FSR Forum 15-03

15th Volume April 2013 issue #3

Credit Rating

Hard and soft information in bank-internal credit ratings

Interview S. Bus

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CFA, Executive Vice President

Column J. F. Slijkerman

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fsrforum • volume 15 • issue #3

Credit Rating

Preface

Dear reader, In front of you lies the third edition of the 15th volume of the FSR Forum. The theme of this edition is Credit Rating. A credit rating is given to a country, company or a structured asset and evaluates the credit worthiness. The credit rating is based upon the history of borrowing and repayment, as well as the availability of assets and extent of liabilities. We selected this theme because many rating agencies have downgraded the credit ratings of almost all Dutch banks.

Hopefully, after reading this edition you will know more about credit ratings, how they are structured and how they are applied.

As in previous editions this edition includes three scientific articles. These articles provide you with a theoretical framework on credit ratings. The first article is written by Lars Norden, Associate Professor of Finance at the Rotterdam School of Management, Erasmus University, and Academic Director of the MSc in Finance & Investments Programme. By analysing credit file data, he finds the evidence that the combined use of financial and non-financial factors leads to a more accurate prediction of future default events than the single use of each of these factors.

The second article is written by Michael Jacobs. In his article he writes about the committed revolving credit facilities that offer borrowers an option to draw funds up to specified limits as dictated by changing circumstances. In his study he builds upon a limited practitioner literature by empirically estimating the exposure at defaults from publicly available data and relating these to a set of variables predictive of realized the exposure at default.

The final article written by Ricard Cantor and Frank Packer explains multiple questions about sovereign credit ratings. They present the first systematic analysis of the determinants and impact of the sovereign credit ratings assigned by the two leading U.S. agencies, Moody’s Investors Service and Standard and Poor’s. Such an analysis has only recently become possible as a result of the rapid growth in sovereign rating assignments.

In the articles we explain the theoretical framework of credit ratings. For the practical side of credit ratings, we had an interview with mister Bus. Mister Bus is head of the Credit team and fund manager of Robeco High Yield Bonds. In this interview mister Bus talks about interesting sectors to invest in, rating agencies and many more subjects concerning credit ratings. We would like to thank mister Bus for his contribution to this FSR Forum.

As always a teacher has written a column especially for the FSR Forum. This time Jan Frederik Slijkerman has written the column in which he talks about that rating agencies have lost part of their credibility. He concludes that once one understands the methodology of agencies, one can determine if the rating is appropriate and one can disagree with the assigned rating.

In the remainder of this edition you will find the column of mister Groeneveld. In his column mister Groeneveld discusses the economy through various headlines and he elaborates the situation of Woonbron. Naturally this edition also includes the FSR News: which includes the News Update, the Former Board Member Column, the FSR Member column and reports about recent events.

2 • Preface

Finally I would like to make you aware of the fact that we are looking for our successors. A board year is of unprecedented value for your student life. You will represent the largest independent study association of the Erasmus University and organize major events and projects for Bachelor 3 and Master students. During a board year at the FSR, you will develop a highly valued set of skills and gain insight into the job market, making it easier to choose your own career path. Most importantly, you will get the chance to help a large group of motivated ­students to pursue their career in finance or accounting.

If you are interested, or if you want to know more about how a board year at the FSR will look like, make sure to contact us, either by sending an e-mail to chairman@fsr.nl or by dropping by at our office at H14-06.

Besides the options noted above, we are organizing a social drink especially for those who are interested in a board year or in participating in one of our committees next year. The drink will take place on the 18th of April at café ‘De Stoep’. For further information, please visit www.fsr.nl.

I hope you will enjoy reading this FSR Forum.

Sincerely, Maaike Lanphen Editor in Chief FSR Forum FSR board 2012-2013

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fsrforum • volume 15 • issue #3

Credit Rating

Table of contents

Hard and soft information in bank-internal credit ratings Lars Norden

Whereas the eligibility of hard information (financial factors) as inputs for internal credit ratings is widely accepted, the role of soft information (non-financial factors) in ratings remains ambiguous.

Analyzing credit file data, we find evidence that the combined use of financial and non-financial factors leads to a more accurate prediction of future default events than the single use of each of 6 these factors.

