Forecasting Europe s Corporate Credit Risk 2013-2015

Vienna, March 2013 Forecasting Europe s Corporate Credit Risk 2013-2015 - Abbreviated Hand-Out Version: Austria - Summary In this study, Quantic Risk Solutions (QRS) presents its innovative and unparalleled approach CreditDynamix to unify credit risk rating with a portfolio view and macro-economic scenario analysis. It is a next-generation approach to explain future credit risk evolution based on Bureau van Dijk (BvD) data. Apart from unifying these three formerly separated strings of quantitative analysis, QRS has mastered to develop an approach that is built on bottom-up understanding how single corporate firm s balance sheets react to a given macro-economic environment and as a result of management action due to their expectations of current and future macro-economic conditions. CreditDynamix reflects and models how the European macro-economies drive the balance sheets of their firms allowing to forecast the evolution of credit risk factors like probability of default (PD), expected loss (EL) and risk-weighted assets (RWA) both on a single entity level as well as on aggregate levels, e.g. economic branches, regions, countries, portfolios etc. Based on the macro-economic consensus scenarios of the Economist Intelligence Unit (EIU) for the European countries 2013-2015 as of autumn 2012 we derive forecasts of certain important company financial ratios as well as credit risk figures for fifteen selected countries in Western, Southern, Northern and Central Europe. BvD s data universe covers approximately 110 million companies globally. This study is based on BvD balance sheet data of more than 1.5 million European companies over more than one decade. BvD has been considered as the most appropriate high quality data source and therefore been chosen for CreditDynamix. The methodology is not necessarily limited to using BvD data exclusively but truly unleashes its power using it. I. Introduction Ever since the outbreak of the recent banking crisis sparked by the downswing of the US subprime market in 2007 and Europe s following sovereign debt crisis since 2010, both the broad public as well as the credit and risk management experts have been puzzled by the economic interactions leading to those domino effects that we have painfully witnessed. Apart from all the institutional settings and incentive-related reasons that have generated this crisis, we think that from a methodology perspective it is fair to assess that the interaction of the 1

macro-economy with the financial markets (partly because there is still no convincing, widely accepted and comprehensive theory) and its meaning for the evolution of credit risk has been largely overlooked so far. Additionally, assessing creditworthiness of single obligors with rating models on the one hand and credit portfolio quantification and management on the other hand often rely on inconsistent information. Specifically, all types of risk analysis are plagued by their backward-looking nature and usually outdated data. Balance sheet ratios at best reflect a company s situation one year ago. As a consequence, we have developed an innovative yet intuitive approach to bridge the gap between these obstacles. We call this approach CreditDynamix. This study outlines the working of CreditDynamix and demonstrates its strength in assessing the credit risk lying ahead of us for the next three years. For this purpose, we use the macroeconomic consensus scenarios of BvD (initial data source Economist Intelligence Unit - EIU) for the European countries 2013-2015. This does not mean that we exclusively believe in these scenarios or that only these scenarios can be used for CreditDynamix. In fact, the appeal of our approach is that any macro-economic scenario can be used and processed automatically across the complete company universe as well as any valid rating model. This modular working is another of CreditDynamix big strengths. II. CreditDynamix : the new paradigm for credit risk forecasting CreditDynamix distils the learning of how the financials of companies react to the macroeconomic environment that these companies act in. Given the advent of Basel II ten years ago, only now there is a sufficient amount of data with the required level of detail that allows this type of analysis. Based on this dependency of micro-financials on macro-economic environment variables, CreditDynamix then uses forecast scenarios of macro-economic variables to arrive at current and forecast corporate financials. The importance of this step can hardly be over-estimated: Macro economic forecasts are generally available from many different sources with certain levels of accuracy. CreditDynamix can be used with any set of scenarios independent from its source CreditDynamix then links these forecasts to individual company financials and credit risk rating as well as portfolio predictions with a statistically tested accuracy via using the dependency of company financials on the macro-economy. This allows for a forward-view on corporate financials with an unprecedented accuracy The following figure shows the modular approach of CreditDynamix : 2

