Cash Flow Sensitivities and Financial Constraints in the Shipping Industry

Transcription

1 Cash Flow Sensitivities and Financial Constraints in the Shipping Industry Abstract Rebekka Haller Faculty of Business, University of Hamburg, Hamburg, Germany Traditionally, the shipping industry is characterized by high financial, operating, and cash flow risks and is significantly higher leveraged than other industries. As a result, shipping are more prone for financing constraints. By applying a recently established multivariate indicator for financial constraints, I examine how cash flow shocks influence financing and investment decisions of 236 international shipping. I find that firms in the shipping industry face more financing obstacles than firms from other capital-intensive industries (i.e. the airline and manufacturing industry). When faced with a decline in cash flows in non-crises periods, shipping try to fill their financing gap via short-term borrowing and excess cash. However, in contrast to the other industries examined, short-term borrowings nearly dried up in the recent financial crisis, and shipping firms were not able to keep up their high level of excess cash, leading to a significant decline in investment activity. Keywords: Maritime Financial Management; Capital Structure; Investment-Cash Flow Sensitivity; Financial Constraints 1 Introduction According to Grammenos (2010), debt capital has traditionally been the most important source of external finance for shipping. Although firms in the shipping industry have recently gained better access to the capital markets, they are still significantly higher leveraged than firms from other industries. Thus, traditional bank loans still satisfies about 75% of the industry s external financing needs. An additional feature of the shipping industry is the firms dependence on the heavily volatile freight rates, which makes the effects of economic crisis even more severe (Drobetz et al. 2013). These stylized facts make shipping more prone for financing constraints. In particular, during the recent global financial crisis bank financing has decreased dramatically and capital-intensive industries like the shipping industry have been heavily impinged (Albertijn et al., 2011) Cash flow Capital Expenditures Figure 1: Flow of Funds in the Shipping Industry Starting with Fazzari et al. (1988), the methodology used to determine whether or not firms face constraints in accessing capital markets has relied on single-equation estimates of the investment-cash flow sensitivity. The established interpretation of this estimate is that a large (small) significant coefficient means that firms are (not) forced to cut back on their investment. The underlying implication is that if firms are reducing their

2 investments dramatically, they are unable to raise funds when faced with adverse cash flow realizations. This approach implies that an unconstrained company is able to smooth their capital expenditure over the year, e.g. a decrease in cash flows does not imply a decrease in investment for the following year. Figure 1 plots the yearly development of the ratio of capital expenditures and cash flow to total assets for my sample of 236 globally-listed shipping. The Figure indicates a large need for external financing, as capital expenditures exceed the generated cash flows by a large fraction. In addition, cash flows are usually accompanied by a decrease in capital expenditures, indicating financing constraints in the shipping industry. However, as the single-equation framework only focuses on the use of funds side, the model does not offer any insights on the financing constraints at work. A recent paper by Gatchev et al. (henceforth GPT, 2010) shows that the approach of earlier single-equation studies suffers from econometric problems and should include the accounting identity that sources of funds need to equal uses of funds. The GPT methodology is based on the insight that corporate policies need to be examined with a constrained multivariate setting instead of with a single-equation model, because financial variables are related by accounting identities that hold at all times and for all firms (e.g. total assets being equal to total liabilities and sources of funds being equal to uses of funds). They argue that ignoring the interdependence of variables and using the information content of the sources-equal-uses identity causes an inefficient estimation of the coefficients. Using the system of equations model of GPT (2010), I examine how cash flow shocks influence financing and investment decision of 236 international shipping in different economic conditions. I find that firms in the shipping industry face more financing obstacles than firms from other capital-intensive industries (i.e. the airline and manufacturing industry). When faced with a decline in cash flow in normal times, shipping mainly try to fill this financing gap through short-term borrowing and excess cash. However, in contrast to other capital-intensive industries, during the recent financial crisis short term borrowings nearly dried out and they were additionally not able to keep up their high level of excess cash, leading to