220 5,205 0,181-1,104-0,131 Finland 2008K1 4,156 5,103 0,195-1,122-0,137 Finland 2008K2 4,149 5,046 0,232-1,109-0,161 Finland 2008K3 4,175 4,987 0,270-1,086-0,189 Finland 2008K4 4,161 4,888 0,329-1,047-0,235 Finland 2009K1 4,217 4,811 0,395-1,020-0,293 Finland 2009K2 4,259 4,916 0,421-0,930-0,336 Finland 2009K3 4,255 4,944 0,372-0,946-0,289 18 of 31
Appendix 2 Observations for each variable in the SAS regression 1 = Time dummies, where T = year and T Î [2004; 2009] Property example: D 2004 =1 if T = 2004 and D 2004 =0 if T 2004. Same logic goes for the other time dummies. = Country dummies, where C Î [Denmark, Spain, England, France, Germany, US, Finland] Property example: D Denmark =1 if C= Denmark and D Denmark =0 if C Denmark Same logic goes for the other country dummies. Short names for dummy variables 1 = D 2004 2 = D 2005 3 = D 2006 4 = D 2007 5 = D 2008 6 = D 2009 7 = D 2004 8 = D 2005 9 = D 2006 10 = D 2007 11 = D 2008 12 = D 2009 13 = D 2004 14 = D 2005 15 = D 2006 16 = D 2007 17 = D 2008 18 = D 2009 19= D 2004 20 = D 2005 21 = D 2006 22 = D 2007 23 = D 2008 24 = D 2009 25 = D Denmark 26 = D Spain 27 = D England 28 = D France 29 = D Germany 30 = D US 31 = D Finland 19 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Time 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Norway 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Norway 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Norway 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Norway 2004K1-1,0900 0 0 0 0 0-0,3640 0 0 0 0 0 0,48619 0 0 0 0 0 Norway 2004K2-1,1189 0 0 0 0 0-0,3543 0 0 0 0 0 0,48165 0 0 0 0 0 Norway 2004K3-1,1111 0 0 0 0 0-0,3595 0 0 0 0 0 0,48556 0 0 0 0 0 Norway 2004K4-1,1299 0 0 0 0 0-0,3563 0 0 0 0 0 0,48562 0 0 0 0 0 Norway 2005K1 0-1,1926 0 0 0 0 0-0,3303 0 0 0 0 0 0,46837 0 0 0 0 Norway 2005K2 0-1,2048 0 0 0 0 0-0,3197 0 0 0 0 0 0,45864 0 0 0 0 Norway 2005K3 0-1,2073 0 0 0 0 0-0,3106 0 0 0 0 0 0,44883 0 0 0 0 Norway 2005K4 0-1,2332 0 0 0 0 0-0,2886 0 0 0 0 0 0,42703 0 0 0 0 Norway 2006K1 0 0-1,2358 0 0 0 0 0-0,2880 0 0 0 0 0 0,4266 0 0 0 Norway 2006K2 0 0-1,2743 0 0 0 0 0-0,2733 0 0 0 0 0 0,4139 0 0 0 Norway 2006K3 0 0-1,2679 0 0 0 0 0-0,2830 0 0 0 0 0 0,4248 0 0 0 Norway 2006K4 0 0-1,2372 0 0 0 0 0-0,2939 0 0 0 0 0 0,4337 0 0 0 Norway 2007K1 0 0 0-1,2873 0 0 0 0 0-0,2571 0 0 0 0 0 0,3951 0 0 Norway 2007K2 0 0 0-1,2821 0 0 0 0 0-0,2644 0 0 0 0 0 0,4038 0 0 Norway 2007K3 0 0 0-1,2853 0 0 0 0 0-0,2642 0 0 0 0 0 0,4039 0 0 Norway 2007K4 0 0 0-1,2932 0 0 0 0 0-0,2707 0 0 0 0 0 0,4130 0 0 Norway 2008K1 0 0 0 0-1,3062 0 0 0 0 0-0,2798 0 0 0 0 0 0,4255 0 Norway 2008K2 0 0 0 0-1,2885 0 0 0 0 0-0,3178 0 0 0 0 0 0,4674 0 Norway 2008K3 0 0 0 0-1,3025 0 0 0 0 0-0,3099 0 0 0 0 0 0,4602 0 Norway 2008K4 0 0 0 0-1,2691 0 0 0 0 0-0,3617 0 0 0 0 0 0,5114 0 Norway 2009K1 0 0 0 0 0-1,2693 0 0 0 0 0-0,4039 0 0 0 0 0 0,5517 Norway 2009K2 0 0 0 0 0-1,2075 0 0 0 0 0-0,4166 0 0 0 0 0 0,5548 Norway 2009K3 0 0 0 0 0-1,1410 0 0 0 0 0-0,4064 0 0 0 0 0 0,5354 DK 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DK 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DK 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DK 2004K1-1,3155 0 0 0 0 0-0,2987 0 0 0 0 0 0,4490 0 0 0 0 0 DK 2004K2-1,3329 0 0 0 0 0-0,2817 0 0 0 0 0 0,4308 0 0 0 0 0 DK 2004K3-1,3081 0 0 0 0 0-0,2823 0 0 0 0 0 0,4288 0 0 0 0 0 DK 2004K4-1,3032 0 0 0 0 0-0,2781 0 0 0 0 0 0,4231 0 0 0 0 0 DK 2005K1 0-1,2670 0 0 0 0 0-0,2615 0 0 0 0 0 0,3983 0 0 0 0 20 of 31
Appendix 2 Observations for each variable in the SAS regression 1 DK 2005K2 0-1,3279 0 0 0 0 0-0,2441 0 0 0 0 0 0,3830 0 0 0 0 DK 2005K3 0-1,3365 0 0 0 0 0-0,2258 0 0 0 0 0 0,3593 0 0 0 0 DK 2005K4 0-1,3674 0 0 0 0 0-0,2135 0 0 0 0 0 0,3454 0 0 0 0 DK 2006K1 0 0-1,3899 0 0 0 0 0-0,2121 0 0 0 0 0 0,3457 0 0 0 DK 2006K2 0 0-1,4908 0 0 0 0 0-0,1942 0 0 0 0 0 0,3283 0 0 0 DK 2006K3 0 0-1,4251 0 0 0 0 0-0,2041 0 0 0 0 0 0,3374 0 0 0 DK 2006K4 0 0-1,4752 0 0 0 0 0-0,1849 0 0 0 0 0 0,3132 0 0 0 DK 2007K1 0 0 0-1,5130 0 0 0 0 0-0,1790 0 0 0 0 0 0,3070 0 0 DK 2007K2 0 0 0-1,5227 0 0 0 0 0-0,1821 0 0 0 0 0 0,3125 0 0 DK 2007K3 0 0 0-1,5751 0 0 0 0 0-0,1675 0 0 0 0 0 0,2934 0 0 DK 2007K4 0 0 0-1,6030 0 0 0 0 0-0,1731 0 0 0 0 0 0,3037 0 0 DK 2008K1 0 0 0 0-1,5749 0 0 0 0 0-0,1987 0 0 0 0 0 0,3406 0 DK 2008K2 0 0 0 0-1,5651 0 0 0 0 0-0,2165 0 0 0 0 0 0,3653 0 DK 2008K3 0 0 0 0-1,5848 0 0 0 0 0-0,2170 0 0 0 0 0 0,3673 0 DK 2008K4 0 0 0 0-1,5560 0 0 0 0 0-0,2478 0 0 0 0 0 0,4071 0 DK 2009K1 0 0 0 0 0-1,5107 0 0 0 0 0-0,3114 0 0 0 0 0 0,4809 DK 2009K2 0 0 0 0 0-1,4940 0 0 0 0 0-0,3350 0 0 0 0 0 0,5055 DK 2009K3 0 0 0 0 0-1,4807 0 0 0 0 0-0,2993 0 0 0 0 0 0,4650 Spain 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spain 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spain 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Spain 2004K1-2,1054 0 0 0 0 0-0,2710 0 0 0 0 0 0,4564 0 0 0 0 0 Spain 2004K2-2,0824 0 0 0 0 0-0,2701 0 0 0 0 0 0,4549 0 0 0 0 0 Spain 2004K3-2,1021 0 0 0 0 0-0,2762 0 0 0 0 0 0,4626 0 0 0 0 0 Spain 2004K4-2,1133 0 0 0 0 0-0,2796 0 0 0 0 0 0,4670 0 0 0 0 0 Spain 2005K1 0-2,1666 0 0 0 0 0-0,2705 0 0 0 0 0 0,4568 0 0 0 0 Spain 2005K2 0-2,1468 0 0 0 0 0-0,2700 0 0 0 0 0 0,4558 0 0 0 0 Spain 2005K3 0-2,1701 0 0 0 0 0-0,2671 0 0 0 0 0 0,4526 0 0 0 0 Spain 2005K4 0-2,2263 0 0 0 0 0-0,2556 0 0 0 0 0 0,4390 0 0 0 0 Spain 2006K1 0 0-2,2758 0 0 0 0 0-0,2647 0 0 0 0 0 0,4511 0 0 0 Spain 2006K2 0 0-2,2932 0 0 0 0 0-0,2614 0 0 0 0 0 0,4472 0 0 0 Spain 2006K3 0 