Figure II-1: Modular structure of CreditDynamix The starting point is the financial statement data. Using BvD corporate data we have processed more than 1.5 million corporate firms in Western, Southern, Northern and Central Europe with more than 10 million single balance sheets. The dependency model is the core of CreditDynamix. It links the macro-economic scenarios to firm decisions and thus the evolution of the financial statements as shown in the next graphic: Figure II-2: Structure of dependency model Based on the results of the dependency model it is possible to forecast any firm s balance sheet and P&L position and/or aggregate the figures across firm sizes, branches, countries, portfolios etc. The level of significance (R 2 ) for individual balance sheet items is in most cases higher than 80%. As an example for the goodness-of-fit of the dependency model the next figure shows turnover and GDP dynamic for Germany: 3

Figure II-3: Historical fit of turnover and GDP in Germany In this study we only present aggregated results on country level based on bottom-up analysis. Note, however, that the forecast of financial statements and credit risk can be broken down on a single firm level in any case. This works schematically as follows: Figure II-4: Forecasting balance sheets and credit risk based on macro-economic scenarios With these results, a manifold of applications is possible ranging from risk management & capital planning to portfolio management and business sizing and planning as shown on the next figure: 4

Stress Testing Macro- economically based Ratings Forward- Looking Pricing Forward- Looking Provisioning Migration Risk Risk Management & Capital Planning Macro credit risk model Business sizing & Steering Portfolio Management Creation of pre- approved limits Credit Portfolio and Capital Management Forecasting risk- return portfolio relations Benchmarking against the market Target Client management Wallet Sizing Pricing Figure II-5: Applications of CreditDynamix As mentioned previously, the basis of our study rests on the use of macro-economic scenarios of the Economist Intelligence Unit (EIU). This approach has been taken for a simple reason: since the EIU counts among the most reputable global macro-economic institutions, we feel that their scenarios are a suitable anchor for our forecasts and to focus on our methodology instead of arguing about which macro-economic scenario would be best to use otherwise. We need to re-state, though, that CreditDynamix can be used with any macro-economic scenario or driver model. Application of CreditDynamix is not restricted on the countries presented in this study. It is applicable to any country and region of the world. III. Study results This study covers the following countries: Austria (AT) Belgium (BE) Denmark (DK) France (FR) Finland (FI) Germany (DE) Great Britain (GB) Greece (GR) Ireland (IE) Italy (IT) 5

Netherlands (NL) Portugal (PT) Spain (ES) Sweden (SE) Switzerland (CH) The study results are conditional on EIU consensus scenarios. Based on these scenarios (one scenario per country), we compute the reactions of corporate firms on single firm level and then aggregate the following five financial ratios across all firms on country level in a numberweighted way: Total Liabilities per Turnover (TOL / TUR) Current Assets minus Current Liabilities per Total Assets ((CUA CUL) / TOA) Equity per Total Assets (EQI / TOA) Cash per Total Liabilities (CSH / TOL) Earnings before taxes per Turnover (EBT / TUR) Additionally, we analyse for all countries Probability of Default (PD), Expected Loss (EL) and Risk-Weighted Assets (RWA) as risk measures on company level and discuss them on an aggregated country level. In order to generate PDs, we use a validated and adequately calibrated rating model. We use the generated PDs to compute EL and RWA, the latter with the use of the well-known RWAformula from the Basel II framework. Moreover, we use LGD = 45% for both EL and RWA calculations and maturity = 1 years for RWA calculations. We compute PD, EL and RWA on a single company basis and then aggregate across all firms as before. For EL and RWA we use predicted liabilities as a proxy for exposure. Since we are mainly interested in the dynamic of these figures the use of liabilities is appropriate for this purpose. All analysis is conducted using variables denoted in local currency. While for most countries this is of minor importance and negligible due to their use of a common currency (i.e. Euro), some country s ratios might be affected. While detailed analysis for all listed countries can be found in section IV of this document, we would like to compare three key areas across all countries to highlight structural impact of the applied scenarios on the European corporate universe. We focus on three areas: 1. Sales and profitability 2. Capital structure 3. Risk profile 6