Empirical Analysis of Exposure at Default for Unfunded Loan Commitments Michael Jacobs

Michael Jacobs writes about the committed revolving credit facilities that offer borrowers an option to draw funds up to specified limits as dictated by changing circumstances. In his study he build upon a limited practitioner literature by empirically estimating EADs from publicly available data, and relating these to a set of variables predictive of realized EAD. 14

Determinants and Impact of Sovereign Credit Ratings Richard Cantor and Frank Packer

In this paper they explain multiple questions about sovereign credit ratings. They present the first systematic analysis of the determinants and impact of the sovereign credit ratings assigned by the two leading U.S. agencies, Moody’s Investors Service and Standard and Poor’s. Such an analysis has only recently become possible as a result of the rapid growth in sovereign rating assignments. 28

Colofon FSR FORUM appears five times a year and is an edition of the Financial Study Association Rotterdam KvK Rotterdam no: V 40346422 VAT no: NL 805159125 B01 ISSN no: 1389-0913 15th volume, number 3, circulation 1900 copies

4 • Table of contents

Editor in chief Maaike Lanphen Editorial department Petra van den Akker Roija Rasuli Editorial advisory Dr. M.B.J. Schauten Dr. W.F.C. Verschoor Drs. R. Van der Wal RA

With the cooperation of S. Bus R. Cantor Drs. J.G. Groeneveld RA RV T. van der Gugten M. Jacobs Dr. L. Norden F. Packer Dr. J. F. Slijkerman M. Vlasveld

Editorial address Editiorial office FSR Forum, Erasmus Universiteit Rotterdam Room H14-06 Postbus 1738, 3000 DR Rotterdam Tel. 010 408 1830 E-mail: forum@fsr.nu

Interview S. Bus

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Table of contents • 5

fsrforum • volume 15 • issue #3

Hard and soft information in bank-internal credit ratings1 Lars Norden

1 This article is a shorter and modified version of: Grunert, J., Norden, L., Weber, M., 2005. The role of non-financial factors in internal credit ratings. Journal of Banking and Finance 29, 509-531.

6 • Hard and soft information in bank-internal credit ratings

Abstract Bank-internal credit ratings have gained in importance because of their use for determining regulatory capital adequacy and banks’ increasing focus on the risk-return profile in commercial lending. Whereas the eligibility of hard information (financial factors) as inputs for internal credit ratings is widely accepted, the role of soft information (non-financial factors) in ratings remains ambiguous. ­Analyzing credit file data, we find evidence that the combined use of financial and nonfinancial factors leads to a more accurate prediction of future default events than the single use of each of these factors.

This paper explores the role of soft information (non-financial factors) in internal credit ratings. For this purpose we examine empirically whether the combined use of financial and nonfinancial factors leads to a more accurate prediction of default events than their single use. Our study has implications for both banks and bank supervisors: banks will be able to better understand the role of quantitative and qualitative factors in internal credit ratings and supervisors will be supported in claiming a credit rating based on hard and soft information to determine regulatory capital requirements (Basel Committee on Banking Supervision, 2006).

1. Introduction

2. Related research

Similar to capital market investors that rely on credit ratings provided by rating agencies, banks assign internal credit ­ratings to appraise the creditworthiness of their borrowers.

In both cases, ratings can be interpreted as a screening technology that is applied to alleviate asymmetric information problems between borrowers and lenders. Whereas external ratings from credit rating agencies have been well established since the beginning of the twentieth century, internal ratings were adopted increasingly by banks during the nineties (see English and Nelson, 1999; Treacy and Carey, 2000). Internal credit ratings for corporate borrowers are an aggregated ­valuation procedure of various financial and non-financial factors. In banking practice, ratings represent the basis for loan approval, pricing, monitoring, and loan loss provisioning.

While considerable research has proven the suitability of financial factors to predict borrower insolvency (see, for example, Altman, 1968), the role of non-financial factors remains ambiguous. Although consideration of non-financial factors such as management quality and industry perspectives is beyond controversy (see Basel Committee on Banking Supervision, 2000a, 2001; Günther and Grüning, 2000) there is a lack of quantitative research on this issue. With respect to these “soft” factors, bankers often refer to their experience and distrust the sole use of financial criteria. A first investigation of the importance of soft information in borrower-bank relationships is conducted

by Berger et al. (2002) and Stein (2002). Depending on bank size, Berger et al. (2002) explore a bank’s ability to act in projects that require the evaluation of soft information. They find that small banks are more capable of collecting and acting on soft information than large banks. Stein (2002) points out that decentralized banking hierarchies are likely to be more attractive when projects’ soft factors are to be evaluated.