a significant decline in investment activity. These findings are already partly reflected in Figure 1. There is a large gap between the actual average cash flow realizations and the financing need of the shipping in my sample. As indicated by the results of the multi-equation model, in normal times, this gap is mainly filled with excess cash and short-term borrowings. However, in the ongoing crisis, the shortage of bank debt is constraining the shipping industry (ABN AMRO, 2011). This paper contributes to the literature by applying a recently established multivariate indicator for financial constraints to a highly leveraged industry, uncovering severe financing constraints faced by shipping The remainder of the study is structured as follows. Section 2 contains a description of the multi-equation model and the data as well as descriptive statistics of the shipping sample. Section 3 analyses the investment and financing behavior of shipping during normal times and the recent financial crisis. Section 4 extends this analysis to the airline and manufacturing industry and also compares them with the shipping industry. Finally, Section 5 concludes. 2 Methodology and Data 2.1 The Simultaneous Equations Model of Gatchev, Pulvino, and Tarhan (2010) Fazzari et al. (1988) propose a single-equation model to determine whether or not firms face constraints in accessing capital markets. They regress capital expenditures (divided by beginning-of-period capital stock) on internal cash flows (also divided by beginning-of-period capital stock) and Tobin s q, which controls for the firm s investment opportunities. The conventional interpretation of the sensitivity coefficient in question is that relatively large (small) coefficient implies that firms are (not) forced to cut back on their capital expenditures due to their limited ability (ability) to raise funds when faced with adverse cash flow realizations., thus these firms are financially constrained (unconstrained). GPT (2010) show that the approach of earlier studies suffers from econometric problems in the form of an omitted variables bias and inefficient coefficient estimates, and also that results obtained from static singleequation models may lead to misleading conclusions about whether or not firms face market access constraints. Earlier studies examine the capital market access issue by estimating cash flow sensitivity of investments in a single equation framework and without allowing for potential persistence in investments. By

3 estimating static rather than dynamic models, prior studies ignore the intertemporal dependencies within and across financial variables. However, many financial variables (such as capital expenditures and dividends) exhibit substantial persistence due to adjustment frictions. GPT (2010) argue that ignoring the intertemporal aspect of financial variables is likely to produce an omitted variables bias. GPT (2010) further argue that since financial variables are related by accounting identities that hold at all times and for all firms (such as assets being equal to liabilities, sources of cash being equal to uses of cash), corporate policies need to be examined in a constrained multivariate setting instead of in a single-equation platform. Due to accounting identities, changes in one variable have ramifications for changes in other variables that make-up the identities. For example, if cash flow (a source variable) increases by one dollar, it is necessarily the case that either other source variables need to decline by a dollar, use variables (such as capital expenditures) need to increase by a dollar, or some combination of increases in use variables and decreases in source variables need to add-up to a dollar. When the information contained in the sources and uses constraint is not contained in the specification of models, cash flow sensitivity of investment estimates produced by single-equation models are likely to be inefficient. The GPT (2010) model used in this paper is based on the notion that uses of funds are equal to sources of funds. Therefore, the following ex-post accounting identity always holds:, (1) Table A1 in the Appendix describes the variables that enter the model. Cash flows, denoted as internally generated funds and are defined as:, provide, (2) where is earnings before interest, taxes, and depreciation, is interest expense, is cash taxes, and is change in net working capital from year t 1 to year t. These variables are jointly determined by the firm s past investments and by consumers current behavior, and thus they, together with, are assumed to be exogenous. The variable represents internally generated funds that are available for undertaking investments and/or for making payments to shareholders and principal payments to debt holders. GPT (2010) introduce a simultaneous equations model where uses of funds are constrained ex ante to be equal to sources of funds, conditional on forecasted cash flows. They also assume that firms take their investment and financing decisions in response to their investment opportunities, proxied by the market-to-book value of equity ratio (MB), and as a function of size (SIZE), measured by the logarithm of the