1. Sales and profitability Following moderate forecasts in macro-economic developments for the next years, a corresponding impact on sales as well as on corporate profits can be observed: Table III-1: Sales forecast Table III-2: Profit per turnover forecast For most countries stagnant sales are to be expected in 2013 to 2014 with some recovery in 2015. Profit ratios remain pretty much constant at 2012 levels. Spain, Portugal and Switzerland are affected most in that they show shrinking sales during 2013-2015 not returning to 2012 levels. Regarding profitability, Switzerland shows negative growth of profit margins in 2013 and 2014 coming to a stand in 2015. Spain shows strongly decreasing profit ratios in 2013 that stabilise afterwards. Portugal, though showing slight recovery in sales in 2015 faces declining profit margins in 2013 but starts to grow their profit margins again in 2014-2015. Greece and Ireland are projected to experience consolidating sales 2013-2014 with some growth in 2015 whereas Italy shows rather strong sales growth 2014 and 2015. The turnaround of Italian firms manifests itself in growing profit margins 2014 whereas Irish and Greek firms are just able to keep the level of profit margins. 2. Capital structure The most prominent ratio to proxy the corporate capital structure is equity per total assets (i.e. equity ratio) due to its typically high relevance in corporate risk analysis. Based on the macroeconomic scenarios we forecast equity ratio by every single firm, aggregate across countries and index the results on 2012 level so that the following impact can be observed: 7

Table III-3: Equity ratio forecast The lesson is that Greek firms are on average not able to maintain their 2012 levels of equity ratio due to the crisis, whereas Portuguese and Italian firms manage to almost keep their equity ratio over the study horizon. In all other countries, corporate firms achieve even higher equity ratio levels over time which is most pronounced for German and, surprisingly, Spanish firms. The reason for this within the assumed macro-economic scenario - is that Spanish firms manage to keep and even slightly increase their equity during 2013-2015 and at the same time reduce their total assets. This means that Spanish firms react quite naturally to the crisis: they clean their balance sheets getting rid of unprofitable activities thus keeping their profit level with smaller balance sheets. German firms, in contrast, increase their activities resulting in growing balance sheets still managing to generate and retain even higher profits so that their equity ratio even grows. 3. Risk profile In order to estimate corporate credit risk, we apply a proprietary quantitative rating model on all individually forecasted corporate balance sheets. The derived PD is then plugged into the Basel-II RWA formula in order to estimate an RWA impact based on projected corporate liabilities: 8

Table III-4: PD forecast For most countries a slight PD reduction can be observed which is in line with the mostly mild macro-economic scenarios. While the general PD increase across Greece, Portugal and Spain is not surprising, the negative forecasts for Switzerland and Netherlands are a bit unexpected, at least at a first sight. In the case of Switzerland, however, one has to take into account that the Swiss macro-economic scenario still assumes a very strong currency for 2013 and 2014. This has two effects: first, deflation is more an issue than inflation because corporate profit erosion might happen once a downward spiral of deflation expectations emerges. Second, export-oriented Swiss firms might get into competitiveness problems if the currency keeps its strength over a prolonged period of time. The Netherlands, in turn, come out of a recession 2012-2013 which leaves its traces in corporate balance sheets so that this is the main reason for increasing Dutch corporate PDs. Table III-5: RWA forecast 9