0-2,3104 0 0 0 0 0-0,2718 0 0 0 0 0 0,4603 0 0 0 Spain 2006K4 0 0-2,2262 0 0 0 0 0-0,2717 0 0 0 0 0 0,4591 0 0 0 Spain 2007K1 0 0 0-2,2476 0 0 0 0 0-0,2689 0 0 0 0 0 0,4560 0 0 Spain 2007K2 0 0 0-2,2995 0 0 0 0 0-0,2675 0 0 0 0 0 0,4549 0 0 21 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Spain 2007K3 0 0 0-2,2999 0 0 0 0 0-0,2757 0 0 0 0 0 0,4650 0 0 Spain 2007K4 0 0 0-2,2591 0 0 0 0 0-0,2825 0 0 0 0 0 0,4727 0 0 Spain 2008K1 0 0 0 0-2,2553 0 0 0 0 0-0,2887 0 0 0 0 0 0,4800 0 Spain 2008K2 0 0 0 0-2,1538 0 0 0 0 0-0,3151 0 0 0 0 0 0,5089 0 Spain 2008K3 0 0 0 0-2,1862 0 0 0 0 0-0,3393 0 0 0 0 0 0,5357 0 Spain 2008K4 0 0 0 0-2,1224 0 0 0 0 0-0,3706 0 0 0 0 0 0,5665 0 Spain 2009K1 0 0 0 0 0-1,9881 0 0 0 0 0-0,3972 0 0 0 0 0 0,5891 Spain 2009K2 0 0 0 0 0-1,9211 0 0 0 0 0-0,4390 0 0 0 0 0 0,6241 Spain 2009K3 0 0 0 0 0-1,9768 0 0 0 0 0-0,4138 0 0 0 0 0 0,6038 England 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 England 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 England 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 England 2004K1-0,9025 0 0 0 0 0-0,2388 0 0 0 0 0 0,2978 0 0 0 0 0 England 2004K2-0,9037 0 0 0 0 0-0,2386 0 0 0 0 0 0,2979 0 0 0 0 0 England 2004K3-0,9189 0 0 0 0 0-0,2368 0 0 0 0 0 0,2997 0 0 0 0 0 England 2004K4-0,9420 0 0 0 0 0-0,2425 0 0 0 0 0 0,3136 0 0 0 0 0 England 2005K1 0-0,9461 0 0 0 0 0-0,2385 0 0 0 0 0 0,3094 0 0 0 0 England 2005K2 0-0,9571 0 0 0 0 0-0,2420 0 0 0 0 0 0,3169 0 0 0 0 England 2005K3 0-0,9448 0 0 0 0 0-0,2412 0 0 0 0 0 0,3126 0 0 0 0 England 2005K4 0-0,9493 0 0 0 0 0-0,2362 0 0 0 0 0 0,3071 0 0 0 0 England 2006K1 0 0-0,9399 0 0 0 0 0-0,2356 0 0 0 0 0 0,3038 0 0 0 England 2006K2 0 0-0,9590 0 0 0 0 0-0,2319 0 0 0 0 0 0,3038 0 0 0 England 2006K3 0 0-0,9594 0 0 0 0 0-0,2353 0 0 0 0 0 0,3086 0 0 0 England 2006K4 0 0-0,9615 0 0 0 0 0-0,2435 0 0 0 0 0 0,3199 0 0 0 England 2007K1 0 0 0-0,9512 0 0 0 0 0-0,2411 0 0 0 0 0 0,3141 0 0 England 2007K2 0 0 0-0,9801 0 0 0 0 0-0,2316 0 0 0 0 0 0,3087 0 0 England 2007K3 0 0 0-0,9943 0 0 0 0 0-0,2279 0 0 0 0 0 0,3070 0 0 England 2007K4 0 0 0-0,9913 0 0 0 0 0-0,2333 0 0 0 0 0 0,3136 0 0 England 2008K1 0 0 0 0-0,9746 0 0 0 0 0-0,2377 0 0 0 0 0 0,3155 0 England 2008K2 0 0 0 0-0,9665 0 0 0 0 0-0,2566 0 0 0 0 0 0,3381 0 England 2008K3 0 0 0 0-0,9645 0 0 0 0 0-0,2566 0 0 0 0 0 0,3376 0 England 2008K4 0 0 0 0-0,9082 0 0 0 0 0-0,2844 0 0 0 0 0 0,3569 0 England 2009K1 0 0 0 0 0-0,9170 0 0 0 0 0-0,3132 0 0 0 0 0 0,3928 England 2009K2 0 0 0 0 0-0,9244 0 0 0 0 0-0,3146 0 0 0 0 0 0,3964 England 2009K3 0 0 0 0 0-0,8803 0 0 0 0 0-0,2947 0 0 0 0 0 0,3609 22 of 31
Appendix 2 Observations for each variable in the SAS regression 1 France 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 France 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 France 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 France 2004K1-0,7156 0 0 0 0 0-0,3113 0 0 0 0 0 0,3192 0 0 0 0 0 France 2004K2-0,7268 0 0 0 0 0-0,3040 0 0 0 0 0 0,3158 0 0 0 0 0 France 2004K3-0,7211 0 0 0 0 0-0,3123 0 0 0 0 0 0,3227 0 0 0 0 0 France 2004K4-0,7103 0 0 0 0 0-0,3249 0 0 0 0 0 0,3318 0 0 0 0 0 France 2005K1 0-0,7305 0 0 0 0 0-0,3136 0 0 0 0 0 0,3283 0 0 0 0 France 2005K2 0-0,7266 0 0 0 0 0-0,3016 0 0 0 0 0 0,3130 0 0 0 0 France 2005K3 0-0,7265 0 0 0 0 0-0,2984 0 0 0 0 0 0,3093 0 0 0 0 France 2005K4 0-0,7547 0 0 0 0 0-0,2762 0 0 0 0 0 0,2947 0 0 0 0 France 2006K1 0 0-0,7923 0 0 0 0 0-0,2640 0 0 0 0 0 0,2942 0 0 0 France 2006K2 0 0-0,8297 0 0 0 0 0-0,2445 0 0 0 0 0 0,2825 0 0 0 France 2006K3 0 0-0,8192 0 0 0 0 0-0,2557 0 0 0 0 0 0,2933 0 0 0 France 2006K4 0 0-0,8292 0 0 0 0 0-0,2494 0 0 0 0 0 0,2887 0 0 0 France 2007K1 0 0 0-0,8490 0 0 0 0 0-0,2371 0 0 0 0 0 0,2791 0 0 France 2007K2 0 0 0-0,8582 0 0 0 0 0-0,2313 0 0 0 0 0 0,2743 0 0 France 2007K3 0 0 0-0,8734 0 0 0 0 0-0,2224 0 0 0 0 0 0,2669 0 0 France 2007K4 0 0 0-0,8732 0 0 0 0 0-0,2336 0 0 0 0 0 0,2821 0 0 France 2008K1 0 0 0 0-0,9166 0 0 0 0 0-0,2317 0 0 0 0 0 0,2923 0 France 2008K2 0 0 0 0-0,8917 0 0 0 0 0-0,2678 0 0 0 0 0 0,3319 0 France 2008K3 0 0 0 0-0,8668 0 0 0 0 0-0,2845 0 0 0 0 0 0,3448 0 France 2008K4 0 0 0 0-0,8400 0 0 0 0 0-0,3138 0 0 0 0 0 0,3699 0 France 2009K1 0 0 0 0 0-0,7961 0 0 0 0 0-0,3590 0 0 0 0 0 0,4025 France 2009K2 0 0 0 0 0-0,8204 0 0 0 0 0-0,3184 0 0 0 0 0 0,3683 France 2009K3 0 0 0 0 0-0,8170 0 0 0 0 0-0,2990 0 0 0 0 0 0,3453 Germany 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Germany 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Germany 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Germany 2004K1-1,4709 0 0 0 0 0-0,2908 0 0 0 0 0 0,4542 0 0 0 0 0 Germany 2004K2-1,4691 0 0 0 0 0-0,2849 0 0 0 0 0 0,4471 0 0 0 0 0 Germany 2004K3-1,4728 0 0 0 0 0-0,2797 0 0 0 0 0 0,4412 0 0 0 0 0 Germany 2004K4-1,4506 0 0 0 0 0-0,2799 0 0 0 0 0 0,4396 0 0 0 0 0 Germany 2005K1 0-1,4529 0 0 0 0 0-0,2692 0 0 0 0 0 0,4267 0 0 0 0 Germany 2005K2 0-1,4343 0 0 0 0 0-0,2673 0 0 0 0 0 0,4229 0 0 0 0 23 of 31