book value of assets. Including size in to the model accounts for the possibility that larger firms have more investment opportunities and easier access to external capital. Based on the constraints specified in equation (2), the following system of nine equations can be estimated:, (3) where represent deviations of actual quantities from planned quantities and are the terms associated with the nine financing and investment decision variables. Also based on equation (2), the sum of these error terms equals the corresponding forecast error for cash flows. Imposing the sources-equal-uses-offunds constraint implies a set of restrictions for the parameter matrices. First, the total response of the investment and financing variables is opposite to the shock in cash flows and the coefficients must add up to one:

4 , (4) where is a unit vector of appropriate order. The interpretation of the first restriction in equation (4) is that when there is a one dollar shock in a source or use variable, the total response of the investment and financing variables is opposite in sign to the shock and adds up to one dollar. For example, if the source variable, CF, increases by one dollar, other source variables must decline by a dollar, use variables must increase by one dollar, or some combination of the response of source and use variables must sum to one dollar. In contrast, if the shock occurs in the current period and originates from a variable that represents neither a source nor a use of funds in the current period (i.e., the two exogenous control variables MB and SIZE and lagged dependent variables), the total response across the system of equations must sum to zero. As an example for the second and third type of restriction in equation (4), assume that the estimated coefficient on SIZE in the capital expenditures equation is 0.20, implying that capital expenditures increase by 20 cents when the natural logarithm of book assets increases by one. As capital expenditures is a use variable, and because sources of funds must equal uses of funds, other use variables must decrease by 20 cents, net source variables must increase by 20 cents, or some combination of these responses must sum to 20 cents. As a result, the SIZE coefficients across the system of nine equations will sum to zero. Similar constraints hold for MB and all lagged dependent variables. The interpretation of the signs of the estimated coefficients from the multiequation model depends on whether it is a use (CAPX, ACQUIS, and ASALES), a distribution (DIV, and RP), or a source variable (LTD, STD, EQUISS and CASH). For example, a positive coefficient on a use variable with a negative sign, e.g. capital expenditures, indicates that in response to a reduction in cash flows, capital expenditures will decrease. The interpretation of the two distribution variables dividends and share repurchases is analogous. In contrast, the inverse logic applies to the source (financing) variables. 2.2 Data I analyze annual balance sheet and market data of 236 shipping from 30 countries over the period. Data on active and inactive exchange-listed firms is obtained from the Compustat Global database. As Compustat Global data becomes available in 1985 and at this early stage does not provide complete records for all countries, I start my analysis in 1990 and add countries as data has become available. Table 1: Descriptive Statistics of the 236 Shipping Companies N Mean S.D. N Mean S.D. Cash Flow 2, Dividends 2, Capital expenditures 2, Long-term debt 2, Acquisitions 2, Short-term debt 2, Asset sales 2, Cash balances 2, Equity issues 2, Market-to-book 2, Share repurchases 2, Firm size 2, Following Drobetz et al. (2013), the condition for firms to be included in the sample is that they own and/or operate commercial ships. This approach implies that shipyards as well as passenger ships, drilling ships, supply vessels, and inland vessels are excluded from the sample. Moreover, the sample is restricted to shipping firms with consolidated balance sheet data, positive values for total book value ( ) and market value of assets ( or ). All variables are denominated in U.S. dollars. Panel A of Table A1 summarizes the definitions and Compustat codes of the source, use, and control variables that enter the simultaneous equations model. Following GPT (2010), I replace missing values with zero. The format of the Statement of Cash Flows differs across countries, thus several variable definitions require some minor adaptations. Panel B of Table A1 explains these differences in the definitions of asset sales, share repurchases, dividend payments, and acquisitions by format code. Finally, I trim the market-to-book ratio at the 95% tail. Table 1 presents means and standard deviations for all variables of the model as a proportion of total assets (except for firm size and market-to-book ratio). The most volatile variables in the shipping sample are firm size and market-to-book ratio, indicating a very heterogenic sample with both young and mature firms. Moreover, the change in long-term debt also has a large standard deviation denoting a strong cyclicality in the amount of long-term debt issued across the sample and over time.