Firstly, our analysis relates to the work on corporate bankruptcy prediction with financial factors (see Beaver, 1966; Altman, 1968; Altman et al., 1977; Ohlson, 1980; Platt and Platt, 1990; Baetge, 1998). These factors typically concern the capital structure, profitability and liquidity of a firm.

Models are based on linear discriminant analysis, on logit and probit regression analysis or, more recent ones, on neural networks. Because of their relatively high discriminary power, these models are widely accepted but they nevertheless show some disadvantages (see Basel Committee on Banking Supervision, 2000b, pp. 107-110). Few of them are based on a theory that explains why and how certain financial factors are linked to corporate bankruptcy. As financial factors are mostly backward-looking point-in-time measures, these models are inherently constrained and it is not clear how well these models perform out-of-sample (time, firm, industry etc.). This area of research is relatively well developed but still has to overcome the above mentioned problems.

Secondly, we briefly review research on banks’ internal credit rating systems which is still scarce but growing considerably.

It can be divided into an empirical and a normative part. On the one hand, empirical analyses of banks’ internal rating systems examine the structure and the use of ratings (see Elsas and Krahnen, 1998; Machauer and Weber, 1998; English and Nelson, 1999; Treacy and Carey, 2000; Crouhy et al., 2001; Ewert and Szczesny, 2001; Norden, 2002). These studies and an overview of international best practice rating standards in the banking industry (see Basel Committee on Banking Supervision, 2000a) show that internal rating systems are based on either statistical methods, constrained expert judgment-based techniques or exclusively expert judgments.

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fsrforum • volume 15 • issue #3

Whether the combined use of financial and non-financial factors leads to a more accurate prediction of default events than their single use.

These systems tend to include similar types of risk factors, typically a mix of quantitative and qualitative factors (e.g. leverage, profitability and liquidity ratios, management experience, industry perspectives). However, the weighting schemes of these risk factors differ considerably across banks. Ratings are used for loan approval, management reporting, pricing, limit setting, and loan loss provisioning.

Other studies analyze the frequency and the extent of banks’ rating disagreement for a given borrower (see Risk Management Association, 2000; Carey, 2001). In addition to the reasons given in these studies, it might be possible that differences in opinion on borrower quality result from a different evaluation of non-financial factors rather than from financial factors.

Using a similar kind of reasoning, Tabakis and Vinci (2002) compare and combine credit assessments of financial institutions from multiple sources (ratings of agencies, other credit assessment institutions and banks’ internal ratings). They develop a rating model that relies on two components: a “core part” which includes easily available and quantifiable financial data, and an “analyst’s contribution” which includes additional, more complex information.

On the other hand, Krahnen and Weber (2001) present a normative set of "Generally accepted rating principles" that points out the necessity of a link between credit rating and probability of default. Requirements concerning completeness, definition of default, monotonicity, back testing etc. of a rating system are developed. They describe credit ratings as being a "mixture of mathematical models and management intuition", but they say nothing about the risk factors, the factor weights and the value function to be included in a "good" rating. Based on the first and second consultation period and several of its own studies, the Basel Committee on Banking Supervision released a second Consultative Document in January 2001 and recently a third one in April 2003.

Both contain the proposal of an internal ratings-based approach for regulatory capital adequacy and include an extensive list of the normative requirements banks have to meet if they want to calculate regulatory risk weights based on their internal credit ratings.

Thirdly, Günther and Grüning’s (2000) survey reports that 70 of 145 German banks not only use quantitative but also qualitative factors in credit risk assessment, with management quality being the most important “soft” factor. 77.6 %

8 • Hard and soft information in bank-internal credit ratings

of these banks state that the additional inclusion of qualitative factors clearly improves default prediction. However, nothing is said about the degree of improvement. Hesselmann (1995) as well as Blochwitz and Eigermann (2000) incorporate qualitative variables (for example accounting behavior or ­discrete cover ratio classes) in discriminant analysis to differentiate between subsequently defaulting and non-defaulting German companies. They find that the use of qualitative ­variables improves the percentage of companies correctly classified. These results support the requirement of the Basel Committee on Banking Supervision (2001) that banks not only have to consider quantitative but also qualitative factors; for example, the availability of audited financial statements, depth and skill of management, the position within the industry and future prospects (see no. 265 in the second Consultative Document). Furthermore, analyses of quantitative and qualitative ratings using different sets of credit file data from German banks (see Weber et al., 1999; Brunner et al., 2000) show that qualitative ratings exhibit significantly better grades with less dispersion around their mean, that they change less often than quantitative ratings and that rating changes stem mainly from changes in the quantitative sub-ratings. They leave open the question of whether the important role of “soft” information in internal credit ratings is a desirable or problematic feature.