Appendix 2 Observations for each variable in the SAS regression 1 Germany 2005K3 0-1,4358 0 0 0 0 0-0,2626 0 0 0 0 0 0,4171 0 0 0 0 Germany 2005K4 0-1,4453 0 0 0 0 0-0,2557 0 0 0 0 0 0,4091 0 0 0 0 Germany 2006K1 0 0-1,4647 0 0 0 0 0-0,2520 0 0 0 0 0 0,4059 0 0 0 Germany 2006K2 0 0-1,4617 0 0 0 0 0-0,2409 0 0 0 0 0 0,3912 0 0 0 Germany 2006K3 0 0-1,4353 0 0 0 0 0-0,2484 0 0 0 0 0 0,3988 0 0 0 Germany 2006K4 0 0-1,4546 0 0 0 0 0-0,2505 0 0 0 0 0 0,4032 0 0 0 Germany 2007K1 0 0 0-1,4961 0 0 0 0 0-0,2383 0 0 0 0 0 0,3903 0 0 Germany 2007K2 0 0 0-1,4939 0 0 0 0 0-0,2333 0 0 0 0 0 0,3836 0 0 Germany 2007K3 0 0 0-1,5146 0 0 0 0 0-0,2243 0 0 0 0 0 0,3728 0 0 Germany 2007K4 0 0 0-1,5159 0 0 0 0 0-0,2245 0 0 0 0 0 0,3731 0 0 Germany 2008K1 0 0 0 0-1,4986 0 0 0 0 0-0,2251 0 0 0 0 0 0,3728 0 Germany 2008K2 0 0 0 0-1,4372 0 0 0 0 0-0,2462 0 0 0 0 0 0,3961 0 Germany 2008K3 0 0 0 0-1,4671 0 0 0 0 0-0,2532 0 0 0 0 0 0,4077 0 Germany 2008K4 0 0 0 0-1,4290 0 0 0 0 0-0,2690 0 0 0 0 0 0,4245 0 Germany 2009K1 0 0 0 0 0-1,3754 0 0 0 0 0-0,2971 0 0 0 0 0 0,4533 Germany 2009K2 0 0 0 0 0-1,3826 0 0 0 0 0-0,3193 0 0 0 0 0 0,4792 Germany 2009K3 0 0 0 0 0-1,4141 0 0 0 0 0-0,2972 0 0 0 0 0 0,4570 US 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 US 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 US 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 US 2004K1-0,6529 0 0 0 0 0-0,1410 0 0 0 0 0 0,0548 0 0 0 0 0 US 2004K2-0,6510 0 0 0 0 0-0,1415 0 0 0 0 0 0,0547 0 0 0 0 0 US 2004K3-0,6532 0 0 0 0 0-0,1412 0 0 0 0 0 0,0553 0 0 0 0 0 US 2004K4-0,6390 0 0 0 0 0-0,1467 0 0 0 0 0 0,0571 0 0 0 0 0 US 2005K1 0-0,6652 0 0 0 0 0-0,1376 0 0 0 0 0 0,0553 0 0 0 0 US 2005K2 0-0,6576 0 0 0 0 0-0,1413 0 0 0 0 0 0,0578 0 0 0 0 US 2005K3 0-0,6574 0 0 0 0 0-0,1426 0 0 0 0 0 0,0599 0 0 0 0 US 2005K4 0-0,6652 0 0 0 0 0-0,1396 0 0 0 0 0 0,0588 0 0 0 0 US 2006K1 0 0-0,6669 0 0 0 0 0-0,1406 0 0 0 0 0 0,0613 0 0 0 US 2006K2 0 0-0,7105 0 0 0 0 0-0,1250 0 0 0 0 0 0,0553 0 0 0 US 2006K3 0 0-0,7171 0 0 0 0 0-0,1239 0 0 0 0 0 0,0565 0 0 0 US 2006K4 0 0-0,7220 0 0 0 0 0-0,1217 0 0 0 0 0 0,0548 0 0 0 US 2007K1 0 0 0-0,7230 0 0 0 0 0-0,1223 0 0 0 0 0 0,0562 0 0 US 2007K2 0 0 0-0,7228 0 0 0 0 0-0,1234 0 0 0 0 0 0,0580 0 0 US 2007K3 0 0 0-0,7427 0 0 0 0 0-0,1183 0 0 0 0 0 0,0576 0 0 24 of 31
Appendix 2 Observations for each variable in the SAS regression 1 US 2007K4 0 0 0-0,7436 0 0 0 0 0-0,1202 0 0 0 0 0 0,0613 0 0 US 2008K1 0 0 0 0-0,7257 0 0 0 0 0-0,1273 0 0 0 0 0 0,0659 0 US 2008K2 0 0 0 0-0,6845 0 0 0 0 0-0,1416 0 0 0 0 0 0,0715 0 US 2008K3 0 0 0 0-0,6782 0 0 0 0 0-0,1437 0 0 0 0 0 0,0720 0 US 2008K4 0 0 0 0-0,6404 0 0 0 0 0-0,1596 0 0 0 0 0 0,0786 0 US 2009K1 0 0 0 0 0-0,5792 0 0 0 0 0-0,1883 0 0 0 0 0 0,0883 US 2009K2 0 0 0 0 0-0,5396 0 0 0 0 0-0,2083 0 0 0 0 0 0,0924 US 2009K3 0 0 0 0 0-0,5729 0 0 0 0 0-0,1877 0 0 0 0 0 0,0836 Finland 2003K2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finland 2003K3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finland 2003K4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Finland 2004K1-0,9507 0 0 0 0 0-0,1701 0 0 0 0 0 0,2121 0 0 0 0 0 Finland 2004K2-1,0072 0 0 0 0 0-0,1588 0 0 0 0 0 0,2078 0 0 0 0 0 Finland 2004K3-0,9537 0 0 0 0 0-0,1867 0 0 0 0 0 0,2382 0 0 0 0 0 Finland 2004K4-0,9693 0 0 0 0 0-0,1936 0 0 0 0 0 0,2524 0 0 0 0 0 Finland 2005K1 0-1,0071 0 0 0 0 0-0,1858 0 0 0 0 0 0,2497 0 0 0 0 Finland 2005K2 0-1,0054 0 0 0 0 0-0,1783 0 0 0 0 0 0,2380 0 0 0 0 Finland 2005K3 0-1,0068 0 0 0 0 0-0,1660 0 0 0 0 0 0,2193 0 0 0 0 Finland 2005K4 0-1,0242 0 0 0 0 0-0,1556 0 0 0 0 0 0,2065 0 0 0 0 Finland 2006K1 0 0-1,0343 0 0 0 0 0-0,1511 0 0 0 0 0 0,2015 0 0 0 Finland 2006K2 0 0-1,0604 0 0 0 0 0-0,1376 0 0 0 0 0 0,1845 0 0 0 Finland 2006K3 0 0-1,0041 0 0 0 0 0-0,1533 0 0 0 0 0 0,1984 0 0 0 Finland 2006K4 0 0-1,0186 0 0 0 0 0-0,1526 0 0 0 0 0 0,2004 0 0 0 Finland 2007K1 0 0 0-1,0518 0 0 0 0 0-0,1437 0 0 0 0 0 0,1930 0 0 Finland 2007K2 0 0 0-1,0518 0 0 0 0 0-0,1427 0 0 0 0 0 0,1913 0 0 Finland 2007K3 0 0 0-1,0816 0 0 0 0 0-0,1337 0 0 0 0 0 0,1820 0 0 Finland 2007K4 0 0 0-1,1041 0 0 0 0 0-0,1307 0 0 0 0 0 0,1811 0 0 Finland 2008K1 0 0 0 0-1,1215 0 0 0 0 0-0,1369 0 0 0 0 0 0,1948 0 Finland 2008K2 0 0 0 0-1,1086 0 0 0 0 0-0,1613 0 0 0 0 0 0,2324 0 Finland 2008K3 0 0 0 0-1,0855 0 0 0 0 0-0,1888 0 0 0 0 0 0,2705 0 Finland 2008K4 0 0 0 0-1,0471 0 0 0 0 0-0,2353 0 0 0 0 0 0,3286 0 Finland 2009K1 0 0 0 0 0-1,0197 0 0 0 0 0-0,2931 0 0 0 0 0 0,3952 Finland 2009K2 0 0 0 0 0-0,9300 0 0 0 0 0-0,3358 0 0 0 0 0 0,4210 Finland 2009K3 0 0 0 0 0-0,9456 0 0 0 0 0-0,2887 0 0 0 0 0 0,3723 25 of 31
Appendix 2 Observations for each variable in the SAS regression 1 19 20 21 22 23 24 25 26 27 28 29 30 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0,4862 0 0 0 0 0 0 0 0 0 0 0 0 0,4816 0 0 0 0 0 0 0 0 0 0 0 0 0,4856 0 0 0 0 0 0 0 0 0 0 0 0 0,4856 0 0 0 0 0 0 0 0 0 0 0 0 0 0,4684 0 0 0 0 0 0 0 0 0 0 0 0 0,4586 0 0 0 0 0 0 0 0 0 0 0 0 0,4488 0 0 0 0 0 0 0 0 0 0 0 0 0,4270 0 0 0 0 0 0 0 0 0 0 0 0 0 0,4266 0 0 0 0 0 0 0 0 0 0 0 0 0,4139 0 0 0 0 0 0 0 0 0 0 0 0 0,4248 0 0 0 0 0 0 0 0 0 0 0 0 0,4337 0 0 0 0 0 0 0 0 0 0 0 0 0 0,3951 0 0 0 0 0 0 0 0 0 0 0 0 0,4038 0 0 0 0 0 0 0 0 0 0 0 0 0,4039 0 0 0 0 0 0 0 0 0 0 0 0 0,4130 0 0 0 0 0 0 0 0 0 0 0 0 0 0,4255 0 0 0 0 0 0 0 0 0 0 0 0 0,4674 0 0 0 0 0 0 0 0 0 0 0 0 0,4602 0 0 0 0 0 0 0 0 0 0 0 0 0,5114 0 0 0 0 0 0 0 0 0 0 0 0 0 0,5517 0 0 0 0 0 0 0 0 0 0 0 0 0,5548 0 0 0 0 0 0 0 0 0 0 0 0 0,5354 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0,0370 0 0 0 0 0 1 0 0 0 0 0 0 0,0356 0 0 0 0 0 1 0 0 0 0 0 0 0,0381 0 0 0 0 0 1 0 0 0 0 0 0 0,0380 0 0 0 0 0 1 0 0 0 0 0 0 0 0,0414 0 0 0 0 1 0 0 0 0 0 0 0 0,0356 0 0 0 0 1 0 0 0 0 0 0 0 0,0349 0 0 0 0 1 0 0 0 0 0 0 26 of 31