5 $/Day 3 The Investment Behavior of Shipping Companies in different Economic Conditions This section presents the results from estimating the system of equations in Eq. 3, subject to the restrictions in Eq. 4, using the shipping sample described in the previous section. Table 2 shows the main results. Column (1) reports the results for the full sample period. The estimates for the normal times in are shown in column (2), and for the recent financial crisis in column (3). I choose this classification to examine shipping firms financing constraints under different economic conditions. As indicated by the development of the ClarkSea index in Figure 2, the weighted average index of freight earnings has been fairly stable or increasing during the period. Therefore, I select this period in order to examine how shipping react to a decline in cash flow in normal times and compare it to the ongoing crisis in the shipping industry. I did not consider the declines in 1992 and 2002 as separate crises, because, in contrast to the recent financial crisis, bank-lending was not affected during these times. For the overall economic, most studies somewhat focus on the period from 2007 to 2009 to capture the recent financial crisis (e.g. Almeida et al., 2011; Kahle and Stulz, 2013). However, freight rates reached their peak in May 2008 and survey evidence in Campello et al. (2010) indicates that spill overs from the recent financial crisis started to heavily affect firms investment policy in the middle of Since the freight rates are still falling and the excess to bank lending for the shipping industry is still constrained, I choose the period to examine how shipping are affected by the recent financial crisis. 60,000 ClarkSea index 50,000 40,000 30,000 20,000 10,000 0 Date (month) Figure 2: Monthly Development of the ClarkSea Index from A comparison of columns (2) and (3) indicates whether shipping firms suffered from a more severe financial environment during the recent financial crisis compared to times of normal economic activity. The first three variables (capital expenditures, acquisitions, and asset sales) constitute the investment block. Distribution decisions are represented by the two variables share repurchases and dividends. Equity issues and changes in short- and long-term debt represent the two external financing sources. The change in cash holdings captures the endogenous component of the internal financing sources. Cash flows, which are the other internal financing source, are exogenous to a firm in the model. Except for the insignificant positive coefficients for asset sales and equity issues, the results in Table 2 indicate that in terms of their signs, the estimated coefficients for investment-, distribution-, and financingcash flow sensitivities are robust. In fact, 24 of the 27 estimated coefficients in Table 2 have the expected signs. Furthermore, 17 of the estimated coefficients are statistically significant, and none of the three coefficients that have the incorrect sign exhibits statistical significance. When firms experience a one dollar decline in cash flows, they reduce their investments, increase asset sales, reduce shareholder payouts (both dividends and share repurchases), increase their short- and long-term borrowings, and draw down on their cash holdings. However, while the cash flow sensitivity coefficients have the same sign independent of the sample period considered, their size, and thus the composition of firms responses to cash flow shocks, differ

6 noticeably. As the estimates for the full sample in column (1) are merely a result of combining the years for the normal times period in column (2) and the recent financial crisis in column (3), for the sake of brevity, I will not discuss these results and focus on the estimates in column (2) and (3). During the normal times period from which is characterized by stable or increasing freight rates (they never fall under the level of 1990), the estimated investment-cash flow sensitivities (i.e., the coefficients on capital expenditures, acquisitions, and asset sales) in column (2) are relatively small in magnitude and do not exhibit economic significance. This indicates that a decline in cash-flows did not significantly affect the firms investments as they were able to bridge the gap by significantly increasing long- and short-term borrowings, make use of excess cash and decrease their distributions to the shareholders (i.e., the distribution-,. borrowing- and leverage-related cash flow sensitivities, dominate the investment responses). Table 2: The Investment Behavior of Shipping Companies in different Economic Conditions Full sample Normal times Recent financial crisis Difference in Cash Flow t (1) (2) (3) (4): (2) -(3) Capital expenditures t *** ** Acquisitions t * *** Asset sales t Equity issues t ** ** Share repurchases t ** ** *** ** Dividends t *** * *** *** Long-term debt t *** *** *** Short-term debt t ** ** ** Cash balances t *** *** ** ** Uses t + Sources t Number of Observations 2,872 1, When firms in the shipping industry face a one dollar decline in cash flow, the decline is mainly overcome by the