Given this literature, it becomes clear that the specific role of and interaction between hard and soft information in internal credit rating systems has to be analyzed in more detail. Soft information is usually derived from experts’ judgments and common industry knowledge but how much does it contribute to an accurate forecast of borrower quality? We intend to answer this question in the following sections.

3. Data Originally, our data on bank-borrower relationships was composed of two randomly drawn sub-samples (A and P) that contained credit file information from six major German banks for 240 borrowers from the period January 1992 to December 1996 (see Elsas et al. (1998) for a detailed description of the original dataset). In our study we eliminated bank 5 due to a lack of non-financial factors and bank 6 because of a small number of observations. For this reason credit file information from four banks remains in our data set. The population was restricted to medium-sized firms with an

annual turnover between EUR 25 and 250 million and a minimum loan size of EUR 1.5 million. To avoid the influence of the restructuring process in the eastern part of Germany, only customers of the western part were included. Sample A was randomly drawn from this population and sample P was randomly drawn from a sub-population which consisted of borrowers in financial distress during 1992-1996. We merged both sub-samples, controlling for a potential oversampling bias.

The meta rating scale with grades from 1 to 6 was created to make internal ratings comparable between banks (see Elsas et al., 1998). Grade 1 means very high, 2 high or above average, 3 average, 4 below average, 5 problematic and 6 highly distressed or defaulted. Some variables were not documented in the credit files because not all relationships lasted for five years and the creditworthiness of high quality borrowers was not checked annually but every second year at one bank.

In our analysis an observation consists of a borrower’s financial, non-financial, and overall rating and his or her default status in the following year. The variable DEF is an indicator for default events. Consistent with the definition given by the Basel Committee of Banking Supervision (2001), the variable DEF equals 1 if one or more of the following sub-events occur in the year following the one of the rating assignment and otherwise zero: moratorium, allowance of loan loss provisions, withdrawal of a credit, disposition of collaterals, liquidation, formation of a bank pool, recapitalization. The financial, non-financial and overall ratings are directly adopted from the original credit files of each bank and transformed accordingly to the overall rating on the meta rating scale. We obtain the non-financial factors (management quality, market position) directly from the credit files, whereas we have to compute the financial factors, some of them are integral parts of the financial rating, because only the underlying balance sheet items are in the dataset. These factors cover all categories of the C’s of credit (excepting collateral), a familiar credit analysis concept in commercial lending (see Collins, 1966). Dummy variables are created to control bank and year-specific effects. Table 2 shows the distribution of the default variable DEF by banks, years and overall rating classes. Panel A shows that default events are agglomerated at bank 2 but quite evenly distributed across banks 1, 3 and 4. Whereas panel B indicates a relatively even distribution of the default events across years, a monotonous

increase of the relative default frequency from rating class 1 to 6 can be observed in panel C. Table 3 displays descriptive statistics of different rating categories. The means of all three credit rating categories are higher for defaulters than for non-defaulters. This is a first indication of a robust relation between credit ratings and default status. The standard deviations of the different rating categories indicate that the dispersions of defaulters’ ratings are lower, which may be caused by the fact that default events occur mainly in the grades 5 and 6. Similar to the study of Weber et al. (1999), the standard deviation of non-financial ratings is lower than the one of financial ratings. Furthermore, non-financial ratings are significantly better at the 0.01-level than financial ratings using a Wilcoxon signedrank test. This means that on an average banks assess the quality of the management and the market position of their borrowers better than their financial situation.

4. Measuring the association between credit ratings and default events Our main objective is to investigate whether an additional inclusion of non-financial factors in a bank’s internal credit rating is beneficial or not. It can be deemed beneficial if it leads to a more accurate prediction of default events.