Appendix 2 Observations for each variable in the SAS regression 1 0 0,0324 0 0 0 0 1 0 0 0 0 0 0 0 0 0,0312 0 0 0 1 0 0 0 0 0 0 0 0 0,0249 0 0 0 1 0 0 0 0 0 0 0 0 0,0289 0 0 0 1 0 0 0 0 0 0 0 0 0,0256 0 0 0 1 0 0 0 0 0 0 0 0 0 0,0234 0 0 1 0 0 0 0 0 0 0 0 0 0,0228 0 0 1 0 0 0 0 0 0 0 0 0 0,0200 0 0 1 0 0 0 0 0 0 0 0 0 0,0187 0 0 1 0 0 0 0 0 0 0 0 0 0 0,0202 0 1 0 0 0 0 0 0 0 0 0 0 0,0209 0 1 0 0 0 0 0 0 0 0 0 0 0,0202 0 1 0 0 0 0 0 0 0 0 0 0 0,0220 0 1 0 0 0 0 0 0 0 0 0 0 0 0,0253 1 0 0 0 0 0 0 0 0 0 0 0 0,0269 1 0 0 0 0 0 0 0 0 0 0 0 0,0277 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0,4564 0 0 0 0 0 0 1 0 0 0 0 0 0,4549 0 0 0 0 0 0 1 0 0 0 0 0 0,4626 0 0 0 0 0 0 1 0 0 0 0 0 0,4670 0 0 0 0 0 0 1 0 0 0 0 0 0 0,4568 0 0 0 0 0 1 0 0 0 0 0 0 0,4558 0 0 0 0 0 1 0 0 0 0 0 0 0,4526 0 0 0 0 0 1 0 0 0 0 0 0 0,4390 0 0 0 0 0 1 0 0 0 0 0 0 0 0,4511 0 0 0 0 1 0 0 0 0 0 0 0 0,4472 0 0 0 0 1 0 0 0 0 0 0 0 0,4603 0 0 0 0 1 0 0 0 0 0 0 0 0,4591 0 0 0 0 1 0 0 0 0 0 0 0 0 0,4560 0 0 0 1 0 0 0 0 0 0 0 0 0,4549 0 0 0 1 0 0 0 0 0 0 0 0 0,4650 0 0 0 1 0 0 0 0 0 0 0 0 0,4727 0 0 0 1 0 0 0 0 0 0 0 0 0 0,4800 0 0 1 0 0 0 0 0 27 of 31
Appendix 2 Observations for each variable in the SAS regression 1 0 0 0 0 0,5089 0 0 1 0 0 0 0 0 0 0 0 0 0,5357 0 0 1 0 0 0 0 0 0 0 0 0 0,5665 0 0 1 0 0 0 0 0 0 0 0 0 0 0,5891 0 1 0 0 0 0 0 0 0 0 0 0 0,6241 0 1 0 0 0 0 0 0 0 0 0 0 0,6038 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0,2978 0 0 0 0 0 0 0 1 0 0 0 0 0,2979 0 0 0 0 0 0 0 1 0 0 0 0 0,2997 0 0 0 0 0 0 0 1 0 0 0 0 0,3136 0 0 0 0 0 0 0 1 0 0 0 0 0 0,3094 0 0 0 0 0 0 1 0 0 0 0 0 0,3169 0 0 0 0 0 0 1 0 0 0 0 0 0,3126 0 0 0 0 0 0 1 0 0 0 0 0 0,3071 0 0 0 0 0 0 1 0 0 0 0 0 0 0,3038 0 0 0 0 0 1 0 0 0 0 0 0 0,3038 0 0 0 0 0 1 0 0 0 0 0 0 0,3086 0 0 0 0 0 1 0 0 0 0 0 0 0,3199 0 0 0 0 0 1 0 0 0 0 0 0 0 0,3141 0 0 0 0 1 0 0 0 0 0 0 0 0,3087 0 0 0 0 1 0 0 0 0 0 0 0 0,3070 0 0 0 0 1 0 0 0 0 0 0 0 0,3136 0 0 0 0 1 0 0 0 0 0 0 0 0 0,3155 0 0 0 1 0 0 0 0 0 0 0 0 0,3381 0 0 0 1 0 0 0 0 0 0 0 0 0,3376 0 0 0 1 0 0 0 0 0 0 0 0 0,3569 0 0 0 1 0 0 0 0 0 0 0 0 0 0,3928 0 0 1 0 0 0 0 0 0 0 0 0 0,3964 0 0 1 0 0 0 0 0 0 0 0 0 0,3609 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0,3192 0 0 0 0 0 0 0 0 1 0 0 0 0,3158 0 0 0 0 0 0 0 0 1 0 0 0 28 of 31
Appendix 2 Observations for each variable in the SAS regression 1 0,3227 0 0 0 0 0 0 0 0 1 0 0 0 0,3318 0 0 0 0 0 0 0 0 1 0 0 0 0 0,3283 0 0 0 0 0 0 0 1 0 0 0 0 0,3130 0 0 0 0 0 0 0 1 0 0 0 0 0,3093 0 0 0 0 0 0 0 1 0 0 0 0 0,2947 0 0 0 0 0 0 0 1 0 0 0 0 0 0,2942 0 0 0 0 0 0 1 0 0 0 0 0 0,2825 0 0 0 0 0 0 1 0 0 0 0 0 0,2933 0 0 0 0 0 0 1 0 0 0 0 0 0,2887 0 0 0 0 0 0 1 0 0 0 0 0 0 0,2791 0 0 0 0 0 1 0 0 0 0 0 0 0,2743 0 0 0 0 0 1 0 0 0 0 0 0 0,2669 0 0 0 0 0 1 0 0 0 0 0 0 0,2821 0 0 0 0 0 1 0 0 0 0 0 0 0 0,2923 0 0 0 0 1 0 0 0 0 0 0 0 0,3319 0 0 0 0 1 0 0 0 0 0 0 0 0,3448 0 0 0 0 1 0 0 0 0 0 0 0 0,3699 0 0 0 0 1 0 0 0 0 0 0 0 0 0,4025 0 0 0 1 0 0 0 0 0 0 0 0 0,3683 0 0 0 1 0 0 0 0 0 0 0 0 0,3453 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 571,4409 0 0 0 0 0 0 0 0 0 1 0 0 565,6148 0 0 0 0 0 0 0 0 0 1 0 0 557,6468 0 0 0 0 0 0 0 0 0 1 0 0 558,1743 0 0 0 0 0 0 0 0 0 1 0 0 0 541,8109 0 0 0 0 0 0 0 0 1 0 0 0 536,5906 0 0 0 0 0 0 0 0 1 0 0 0 527,6571 0 0 0 0 0 0 0 0 1 0 0 0 515,3191 0 0 0 0 0 0 0 0 1 0 0 0 0 507,1695 0 0 0 0 0 0 0 1 0 0 0 0 484,4725 0 0 0 0 0 0 0 1 0 0 0 0 495,9191 0 0 0 0 0 0 0 1 0 0 0 0 500,4528 0 0 0 0 0 0 0 1 0 0 29 of 31
Appendix 2 Observations for each variable in the SAS regression 1 0 0 0 482,9060 0 0 0 0 0 0 1 0 0 0 0 0 473,1586 0 0 0 0 0 0 1 0 0 0 0 0 456,4717 0 0 0 0 0 0 1 0 0 0 0 0 457,9256 0 0 0 0 0 0 1 0 0 0 0 0 0 458,5841 0 0 0 0 0 1 0 0 0 0 0 0 497,6030 0 0 0 0 0 1 0 0 0 0 0 0 512,9459 0 0 0 0 0 1 0 0 0 0 0 0 538,5391 0 0 0 0 0 1 0 0 0 0 0 0 0 581,6532 0 0 0 0 1 0 0 0 0 0 0 0 619,6698 0 0 0 0 1 0 0 0 0 0 0 0 583,3827 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 37,2119 0 0 0 0 0 0 0 0 0 0 1 0 38,4534 0 0 0 0 0 0 0 0 0 0 1 0 38,8301 0 0 0 0 0 0 0 0 0 0 1 0 40,8007 0 0 0 0 0 0 0 0 0 0 1 0 0 38,9864 0 0 0 0 0 0 0 0 0 1 0 0 40,2068 0 0 0 0 0 0 0 0 0 1 0 0 43,7840 0 0 0 0 0 0 0 0 0 1 0 0 46,0357 0 0 0 0 0 0 0 0 0 1 0 0 0 51,3782 0 0 0 0 0 0 0 0 1 0 0 0 47,2735 0 0 0 0 0 0 0 0 1 0 0 0 46,8777 0 0 0 0 0 0 0 0 1 0 0 0 47,0948 0 0 0 0 0 0 0 0 1 0 0 0 0 47,4794 0 0 0 0 0 0 0 1 0 0 0 0 46,5319 0 0 0 0 0 0 0 1 0 0 0 0 44,4098 0 0 0 0 0 0 0 1 0 0 0 0 45,5500 0 0 0 0 0 0 0 1 0 0 0 0 0 47,2140 0 0 0 0 0 0 1 0 0 0 0 0 51,5337 0 0 0 0 0 0 1 0 0 0 0 0 51,8666 0 0 0 0 0 0 1 0 0 0 0 0 56,2884 0 0 0 0 0 0 1 0 0 0 0 0 0 65,0129 0 0 0 0 0 1 0 0 0 0 0 0 68,8342 0 0 0 0 0 1 0 30 of 31
Appendix 2 Observations for each variable in the SAS regression 1 0 0 0 0 0 62,0949 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0,2121 0 0 0 0 0 0 0 0 0 0 0 1 0,2078 0 0 0 0 0 0 0 0 0 0 0 1 0,2382 0 0 0 0 0 0 0 0 0 0 0 1 0,2524 0 0 0 0 0 0 0 0 0 0 0 1 0 0,2497 0 0 0 0 0 0 0 0 0 0 1 0 0,2380 0 0 0 0 0 0 0 0 0 0 1 0 0,2193 0 0 0 0 0 0 0 0 0 0 1 0 0,2065 0 0 0 0 0 0 0 0 0 0 1 0 0 0,2015 0 0 0 0 0 0 0 0 0 1 0 0 0,1845 0 0 0 0 0 0 0 0 0 1 0 0 0,1984 0 0 0 0 0 0 0 0 0 1 0 0 0,2004 0 0 0 0 0 0 0 0 0 1 0 0 0 0,1930 0 0 0 0 0 0 0 0 1 0 0 0 0,1913 0 0 0 0 0 0 0 0 1 0 0 0 0,1820 0 0 0 0 0 0 0 0 1 0 0 0 0,1811 0 0 0 0 0 0 0 0 1 0 0 0 0 0,1948 0 0 0 0 0 0 0 1 0 0 0 0 0,2324 0 0 0 0 0 0 0 1 0 0 0 0 0,2705 0 0 0 0 0 0 0 1 0 0 0 0 0,3286 0 0 0 0 0 0 0 1 0 0 0 0 0 0,3952 0 0 0 0 0 0 1 0 0 0 0 0 0,4210 0 0 0 0 0 0 1 0 0 0 0 0 0,3723 0 0 0 0 0 0 1 31 of 31
Appendix 3 Distribution of observations for bond level, equity level and bank loan level The appendix illustrates the observations for bond level, equity level and bank loan level, in the natural scale and in a logged scale. It is evident that observations for bond level and equity level becomes more normal distributed when they are logged. This is not the case for observations of bank loan level. 1 of 3