leverage variables (an increase in borrowings plus the draw-down of cash balances) with a total of $0.89. The coefficient on equity issues is not significant and very small ($0.01). Thoroughly, shipping firms are able to retrieve $0.44 from short-term, $0.21 from long-term borrowings and $0.26 from using excess cash. This finding is in contrast to the usual response of industrial firms examined by GPT (2010), who find that in normal times firms mainly rely on long-term borrowings followed by short-term borrowings, and only an insignificant amount of excess cash. However, as the shipping industry is particularly risky, commercial banks, which are the most important source of debt finance for the shipping industry, refrain from long-term commitment und high credit volumes. In addition shipping loans usually have very restrictive covenants (Stopford, 2009; Grammenos, 2010). This could also be the reason why shipping firms hold so much excess cash. This notion is also supported by Bates et al. (2009), who find that firms with higher cash flow volatilities generally have higher cash holding. Figure 1 indicates extremely volatile cash flow in the shipping industry and the positive significant estimate on cash for the normal times sample in column (2) supports their results. Even in normal times, they use $0.26 of excess cash in order to avoid that their financing gap spills over on their investment activity. Moreover, they even significantly reduce their distributions to the shareholders. As a consequence the investment coefficients are all very small and do not exhibit statistical significance. This results yields, that in normal times shipping are somewhat able to finance their investment activity. The recent global financial crisis has heavily impinged on the shipping industry and in contrast to the majority of the other industries, the shipping industry remains depressed. There are several factors that influenced this development. Traditionally, the shipping industry has always been an extremely volatile business that is closely connected to the global economy. The last booming phase of the shipping cycle ended in mid-2008 and was characterized by extremely high freight rates and consequently by one of the largest ordering phases in history (Albertijn et al., 2011). This development is supported by the results of Greenwood and Hanson (2013), who find that shipping firms overinvest in boom periods because they over-extrapolate abnormally high profits in the future. The recent financial crisis hit the shipping industry in September As indicated by Figure 2, the ClarkSea index dropped by almost 85% between September 2008 and April 2009, leading to massive drop in second hand vessel prices and an overall large vessel oversupply. Hence, the current shipping market shows limited activity. An additional caveat is that the banking industry was heavily affected by the

7 financial crisis as well. Traditionally bank loans have satisfied about 75% of the shipping industries financing needs, however, as a consequence of the recent financial crisis the number of active shipping banks has decreased dramatically (Albertijn et al., 2011). In contrast to the results for the normal times, the coefficients for the recent financial crisis period in column (3) indicate severe financing constraint of firms in the shipping industry. The firms investment decisions are now strongly affected by a decline in cash flow. However, the reasons for this development can mostly be identified by a significant change in the leverage coefficients. Short-term borrowings, the largest financing source of shipping in normal times, completely dried out. The coefficient on short-term debt in column (3) is not significant and close to zero. Additionally, the bootstrap difference test in column (4) indicates a significant decline from $0.41 to -$0.05. In this case, banks were probably able to use escape clauses they had in their lines of credit contracts as many shipping probably were in violation of loan covenants due to the impact of the crisis (Sufi, 2009; Acharya et al., 2013). Even more troubling, the shipping were not able to keep up with their high level of excess cash. In normal times shipping already have to use a large fraction of these cash reserves ($0.26) in order to prevent their financing constraint from spilling over on their investment activity. However, during the recent financial crisis they were not able to keep up this level. Moreover, the coefficient significantly decreased by $0.17 to $0.09. Shipping were able to significantly increase their equity issues to $0.04 and increase (not significant) their long-term borrowings by $0.10. All in all however, the financing side decreased from $0.89 to $0.48. Naturally, the investment side was heavily affected. As shipping were only able to fund $0.48 of a one dollar decline in cash flow on the financing side, in contrast to the normal times, the investment variables exhibit positive and significant coefficients. During the recent financial crisis the shipping respond to a one dollar decline in cash flows by decreasing capital expenditures by $0.10 and acquisitions