The purpose of a credit rating is to classify prospects and borrowers according to their probability of default over a given time horizon. As banks typically assign credit ratings for a one-year horizon (see Treacy and Carey, 2000), we analyze how different rating categories are related to the default status in the year following a rating assignment. For this purpose, we compare credit ratings assigned in the year t with the variable DEF (default in t+1).

Since descriptive statistics such as rank correlations or concordance coefficients support our hypothesis (the results are not reported here), we directly estimate probit regression models with DEF as dependent variable, and the financial rating FR (model 1), the non-financial rating NFR (model 2), and the overall rating OR (model 3) respectively as independent variables. In a preparatory analysis dummy variables for the financial, the non-financial and the overall rating were used.

As this specification basically yields the same results, we use the credit rating variables (coded on a scale from 1 to 6) in

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fsrforum • volume 15 • issue #3

the remainder. In each model we control for bank and yearspecific influences with dummy variables using bank 1 and year 1992 as reference categories.

Given that our sample includes a relatively high number of defaults due to the oversampling of a distressed sub-sample, we employ sampling weights in all subsequent regression models in order to correct for potentially biased coefficients.

Sampling weights represent the inverse of an observation’s probability of being included in the sample. In the estimation procedure, sampling weights put more weight on non-defaulters and less weight on defaulters in order to approximate the default distribution of the underlying population (see Zmijewski, 1984, and Ewert and Szczesny, 2001). We assume a population probability of default derived from the OECD data on loan loss provisions of German commercial banks, in which the rounded average probability of default for the years 1993 to 1996 amounts approximately to 2%. In addition, Moody’s Investor Service (2001) takes 1.6% for their RiskCalcTM model for German private firms. For robustness purposes, we also apply probability weights, assuming a population probability of default of 1.6%. The results for models 1-3 and regressions 1-2 are very similar to those obtained from 2%. Neither the regressions coefficients’ magnitude and significance, nor relative model performance change substantially. Therefore we stick to the previously assumed value of 2%.

The models can be evaluated by using different criteria (see Hosmer and Lemeshow, 2000; Deutsche Bundesbank, 2003).

We decided to use the McFadden’s R2, the Brier Score, the percentage of correctly classified observations, and type I and type II error rates as evaluation criteria because they represent an adequate mix of goodness of fit and classification accuracy measures. Since the conventional R2 cannot be calculated for probit and logit models, McFadden’s R2 (Pseudo R2) is employed. It is defined as 1 - (unrestricted log-likelihood function/restricted log-likelihood function). The Brier Score (BS) is a measure of prediction accuracy that is well-known in meteorology and medical science (see Brier, 1950). It is calculated as where θi is a binary indicator for the actual realization of the default variable (1 if default, 0 if no default) and pi is the estimated probability of default. The difference between the Brier Score and the percentage of correctly classified observations is that the former is more sensitive to the level of the estimated probabilities. The Brier Score

10 • Hard and soft information in bank-internal credit ratings

takes the estimated probabilities directly into account. Otherwise, the percentage of correctly classified observations transforms probabilities that are higher than a specific cutoff point to 1 and others to 0. In the following, predicted defaults are those observations with an estimated probability above 0.11, which is the cutoff point that maximizes the proportion of observations correctly predicted by model 1. It is important to mention that using the optimal cutoff point of model 1 is conservative because predictions of model 2 and 3 will be based on the same value. If, for example, model 3 outperforms model 1 even in this setting, it certainly will with its optimal cutoff value.

Type I error is the percentage of observations classified as “non-default” but which actually did default. Type II error is the percentage of observations classified as “default” that actually did not default. Note that in commercial banking the type I error is more important than the type II error because of its higher costs. We compare the accuracy measures with those of a naive forecast and between models. The Brier Score of a naive forecast is calculated by taking the average relative default frequency (ADF) of the entire sample as a default probability for each individual observation.