Appendix 3 Distribution of observations for bond level, equity level and bank loan level 2 of 3
Appendix 3 Distribution of observations for bond level, equity level and bank loan level 3 of 3
Appendix 4 Regression output for bond level, equity level and bank loan level First the regression output for bond level is presented. Then follows the output for equity and finally for bank loans. For each type of finance three regressions are presented, all with the level as the dependent variable: (i) Regression I.i: The first results show all variables included in 1 regression. This regression is only used to get a picture of how the estimators would be if they were extracted for the overall regression. None of these estimators are used. (ii) Regression I.ii: The second regression includes all variables except CCTs and CTTs. Coefficients for the time and country dummies as well as the intercept and Bond level to total finance are extracted from regression 2. (iii)regression I.iii: The third regression includes the variable on capital structure and the cross-time term. The coefficients of CTTs are extracted. For each sub-regression the distribution of the residuals is presented, because normal distributed residuals is a precondition for BLUE OLS estimators. In addition a correlation matrix between all variables is presented for each type of finance. All regressions use: Number of Observations Read: 208 Number of Observations Used: 207 Number of Observations with Missing Values: 1 1 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level 4.1 Bonds Correlation of Estimates Variable Intercept Lag1(Log(B/TL)) Denmark Spain England France Germany US Finland 2004 2005 2006 2007 2008 2009 Intercept 1.0000 0.9869 0.6730 0.8996-0.8044-0.8762 0.7002-0.9013-0.7210-0.2462-0.2392-0.2191-0.1313-0.0891-0.1322 Lag1(Log(B/TL)) 0.9869 1.0000 0.7268 0.9246-0.7741-0.8579 0.7523-0.8895-0.6809-0.2134-0.2075-0.1872-0.0989-0.0573-0.1025 Denmark 0.6730 0.7268 1.0000 0.8159-0.4587-0.5743 0.8007-0.6277-0.3427 0.0309 0.0285 0.0500 0.1238 0.1571 0.1138 Spain 0.8996 0.9246 0.8159 1.0000-0.7555-0.8561 0.8355-0.8975-0.6455 0.0519 0.0568 0.0776 0.1634 0.2016 0.1416 England -0.8044-0.7741-0.4587-0.7555 1.0000 0.8663-0.4846 0.8705 0.7892-0.0521-0.0610-0.0801-0.1532-0.1894-0.1383 France -0.8762-0.8579-0.5743-0.8561 0.8663 1.0000-0.6006 0.9283 0.7998-0.0564-0.0653-0.0815-0.1612-0.1982-0.1441 Germany 0.7002 0.7523 0.8007 0.8355-0.4846-0.6006 1.0000-0.6531-0.3683 0.0334 0.0287 0.0422 0.1137 0.1427 0.0990 US -0.9013-0.8895-0.6277-0.8975 0.8705 0.9283-0.6531 1.0000 0.7953-0.0560-0.0645-0.0834-0.1668-0.2082-0.1544 Finland -0.7210-0.6809-0.3427-0.6455 0.7892 0.7998-0.3683 0.7953 1.0000-0.0463-0.0549-0.0715-0.1344-0.1630-0.1241 2004-0.2462-0.2134 0.0309 0.0519-0.0521-0.0564 0.0334-0.0560-0.0463 1.0000 0.6555 0.6539 0.6430 0.6347 0.6061 2005-0.2392-0.2075 0.0285 0.0568-0.0610-0.0653 0.0287-0.0645-0.0549 0.6555 1.0000 0.6539 0.6434 0.6354 0.6063 2006-0.2191-0.1872 0.0500 0.0776-0.0801-0.0815 0.0422-0.0834-0.0715 0.6539 0.6539 1.0000 0.6447 0.6377 0.6074 2007-0.1313-0.0989 0.1238 0.1634-0.1532-0.1612 0.1137-0.1668-0.1344 0.6430 0.6434 0.6447 1.0000 0.6413 0.6066 2008-0.0891-0.0573 0.1571 0.2016-0.1894-0.1982 0.1427-0.2082-0.1630 0.6347 0.6354 0.6377 0.6413 1.0000 0.6038 2009-0.1322-0.1025 0.1138 0.1416-0.1383-0.1441 0.0990-0.1544-0.1241 0.6061 0.6063 0.6074 0.6066 0.6038 1.0000 2004*Lag1(LOG(B/TL)) -0.2852-0.2899-0.0087-0.0041 0.0016 0.0011-0.0080 0.0026 0.0029 0.9328 0.6161 0.6135 0.5983 0.5882 0.5644 2005*Lag1(LOG(B/TL)) -0.3359-0.3424-0.0578-0.0585 0.0423 0.0471-0.0611 0.0511 0.0378 0.6181 0.9276 0.6134 0.5933 0.5811 0.5602 2006*Lag1(LOG(B/TL)) -0.4035-0.4107-0.1075-0.1286 0.0996 0.1155-0.1212 0.1199 0.0884 0.6168 0.6154 0.9126 0.5842 0.5692 0.5523 2007*Lag1(LOG(B/TL)) -0.3847-0.3915-0.0858-0.1090 0.0818 0.0972-0.1035 0.1000 0.0741 0.6177 0.6163 0.6120 0.8977 0.5736 0.5557 2008*Lag1(LOG(B/TL)) -0.3095-0.3159-0.0229-0.0331 0.0136 0.0249-0.0442 0.0221 0.0174 0.6169 0.6161 0.6135 0.5963 0.9090 0.5635 2009*Lag1(LOG(B/TL)) -0.1754-0.1803 0.0733 0.0841-0.0845-0.0860 0.0556-0.0954-0.0751 0.5745 0.5743 0.5744 0.5691 0.5643 0.9367 2 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Correlation of Estimates Variable 2004*Lag1(LOG(B/TL)) 2005*Lag1(LOG(B/TL)) 2006*Lag1(LOG(B/TL)) 2007*Lag1(LOG(B/TL)) 2008*Lag1(LOG(B/TL)) 2009*Lag1(LOG(B/TL)) Intercept 2006 2007 2008 2009-0.3095-0.1754 Lag1(Log(B/TL)) -0.2899-0.3424-0.4107-0.3915-0.3159-0.1803 Denmark -0.0087-0.0578-0.1075-0.0858-0.0229 0.0733 Spain -0.0041-0.0585-0.1286-0.1090-0.0331 0.0841 England 0.0016 0.0423 0.0996 0.0818 0.0136-0.0845 France 0.0011 0.0471 0.1155 0.0972 0.0249-0.0860 Germany -0.0080-0.0611-0.1212-0.1035-0.0442 0.0556 US 0.0026 0.0511 0.1199 0.1000 0.0221-0.0954 Finland 0.0029 0.0378 0.0884 0.0741 0.0174-0.0751 2004 0.9328 0.6181 0.6168 0.6177 0.6169 0.5745 2005 0.6161 0.9276 0.6154 0.6163 0.6161 0.5743 2006 0.6135 0.6134 0.9126 0.6120 0.6135 0.5744 2007 0.5983 0.5933 0.5842 0.8977 0.5963 0.5691 2008 0.5882 0.5811 0.5692 0.5736 0.9090 0.5643 2009 0.5644 0.5602 0.5523 0.5557 0.5635 0.9367 2004*Lag1(LOG(B/TL)) 1.0000 0.6722 0.6753 0.6750 0.6692 0.6157 2005*Lag1(LOG(B/TL)) 0.6722 1.0000 0.6870 0.6855 0.6754 0.6149 2006*Lag1(LOG(B/TL)) 0.6753 0.6870 1.0000 0.6966 0.6810 0.6116 2007*Lag1(LOG(B/TL)) 0.6750 0.6855 0.6966 1.0000 0.6805 0.6138 2008*Lag1(LOG(B/TL)) 0.6692 0.6754 0.6810 0.6805 1.0000 0.6165 