by $0.08. Yet, this dramatic decline is somewhat buffered by the strong significant decrease in shareholder distribution (the difference summing up to $0.39). Overall, firms from the shipping industry are confronted with severe financing constraints in the ongoing financial crisis. According to Drobetz et al (2013), shipping generally have higher leverage ratios and a higher financial risk than the average industrial firm. Moreover, the shipping industry is fragmented and consists of a large number of small firms with concentrated ownership, indicating a limited access to the capital market. Even though shipping have begun to tap the global capital markets, traditional bank loans are still their main source of financing (Grammenos et. al, 2007; Stopford, 2009). Therefore, the shortage of bank debt in the recent financial crisis is constraining the shipping industry. My results support this view. In normal times, shipping are somewhat able to keep their financing constraints from spilling over on their investment policy. They mainly manage this by using large amounts of access cash. However, they are not able to keep their high level of excess cash during the recent financial crisis, as their access to short-term borrowings has severely declined as well. In the following I will compare the current situation of the shipping industry with other capital-intensive industries, namely the airline and manufacturing industry. 4 Cash Flow Sensitivities across Capital-Intensive Industries In order to better understand the financing constraints faced by the shipping industry, I compare the estimates from the multi-equation model in Table 3 with other capital-intensive industries. I choose the airline and manufacturing industry, as they both also hold sophisticated assets with highly volatile market values. The full shipping sample consists of 2,872 from 30 countries during the sample period. The airline sample is drawn from the 30 countries included in the shipping sample and identified by SIC codes (Transportation by Air). It comprises 2,158 firm-year observations. Following Chen and Chen (2012), the manufacturing sample comprises all firms with the first digit of the Standard Industry Classification (SIC) code equaling two or three but excluding firms with a two-digit SIC codes of 39 ( Miscellaneous Manufacturers ). The manufacturing sample is further drawn from the 30 countries included in the shipping sample and comprises 183,784 firm-year observations. However, in order to better fit the number of firmyears of the other industry samples I used an age- and size-matched bootstrap of 300 manufacturing firms, leaving the manufacturing sample with a size of 4,514 firm-year observations. Again, the sample period is divided into the normal times and the recent financial crisis. Table 3 presents the results

8 from estimating the system of equations in (3), subject to the restrictions in (4), over the three industries. The estimates for the model for the normal times are reported in column (1)-(3) in of Panel A and the estimates for the recent financial crisis are reported in column (4)-(6). The bootstrap difference tests for the coefficients from Panel A are reported in Panel B. Table 3: Cash Flow Sensitivities across Capital-Intensive Industries Panel A: Cash Flow Sensitivities Normal times ( ) Recent financial crisis ( ) Shipping Airline Manufacturing Shipping Airline Manufacturing [1] [2] [3] [4] [5] [6] Capital expenditures t *** *** *** Acquisitions t *** * Asset sales t ** ** Equity issues t * ** Share repurchases t ** ** * *** ** Dividends t * * *** *** ** Long-term debt t *** *** *** *** ** *** Short-term debt t ** ** *** *** Cash balances t *** *** ** ** *** *** Uses t + Sources t Number of Observations 1,888 1,578 3, ,115 Panel B: Bootstrap Difference Tests Difference in Cash Flow t Difference in Cash Flow t Difference in Cash Flow t (1): (1) -(4) (2): (2) -(5) (3): (3) -(6) Capital expenditures t ** Acquisitions t Asset sales t Equity issues t ** Share repurchases t ** Dividends t *** Long-term debt t Short-term debt t ** Cash balances t ** *** *** Examining the estimates in column (1) to (3) the financing constraints faced by the shipping industry in normal times are positioned somewhere in the middle between the manufacturing and airline industry. A one dollar decline in cash flow is financed with $0.75 in the airline industry, $0.89 of leverage in the shipping industry, and $0.90 of leverage in the manufacturing industry. The most severe financing constraints in normal times are therefore certainly faced by the airline industry. These firms respond to a one dollar decline in cash flow with significantly cutting their capital expenditures by $0.20. In contrast to the shipping industry they even significantly issue equity in order to maximize their source of funds. The airline industry is also forced to use a high level of excess cash ($0.22 compared to $0.26 and $0.15 in the shipping and manufacturing industry) to fund their investments. Moreover, in response to a one dollar decline in cash flow all firms significantly cut shareholder distributions. However, with a total