In terms of risk-weighted assets a moderate growth can be expected for most countries, mainly driven by an increase in liabilities in corporate balance sheets (Table III-5). Of course the detailed level of RWA-dynamics is of major importance for European bank s business planning. 4. Summary We have shown that based on the EIU consensus scenarios the results for the evolution of countrywide credit risk during the period 2013-2015 are quite different across European countries. As bottom line we draw the following conclusions based on the applied macroeconomic forecasts across European corporates: In general, a de-leveraging across corporates is expected This effect combined with reduced sales growth and profitability is expected to cause negative impact on equity returns at least until 2015 In parallel, the increased equity ratios result in reduction in default risk Still RWAs are expected to grow mainly driven by growing liabilities this implies, that companies are expected to have sufficient access to liquidity Countries which are affected most by the recent crisis (Spain, Portugal, Greece as well as Italy to some extend) show in most cases inverse or at least delayed effects More details (e.g. breakdown of all forecasted variables by branches and firm sizes) can be requested from QRS directly. IV. Example: Results on Country Level - Austria IV.1 Austria The EIU consensus scenario for Austria 2012-2015 looks as follows: 2012 2013 2014 2015 GDP growth 0.12% 0.95% 1.27% 1.79% Inflation 2.24% 2.30% 2.66% 2.11% Unemployment rate 4.1% 4.1% 4.1% 3.9% USD/LCU 1.278 1.262 1.253 1.237 Table IV-1: EIU consensus scenario for Austria 2012-2015 According to this scenario, GDP growth increases steadily from zero growth in 2012 to reach almost 2% in 2015. Inflation remains rather stable floating around an average rate of 2.3%, unemployment rate is very low at around 4% and the exchange rate between the US$ und the declines slightly from 1.28 to around 1.24 i.e. the Dollar is supposed to slightly appreciate to the Euro between 2012 and 2015. 10

Applying the macro-economic scenario to corporate firms in Austria shows the following impact on the financial ratios: Figure IV-1: Selected aggregated forecasts of corporate financial ratios for Austrian firms Total Liabilities per Turnover slightly decreases during the whole period 2013-2015 on average at 1.8%, which means that in the light of the still recovering economy Austrian firms manage their liabilities carefully so that their liabilities grow at lower pace than their turnover. Current Assets minus Current Liabilities per Total Assets as a measure for working capital grows strongly by 7.9% p.a. on average. A reason for this could be that Austrian firms will still remain risk-averse and will hence be about to further de-leverage themselves. Equity per Total Assets also grows over the period 2013-2015 but at a much lower rate of 3.6% p.a. on average, which means that firms are still profitable and prefer to retain relatively much of their after-tax profits because Total Assets grow on average by 0.8%. Cash per Total Liabilities also grows (on average by 2% p.a.) but the slope is different: growth remains at 3.5% p.a. until 2014 but then Cash per Total Liabilities drops by 1.1% in 2015. One driver is that Current Assets grow quite strongly which often goes against the cash position. EBT per Turnover drops in 2013 by 0.2 percentage points and in 2014 by 0.1 percentage points starting from a level of 3.2%. The good news, however, is that it stabilises in 2015 for the simple reason that pre-tax profits start growing again. All of these effects together cause the following evolution of credit risk relevant variables probability of default (PD), expected loss (EL) and risk-weighted assets (RWA): 11

Figure IV-2: Selected aggregated forecasts of corporate financial ratios for Austrian firms Interestingly, the yearly average of the PD slightly shrinks by an annual rate of 2.1%. This implies that average credit risk per company slightly declines. However, this does not imply that credit risk overall declines because due to the growth of credit exposure we see that both EL and RWA increase over time. As a consequence, total credit risk in Austria grows on average by 1% annually, which seems to be well bearable given the macro-economic scenario. V. Conclusion and outlook This study shows the structural difference in economic outlook and their consequences for corporate credit risk for the selected European countries. However, it also clarifies the efficiency in responses to the crisis, e.g. in the capital ratios of Spanish corporates. The quantified results as given here allow financial firms to manage their portfolios accordingly with regards to both risk limits and buffers as well as pricing and profitability management. CreditDynamix for the first time now enables financial and corporate firms to finally execute the paradigm shift in risk management that regulators and politicians called for in the past years away from a rear-view mirror approach and towards an understanding of the underlying connection between the macro-economy and detailed impact on a bank s credit portfolio. It hence will be one of the building blocks for a more stable financial sector: improved predictability will lead to improved stability not only of the banks themselves but also of bank profitability. 12

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