2009*Lag1(LOG(B/TL)) 0.6157 0.6149 0.6116 0.6138 0.6165 1.0000 3 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 20 118.98314 5.94916 1993.91 <.0001 Error 180 0.53706 0.00298 Corrected Total 200 119.52020 Root MSE 0.05462 R-Square 0.9955 Dependent Mean 4.95218 Adj R-Sq 0.9950 Coeff Var 1.10301 Variable D F Paramete r Estimate Parameter Estimates Standar d Error t Value Pr > t Squared Partial Corr Type I Squared Partial Corr Type II Intercept 1 5.03955 0.10098 49.90 <.0001.. Lag1(Log(B/TL)) 1 0.41956 0.08857 4.74 <.0001 0.50228 0.11084 Denmark 1-0.17698 0.02400-7.37 <.0001 0.04641 0.23195 Spain 1-0.16262 0.07889-2.06 0.0407 0.08049 0.02306 England 1 0.57541 0.02711 21.23 <.0001 0.00001903 0.71456 France 1 0.63564 0.03741 16.99 <.0001 0.01284 0.61594 Germany 1 0.42249 0.02512 16.82 <.0001 0.33915 0.61107 US 1 1.75512 0.04741 37.02 <.0001 0.92548 0.88389 Finland 1-0.47697 0.02223-21.46 <.0001 0.55714 0.71892 2004 1-0.00122 0.04617-0.03 0.9789 0.14860 0.00000391 2005 1 0.05532 0.04625 1.20 0.2332 0.06535 0.00789 2006 1 0.08158 0.04643 1.76 0.0806 0.00953 0.01686 2007 1 0.10027 0.04757 2.11 0.0364 0.00008914 0.02409 2008 1 0.04870 0.04837 1.01 0.3153 0.01698 0.00560 2009 1 0.07276 0.05041 1.44 0.1507 0.34042 0.01144 2004*Lag1(LOG(B/TL)) 1-0.01427 0.03797-0.38 0.7075 0.00707 0.00078388 2005*Lag1(LOG(B/TL)) 1-0.01132 0.03774-0.30 0.7646 0.01271 0.00049941 2006*Lag1(LOG(B/TL)) 1-0.02839 0.03758-0.76 0.4508 0.00609 0.00316 2007*Lag1(LOG(B/TL)) 1-0.03431 0.03758-0.91 0.3625 0.00790 0.00461 2008*Lag1(LOG(B/TL)) 1-0.08391 0.03789-2.21 0.0280 0.00688 0.02653 2009*Lag1(LOG(B/TL)) 1-0.08837 0.04114-2.15 0.0330 0.02499 0.02499 4 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 14 118.94642 8.49617 2754.16 <.0001 Error 186 0.57378 0.00308 Corrected Total 200 119.52020 Root MSE 0.05554 R-Square 0.9952 Dependent Mean 4.95218 Adj R-Sq 0.9948 Coeff Var 1.12155 Variable Parameter Estimates DF Parameter Estimate Standard Error t Value Pr > t Intercept 1 5.03279 0.09129 55.13 <.0001 Lag1(Log(B/TL)) 1 0.41287 0.07971 5.18 <.0001 Denmark 1-0.16864 0.02375-7.10 <.0001 Spain 1-0.13490 0.07711-1.75 0.0819 England 1 0.56560 0.02680 21.11 <.0001 France 1 0.62267 0.03674 16.95 <.0001 Germany 1 0.42842 0.02483 17.25 <.0001 US 1 1.73624 0.04636 37.45 <.0001 Finland 1-0.48375 0.02211-21.88 <.0001 2004 1 0.01510 0.01688 0.89 0.3721 2005 1 0.06838 0.01747 3.91 0.0001 2006 1 0.11610 0.01916 6.06 <.0001 2007 1 0.14329 0.02101 6.82 <.0001 2008 1 0.15381 0.02004 7.67 <.0001 2009 1 0.17528 0.01789 9.80 <.0001 5 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 14 118.50619 8.46473 1552.69 <.0001 Error 186 1.01401 0.00545 Corrected Total 200 119.52020 Root MSE 0.07384 R-Square 0.9915 Dependent Mean 4.95218 Adj R-Sq 0.9909 Coeff Var 1.49097 Variable Parameter Estimates DF Parameter Estimate Standard Error t Value Pr > t Intercept 1 5.63137 0.12135 46.41 <.0001 Lag1(Log(B/TL)) 1 0.92696 0.10783 8.60 <.0001 Lag1(Log(B/TL))*DK 1 0.03602 0.02308 1.56 0.1202 Lag1(Log(B/TL))*Spain 1-0.16141 0.04891-3.30 0.0012 Lag1(Log(B/TL))*England 1-0.44662 0.03712-12.03 <.0001 Lag1(Log(B/TL))*France 1-0.50122 0.06010-8.34 <.0001 Lag1(Log(B/TL))*Germany 1-0.37728 0.02366-15.95 <.0001 Lag1(Log(B/TL))*US 1-2.16924 0.09137-23.74 <.0001 Lag1(Log(B/TL))*Finland 1 0.57793 0.02807 20.59 <.0001 2004 1 0.02386 0.02251 1.06 0.2906 2005 1 0.09032 0.02339 3.86 0.0002 2006 1 0.14759 0.02592 5.69 <.0001 2007 1 0.18540 0.02883 6.43 <.0001 2008 1 0.20510 0.02754 7.45 <.0001 2009 1 0.21202 0.02392 8.86 <.0001 6 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level 4.2 Equity Correlation of Estimates Variable Intercept Lag1(Log(E/TL)) Denmark Spain England France Germany US Finland 2004 2005 2006 2007 2008 2009 Intercept 1.0000 0.9662-0.5814-0.3830-0.5736-0.4155-0.5049-0.6991-0.6862-0.5269-0.4623-0.4816-0.5321-0.5692-0.4086 Lag1(Log(E/TL)) 0.9662 1.0000-0.4605-0.2804-0.4570-0.3000-0.3857-0.5905-0.5750-0.5820-0.5196-0.5379-0.5838-0.6182-0.4618 Denmark -0.5814-0.4605 1.0000 0.5337 0.7121 0.6084 0.6789 0.7832 0.7801-0.0116-0.0736-0.0757-0.0414-0.0017-0.0732 Spain -0.3830-0.2804 0.5337 1.0000 0.5556 0.5426 0.5507 0.4718 0.4994 0.0201 0.0202 0.0567 0.1023 0.1163 0.0715 England -0.5736-0.4570 0.7121 0.5556 1.0000 0.6083 0.6703 0.7391 0.7400-0.0144-0.0388-0.0103 0.0383 0.0532-0.0505 France -0.4155-0.3000 0.6084 0.5426 0.6083 1.0000 0.6037 0.5686 0.5839 0.0088 0.0014-0.0112 0.0085 0.0256-0.0567 Germany -0.5049-0.3857 0.6789 0.5507 0.6703 0.6037 1.0000 0.6790 0.6845-0.0089-0.0367-0.0201 0.0111 0.0162-0.0736 US -0.6991-0.5905 0.7832 0.4718 0.7391 0.5686 0.6790 1.0000 0.8795-0.0380-0.0987-0.0831-0.0292 0.0124-0.0993 Finland -0.6862-0.5750 0.7801 0.4994 0.7400 0.5839 0.6845 0.8795 1.0000-0.0275-0.0837-0.0729-0.0219 0.0262-0.0495 2004-0.5269-0.5820-0.0116 0.0201-0.0144 0.0088-0.0089-0.0380-0.0275 1.0000 0.6070 0.6136 0.6231 0.6274 0.5310 2005-0.4623-0.5196-0.0736 0.0202-0.0388 0.0014-0.0367-0.0987-0.0837 0.6070 1.0000 0.6011 0.6080 0.6074 0.5187 2006-0.4816-0.5379-0.0757 0.0567-0.0103-0.0112-0.0201-0.0831-0.0729 0.6136 0.6011 1.0000 0.6236 0.6221 0.5296 2007-0.5321-0.5838-0.0414 0.1023 0.0383 0.0085 0.0111-0.0292-0.0219 0.6231 0.6080 0.6236 1.0000 0.6396 0.5397 2008-0.5692-0.6182-0.0017 0.1163 0.0532 0.0256 0.0162 0.0124 0.0262 0.6274 0.6074 0.6221 0.6396 1.0000 0.5435 2009-0.4086-0.4618-0.0732 0.0715-0.0505-0.0567-0.0736-0.0993-0.0495 0.5310 0.5187 0.5296 0.5397 0.5435 1.0000 2004*log(lag(E/TL)) -0.4556-0.5518-0.0403 0.0074-0.0433-0.0056-0.0314-0.0804-0.0676 