of $0.05 the shareholders in the airline industry have to deal with the highest cutbacks compared to the shipping ($0.04) and the manufacturing industry ($0.03). Surprisingly, in contrast to the shipping industry even the financing constraints of the airline industry are not notably increasing during the recent financial crisis. Panel B uses bootstrapped difference test in order to compares the estimates from the three industries in Panel A between the normal times and the recent financial crisis period. Except for the shipping industry in column (1) and the cash coefficients in column (2) and (3) there are no significant differences, indicating no significant effect of the recent financial crisis on the airline or manufacturing industries investment policy. A one dollar decline in cash flow is still financed with $0.78 of leverage in the airline industry and manufacturing industry, while it declines to $0.48 in the shipping

9 industry. This is mainly a consequence of the inability of the shipping to increase their cash financing. The from the airline and manufacturing industry were able to increase their cash by $0.26 and $0.31 in order to fill their financing gap. However, the shipping industry was not even able to maintain the high level of excess cash ($0.26); it declined by a significant $0.17 to $0.09. All in all, when comparing all three industries, Firms from the manufacturing are facing the least financing constraints in both periods. They are able to preserve their access to external capital during the financial crisis and have enough internal funds to keep their declining cash flows from spilling over on their investment activity. Firms from the airline industry are facing severe financing constraints, which significantly influence their investment policy. However, they are able to preserve more internal fund in normal times, so the decline in external funds during the recent financial crisis did not affect the airline industry as much as the shipping industry. The shipping industry was heavily affected by the recent financial crisis, as firms were not able to maintain their high level of excess cash and banks additionally did not continue their engagements in short-term borrowings. 5 Conclusion Using the system of equations model of GPT (2010), I examine how cash flow shocks influence financing and investment decision of 236 globally-listed shipping in different economic conditions. This paper contributes to the literature by applying a recently established multivariate indicator for financial constraints to a highly leveraged industry, uncovering severe financing constraints faced by shipping In normal times, shipping are to some extend able to compensate declines in cash flow by using internal funds (e.g. excess cash) and a large fraction of short-term debt. This finding is in line with Bates et al. (2009) who find that firms with higher cash flow volatilities generally have higher cash holding. When firms in the shipping industry face a one dollar decline in cash flow, the decline is mainly overcome by the leverage variables with a total of $0.89. This finding is in contrast to the usual response of industrial firms examined by GPT (2010), who find that in normal times firms mainly rely on long-term borrowings followed by short-term borrowings, and only an insignificant amount of excess cash. However, as the shipping industry is particularly risky, commercial banks, which are the most important source of debt finance for the shipping industry, refrain from long-term commitment und high credit volumes. In addition shipping loans usually have very restrictive covenants (Stopford, 2009; Grammenos, 2010). The recent global financial crisis has heavily impinged on the shipping industry. Freight rates are in free fall and there is also no sign of recovery in the second-hand prices for vessels. In addition, during the recent global financial crisis bank financing, the main source of capital for the shipping industry, has decreased dramatically. All these stylized facts indicate, that shipping might face more severe financing constraints during the ongoing crisis than other capital-intensive industries. The results presented in the paper support this notion. During the recent financial crisis shipping were not able to maintain enough internal funds to compensate the decline in cash flow. In addition, short-term debt declined dramatically, leading to a significant decrease in investment. Even though, compared to the shipping industry, the airline industry is generally more constrained in their investment policy, airline were able to somewhat preserve their levels of financing sources during the recent financial crisis. One reason for the heavy impact of the recent financial crisis on the shipping industry could be their limited access to the capital market. Instead of relying on commercial banks to fund 75% of their external financing needs, the shipping industry should try to better balance their sources of funds. Moreover, overinvestment in normal times is still an immense issue, as it leads to a low speed of adjustment during economic downturns (Drobetz et al., 2013).

10 References ABN AMRO, 2011, Ship Finance and Investment: Current Trends in Ship Finance, Istanbul: 3rd Mare Forum in Ship Finance. Acharya, V., Almeida, H., and Campello, M. (2013), Aggregate risk and the choice between cash and lines of credit, Journal of Finance 68(5): Albertijn, S., Bessler, W., and Drobetz, W. (2011), Financing shipping and shipping operations: A risk-management perspective, Journal of Applied Corporate Finance 23(4): Almeida, H., Campello, M., Laranjeira, B., and Weisbenner, S. (2011), Corporate debt maturity and the real effects of the 2007 credit crisis, Critical Finance Review 1(1): Bates, T.W., Kahle, K.M., and Stulz, R.M. (2009), Why do U.S. firms hold so much more cash than they used to?, Journal of Finance 64(5): Campello, M., Graham, J., and Harvey, C. (2010), The real effects of financial constraints: evidence from a financial crisis, Journal of Financial Economics 97(3): Chen, H., and Chen, S. (2012), Investment-cash flow sensitivity cannot be a good measure of financial constraints: Evidence from the time series, Journal of Financial Economics 103(2): Drobetz, W., Gounopoulos, D., Merikas, A., and Schroeder, H. (2013), Capital structure decisions of globallylisted shipping, Transportation Research Part E: Logistics and Transportation Review 52(SI): Fazzari, S.M., Hubbard, R.G., and Petersen, B.C. (1988), Financing constraints and corporate investment, Brookings Paper on Economic Activity (1): Gatchev, V.A., Pulvino, T., and Tarhan, V. (2010), The interdependent and intertemporal nature of financial decisions: An application to cash flow sensitivities, Journal of Finance 65(2): Greenwood, R., and Hanson, S.G. (2013), Waves in ship prices and investment. Working paper. Harvard Business School. Grammenos, C.T., Alizadeh, A.H., and Papapostolou, N.C. (2007), Factors affecting the dynamics of yield premia on shipping seasoned high yield bonds, Transportation Research Part E: Logistics and Transportation Review 43(5): Grammenos, C.T. (2010), The Handbook of Maritime Economics and Business, Informa Law: London. Kahle, K.M., and Stulz, R.M. (2013), Access to capital, investment, and the financial crisis, Journal of Financial Economics 110(2): Rogers, W.H. (1993), Regression standard errors in clustered samples, Stata Technical Bulletin 3(13), Sufi, A., 2009, Bank lines of credit in corporate finance: An empirical analysis, Review of Financial Studies 22(3): Stopford, M. (2009), Maritime Economics, Taylor & Francis: New York.

11 Appendix Table A1: Compustat Variable Definitions of the Source, Use, and Control variables Variable name Abbreviation Description Construction from Compustat items Source Cash flow CF Internally available cash flow oibdp (xint idit) (txt txdc) for investment and financing NWC Long-term debt LTD Change in long-term debt dltt Short-term debt STD Change in short-term debt dlc Equity issues EQUISS Equity issues sstk Asset sales ASALES Sales of assets and investments sppe(scf =1, 3, 5, 7); psfix(scf = 10); prosai(scf =11); stfixa(scf = 12) Use Share repurchases RP Purchase of common and preferred stocks prstkc; prstkc + purtshr(scf = 10) Dividends DIV Cash dividend dv; eqdivp(scf = 12) Capital expenditures CAPX Net capital expenditures capx Acquisitions ACQUIS Acquisitions aqc; aqcdisn (scf = 12) Cash balances CASH Change in cash balance che Other Market-to-book MB Ratio of market value of equity to book value of equity (at ceq* + prcc csho) / at Firm size SIZE Logarithm of total book assets ln(at) *If ceq is missing, either seq or otherwise (at lt) is used. **scf = Variable in Compustat which contains the format code of the Statement of Cash Flows. U.S. firms report their Statement of Cash Flows in format codes 1, 3, 5, and 7, U.K. firms in format codes 7, 10, and 11, and firms from the rest of the world in format codes 10 and 11.