0.9645 0.5679 0.5731 0.5792 0.5809 0.4961 2005*log(lag(E/TL)) -0.3336-0.4303-0.1394-0.0104-0.1005-0.0331-0.0877-0.1870-0.1679 0.5377 0.9620 0.5389 0.5393 0.5333 0.4649 2006*log(lag(E/TL)) -0.2922-0.3892-0.1828 0.0095-0.1069-0.0723-0.1017-0.2219-0.2064 0.5190 0.5208 0.9511 0.5310 0.5211 0.4580 2007*log(lag(E/TL)) -0.3108-0.4047-0.1731 0.0495-0.0771-0.0672-0.0887-0.1972-0.1838 0.5172 0.5193 0.5321 0.9414 0.5270 0.4614 2008*log(lag(E/TL)) -0.4702-0.5607-0.0528 0.1020 0.0090-0.0015-0.0230-0.0550-0.0373 0.5729 0.5605 0.5742 0.5872 0.9618 0.5029 2009*log(lag(E/TL)) -0.4759-0.5603-0.0118 0.1073 0.0097-0.0143-0.0208-0.0230 0.0237 0.5416 0.5239 0.5362 0.5508 0.5582 0.9689 7 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Correlation of Estimates Variable 2004*log(lag(E/TL)) 2005*log(lag(E/TL)) 2006*log(lag(E/TL)) 2007*log(lag(E/TL)) 2008*log(lag(E/TL)) 2009*log(lag(E/TL)) Intercept -0.4556-0.3336-0.2922-0.3108-0.4702-0.4759 Lag1(Log(E/TL)) -0.5518-0.4303-0.3892-0.4047-0.5607-0.5603 Denmark -0.0403-0.1394-0.1828-0.1731-0.0528-0.0118 Spain 0.0074-0.0104 0.0095 0.0495 0.1020 0.1073 England -0.0433-0.1005-0.1069-0.0771 0.0090 0.0097 France -0.0056-0.0331-0.0723-0.0672-0.0015-0.0143 Germany -0.0314-0.0877-0.1017-0.0887-0.0230-0.0208 US -0.0804-0.1870-0.2219-0.1972-0.0550-0.0230 Finland -0.0676-0.1679-0.2064-0.1838-0.0373 0.0237 2004 0.9645 0.5377 0.5190 0.5172 0.5729 0.5416 2005 0.5679 0.9620 0.5208 0.5193 0.5605 0.5239 2006 0.5731 0.5389 0.9511 0.5321 0.5742 0.5362 2007 0.5792 0.5393 0.5310 0.9414 0.5872 0.5508 2008 0.5809 0.5333 0.5211 0.5270 0.9618 0.5582 2009 0.4961 0.4649 0.4580 0.4614 0.5029 0.9689 2004*log(lag(E/TL)) 1.0000 0.5442 0.5271 0.5242 0.5727 0.5390 2005*log(lag(E/TL)) 0.5442 1.0000 0.5154 0.5116 0.5351 0.4950 2006*log(lag(E/TL)) 0.5271 0.5154 1.0000 0.5136 0.5268 0.4836 2007*log(lag(E/TL)) 0.5242 0.5116 0.5136 1.0000 0.5316 0.4888 2008*log(lag(E/TL)) 0.5727 0.5351 0.5268 0.5316 1.0000 0.5470 2009*log(lag(E/TL)) 0.5390 0.4950 0.4836 0.4888 0.5470 1.0000 8 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 20 64.95753 3.24788 974.21 <.0001 Error 180 0.60009 0.00333 Corrected Total 200 65.55763 Root MSE 0.05774 R-Square 0.9908 Dependent Mean 5.91381 Adj R-Sq 0.9898 Coeff Var 0.97635 Variable Parameter Estimates DF Parameter Standard Estimate Error t Value Pr > t Intercept 1 6.07585 0.07655 79.37 <.0001 Lag1(Log(E/TL)) 1 2.05911 0.22800 9.03 <.0001 Denmark 1-0.09862 0.02275-4.33 <.0001 Spain 1 0.38085 0.01723 22.10 <.0001 England 1 0.34056 0.02116 16.09 <.0001 France 1 0.35308 0.01785 19.78 <.0001 Germany 1 0.52231 0.01954 26.73 <.0001 US 1 1.27236 0.03706 34.33 <.0001 Finland 1-0.72099 0.03133-23.02 <.0001 2004 1-0.02846 0.06746-0.42 0.6737 2005 1 0.00985 0.07035 0.14 0.8888 2006 1 0.07055 0.06953 1.01 0.3116 2007 1 0.11710 0.06826 1.72 0.0880 2008 1-0.01908 0.06759-0.28 0.7780 2009 1-0.07027 0.08059-0.87 0.3845 2004*log(lag(E/TL)) 1-0.10731 0.23866-0.45 0.6535 2005*log(lag(E/TL)) 1-0.21101 0.26271-0.80 0.4229 2006*log(lag(E/TL)) 1-0.19771 0.27336-0.72 0.4705 2007*log(lag(E/TL)) 1-0.13806 0.27409-0.50 0.6151 2008*log(lag(E/TL)) 1-0.57635 0.24410-2.36 0.0193 2009*log(lag(E/TL)) 1-0.90925 0.25736-3.53 0.0005 9 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 14 64.89326 4.63523 1297.70 <.0001 Error 186 0.66437 0.00357 Corrected Total 200 65.55763 Root MSE 0.05977 R-Square 0.9899 Dependent Mean 5.91381 Adj R-Sq 0.9891 Coeff Var 1.01060 Variable DF Parameter Estimates Parameter Standard Estimate Error t Value Pr > t Intercept 1 5.95500 0.06552 90.89 <.0001 Lag1(Log(E/TL)) 1 1.66254 0.17594 9.45 <.0001 Denmark 1-0.09090 0.02271-4.00 <.0001 Spain 1 0.39220 0.01757 22.32 <.0001 England 1 0.34884 0.02156 16.18 <.0001 France 1 0.35486 0.01837 19.32 <.0001 Germany 1 0.52532 0.02002 26.23 <.0001 US 1 1.28913 0.03646 35.36 <.0001 Finland 1-0.70086 0.03074-22.80 <.0001 2004 1 0.00877 0.01830 0.48 0.6326 2005 1 0.07737 0.01920 4.03 <.0001 2006 1 0.13889 0.02073 6.70 <.0001 2007 1 0.17546 0.02190 8.01 <.0001 2008 1 0.13742 0.01888 7.28 <.0001 2009 1 0.20723 0.02040 10.16 <.0001 10 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 14 66.44577 4.74613 1299.19 <.0001 Error 192 0.70141 0.00365 Corrected Total 206 67.14718 Root MSE 0.06044 R-Square 0.9896 Dependent Mean 5.91509 Adj R-Sq 0.9888 Coeff Var 1.02182 Variable DF Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept 1 6.06653 0.06032 100.57 <.0001 Lag1(Log(E/TL)) 1 2.00749 0.16312 12.31 <.0001 Denmark 1-0.09321 0.02289-4.07 <.0001 Spain 1 0.37957 0.01763 21.52 <.0001 England 1 0.34360 0.02154 15.95 <.0001 France 1 0.36057 0.01820 19.81 <.0001 Germany 1 0.53059 0.01985 26.74 <.0001 US 1 1.28402 0.03706 34.64 <.0001 Finland 1-0.70664 0.03164-22.33 <.0001 2004*log(lag(E/TL)) 1 0.03247 0.06036 0.54 0.5912 2005*log(lag(E/TL)) 1-0.21406 0.06770-3.16 0.0018 2006*log(lag(E/TL)) 1-0.46601 0.07898-5.90 <.0001 2007*log(lag(E/TL)) 1-0.63320 0.08744-7.24 <.0001 2008*log(lag(E/TL)) 1-0.46851 0.06434-7.28 <.0001 2009*log(lag(E/TL)) 1-0.66046 0.05930-11.14 <.0001 11 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level 4.3 Bank loan Correlation of Estimates Variable Intercept Lag1(BL/TL) Denmark Spain England France Germany US Finland 2004 2005 2006 2007 2008 2009 Intercept 1.0000-0.9682-0.6234 0.0677-0.7757-0.7806-0.4280-0.8764-0.8467-0.3479-0.2885-0.3284-0.3527-0.3083-0.2168 Lag1(BL/TL) -0.9682 1.0000 0.5246-0.1426 0.6896 0.6943 0.3273 0.8132 0.7721 0.3939 0.3365 0.3747 0.3975 0.3548 0.2624 Denmark -0.6234 0.5246 1.0000 0.2394 0.7270 0.7288 0.5897 0.6968 0.7244-0.0144-0.0640-0.0453-0.0328-0.0554-0.0835 Spain 0.0677-0.1426 0.2394 1.0000 0.1114 0.0993 0.3611-0.0895 0.0035 0.0310 0.0533 0.0585 0.0662 0.0725 0.0811 England -0.7757 0.6896 0.7270 0.1114 1.0000 0.8267 0.5731 0.8530 0.8537-0.0262-0.0694-0.0317-0.0061-0.0480-0.1075 France -0.7806 0.6943 0.7288 0.0993 0.8267 1.0000 0.5699 0.8628 0.8614-0.0275-0.0762-0.0434-0.0219-0.0553-0.1167 Germany -0.4280 0.3273 0.5897 0.3611 0.5731 0.5699 1.0000 0.4784 0.5308-0.0057-0.0275-0.0101-0.0006-0.0327-0.0621 US -0.8764 0.8132 0.6968-0.0895 0.8530 0.8628 0.4784 1.0000 0.9317-0.0388-0.0992-0.0589-0.0330-0.0768-0.1432 Finland -0.8467 0.7721 0.7244 0.0035 0.8537 0.8614 0.5308 0.9317 1.0000-0.0297-0.0842-0.0500-0.0260-0.0607-0.1090 2004-0.3479 0.3939-0.0144 0.0310-0.0262-0.0275-0.0057-0.0388-0.0297 1.0000 0.6432 0.6504 0.6521 0.6415 0.5846 2005-0.2885 0.3365-0.0640 0.0533-0.0694-0.0762-0.0275-0.0992-0.0842 0.6432 1.0000 0.6434 0.6441 0.6360 0.5835 2006-0.3284 0.3747-0.0453 0.0585-0.0317-0.0434-0.0101-0.0589-0.0500 0.6504 0.6434 1.0000 0.6531 0.6426 0.5858 2007-0.3527 0.3975-0.0328 0.0662-0.0061-0.0219-0.0006-0.0330-0.0260 0.6521 0.6441 0.6531 1.0000 0.6445 0.5854 2008-0.3083 0.3548-0.0554 0.0725-0.0480-0.0553-0.0327-0.0768-0.0607 0.6415 0.6360 0.6426 0.6445 1.0000 0.5820 2009-0.2168 0.2624-0.0835 0.0811-0.1075-0.1167-0.0621-0.1432-0.1090 0.5846 0.5835 0.5858 0.5854 0.5820 1.0000 2004*LOG(BL/TL) 0.2670-0.3723 0.0383-0.0529 0.0638 0.0655 0.0156 0.0887 0.0744-0.9260-0.5863-0.5906-0.5909-0.5836-0.5360 2005*LOG(BL/TL) 0.1422-0.2477 0.1284-0.0875 0.1573 0.1656 0.0614 0.2096 0.1860-0.5637-0.9237-0.5670-0.5646-0.5628-0.5254 2006*LOG(BL/TL) 0.1198-0.2246 0.1489-0.1066 0.1688 0.1833 0.0670 0.2287 0.2086-0.5567-0.5624-0.9119-0.5595-0.5581-0.5217 2007*LOG(BL/TL) 0.1084-0.2119 0.1592-0.1237 0.1711 0.1909 0.0710 0.2371 0.2169-0.5503-0.5573-0.5569-0.9035-0.5536-0.5179 2008*LOG(BL/TL) 0.2360-0.3399 0.0756-0.0959 0.0770 0.0852 0.0406 0.1175 0.0963-0.5803-0.5788-0.5831-0.5838-0.9298-0.5329 2009*LOG(BL/TL) 0.3552-0.4512-0.0261-0.0610-0.0324-0.0241-0.0099-0.0181-0.0456-0.5708-0.5602-0.5689-0.5726-0.5625-0.9210 12 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Correlation of Estimates Variable 2004*LOG(BL/TL) 2005*LOG(BL/TL) 2006*LOG(BL/TL) 2007*LOG(BL/TL) 2008*LOG(BL/TL) 2009*LOG(BL/TL) Intercept 0.2670 0.1422 0.1198 0.1084 0.2360 0.3552 Lag1(BL/TL) -0.3723-0.2477-0.2246-0.2119-0.3399-0.4512 Denmark 0.0383 0.1284 0.1489 0.1592 0.0756-0.0261 Spain -0.0529-0.0875-0.1066-0.1237-0.0959-0.0610 England 0.0638 0.1573 0.1688 0.1711 0.0770-0.0324 France 0.0655 0.1656 0.1833 0.1909 0.0852-0.0241 Germany 0.0156 0.0614 0.0670 0.0710 0.0406-0.0099 US 0.0887 0.2096 0.2287 0.2371 0.1175-0.0181 Finland 0.0744 0.1860 0.2086 0.2169 0.0963-0.0456 2004-0.9260-0.5637-0.5567-0.5503-0.5803-0.5708 2005-0.5863-0.9237-0.5624-0.5573-0.5788-0.5602 2006-0.5906-0.5670-0.9119-0.5569-0.5831-0.5689 2007-0.5909-0.5646-0.5595-0.9035-0.5838-0.5726 2008-0.5836-0.5628-0.5581-0.5536-0.9298-0.5625 2009-0.5360-0.5254-0.5217-0.5179-0.5329-0.9210 2004*LOG(BL/TL) 1.0000 0.6024 0.5962 0.5900 0.6153 0.5983 2005*LOG(BL/TL) 0.6024 1.0000 0.5945 0.5905 0.5983 0.5638 2006*LOG(BL/TL) 0.5962 0.5945 1.0000 0.5904 0.5943 0.5557 2007*LOG(BL/TL) 0.5900 0.5905 0.5904 1.0000 0.5901 0.5496 2008*LOG(BL/TL) 0.6153 0.5983 0.5943 0.5901 1.0000 0.5891 2009*LOG(BL/TL) 0.5983 0.5638 0.5557 0.5496 0.5891 1.0000 13 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 20 3.295321E13 1.647661E12 147.86 <.0001 Error 180 2.005749E12 11143050928 Corrected Total 200 3.495896E13 Root MSE 105561 R-Square 0.9426 Dependent Mean 620905 Adj R-Sq 0.9363 Coeff Var 17.00109 Variable DF Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept 1-339083 144997-2.34 0.0205 Lag1(BL/TL) 1 1163520 305056 3.81 0.0002 Denmark 1-17023 37775-0.45 0.6528 Spain 1 569465 30423 18.72 <.0001 England 1 372523 50075 7.44 <.0001 France 1 324514 51502 6.30 <.0001 Germany 1 773598 31972 24.20 <.0001 US 1 1217108 119023 10.23 <.0001 Finland 1-88724 71487-1.24 0.2162 2004 1 15743 84841 0.19 0.8530 2005 1 92558 86502 1.07 0.2861 2006 1 191476 85338 2.24 0.0261 2007 1 383838 85003 4.52 <.0001 2008 1 535438 86690 6.18 <.0001 2009 1 541273 95731 5.65 <.0001 2004*LOG(BL/TL) 1 20481 221958 0.09 0.9266 2005*LOG(BL/TL) 1 45264 235272 0.19 0.8477 2006*LOG(BL/TL) 1 27242 238696 0.11 0.9093 2007*LOG(BL/TL) 1-359011 241830-1.48 0.1394 2008*LOG(BL/TL) 1-667830 227155-2.94 0.0037 2009*LOG(BL/TL) 1-705795 228865-3.08 0.0024 14 of 6
Appendix 4 Regression output for bond level, equity level and bank loan level Source DF Analysis of Variance Sum of Squares Mean Square F Value Pr > F Model 14 3.260624E13 2.329017E12 184.13 <.0001 Error 186 2.352722E12 12649045491 Corrected Total 200 3.495896E13 Root MSE 112468 R-Square 0.9327 Dependent Mean 620905 Adj R-Sq 0.9276 Coeff Var 18.11355 Variable DF Parameter Estimates Parameter Estimate Standard Error t Value Pr > t Intercept 1-150478 140921-1.07 0.2870 Lag1(BL/TL) 1 721037 282670 2.55 0.0116 Denmark 1-33425 38867-0.86 0.3909 Spain 1 563532 32084 17.56 <.0001 England 1 339561 51045 6.65 <.0001 France 1 292461 52209 5.60 <.0001 Germany 1 768419 33828 22.72 <.0001 US 1 1133574 117700 9.63 <.0001 Finland 1-145004 70929-2.04 0.0423 2004 1 14239 34062 0.42 0.6764 2005 1 91596 34953 2.62 0.0095 2006 1 176471 36531 4.83 <.0001 2007 1 249129 37701 6.61 <.0001 2008 1 298143 33947 8.78 <.0001 2009 1 271789 39558 6.87 <.0001 15 of 6