Regression results and evaluation criteria for models 1-3 are presented in table 4. All three rating variables have, as expected, positive coefficients and are significant at the 0.01level. The coefficients of the rating variables are highly economically significant indicating the strong relation between future default events and credit ratings. With respect to the dummy variables, bank 2 and bank 3 have significant influence on the prediction of default, which is consistent with the fact that these two banks show higher average default frequencies than the two other banks. None of the year dummies are ­significant at the 0.05-level, which is consistent with the relatively even distribution of default events over time. Note that all models are more accurate than the naive forecast, which leads to a Brier Score of 0.1402. The model evaluation results shown in panel B show that model 3 is superior to models 1 and 2 with respect to all criteria. We find that the Brier Score of model 3 is significantly lower than the ones of both other models. Moreover, the type I error rate of model 3 (0.4058) is lower than the one of model 1 (0.5217). For comparison, the logit default prediction model of Carey and Hrycay (2001), which is based on four financial factors,

This is a first indication of a robust relation between credit ratings and default status.

p­ roduces a type I error of 0.68 in the sample and 0.65 out of the sample. This means, in terms of economic significance, that a bank that relies on a model 3-type rating system will incur considerably less losses (roughly 10%) due to erroneously accepted borrowers that subsequently default.

rate) we obtain values in the range 0.42 – 0.69 (0.26 – 0.40).

These results indicate that biased coefficients do not represent a serious problem for most of the evaluation criteria. Moreover, the reported confidence intervals support the previously found ranking of the three models.

Although we control for an oversampling bias, probit estimates could be biased because of violated distributional assumptions or correlated regressors and error terms. To address this concern we apply the bootstrap methodology (see Efron, 1979) to the different evaluation criteria. Bootstrapping is a general resampling technique that helps to answer the question of whether sample statistics or estimated regression parameters are biased. It does not depend on specific distributional forms and can be implemented with Monte Carlo sampling. The only condition to be fulfilled is that the sample has to be representative. As explained above, the latter is met by estimating all regressions with sampling weights. Subsequently, we generate an empirical estimate of the sampling distribution of each evaluation ­criterion in the following manner:

Furthermore, the bootstrap procedure enables us to verify the number of cases in which model 3 exhibits a better fit than model 1. Using a diagram in which McFadden’s R2 of model 1 is indicated on the horizontal axis and that of model 3 on the vertical axis, we obtain 1000 dots of comparison pairs. Note that McFadden’s R2 of model 1 and 3 stem from different regressions that are estimated with the same observations. The 45° line indicates model pairs of equal goodness of fit. Figure 1 illustrates this analysis. As a result, model 3 displays in 997 of 1000 cases, a better goodness of fit which is clear evidence for our hypothesis. Likewise, we can compare the bootstrap Brier Score of model 1 with that of model 3.

Figure 2 strongly supports our hypothesis because the Brier score of model 3 is lower than that of model 1 in each of the 1000 cases, indicating that the use of the overall rating – instead of the pure financial rating – results in a higher predictive accuracy. Finally, plotting the bootstrap values of the percentage of correctly classified observations of model 1 against that of model 3 in figure 3, we obtain an alternative impression of the prediction accuracy of the models. The ­pattern of dots in figure 3 mirrors the fact that the number of correctly classified observations is integer because the cutoff value transforms fitted probabilities into binary predictions (1 if default, zero otherwise). Therefore, the percentage values cluster on the corresponding integer number of predictions. Model 3 leads in 979 of 1000 cases to a higher number of correctly classified firms than model 1.

1. Random draw of 409 observations with replacement from the original sample 2. Estimation of models 1-3 in the way described above using the sample drawn in step 1 3. Evaluation of each model’s performance using the same previous criteria 4. Independent replication of steps 1 to 3 for 1000 times Placing a probability of 1/1000 at each point, we obtain a ­relative frequency distribution of each criterion which represents a non-parametric estimate of the sampling distribution.

Using this bootstrap distribution we can determine the bias, calculated as the average difference between the bootstrap estimates and the observed statistic (as shown in table 4), its standard deviation and confidence intervals. Efron (1982) suggests that when the ratio of the bias to its standard deviation is less than 0.25, the bias is not a problem as the random error will surpass it. Table 5 presents results. In most of the cases the bias is relatively small in comparison to its standard deviation. For example, calculating each model’s ratio of the bias to the estimated standard deviation for the Brier Score (observations correctly classified) yields a range of 0.14 - 0.2 (0.14 - 0.18). However, for the type I error rate (type II error

A similar way of interpreting model performance is analyzing the differences between the bootstrapped evaluation criteria.

Fur this purpose, we calculate for each bootstrap run the pairwise difference between the evaluation criteria of model 1 or 2 and model 3. From the distribution of differences we obtain the probability of model 3 performing worse than the two other models. The results of the different evaluation ­criteria are presented in table 6. It turns out that model 3 clearly dominates the other two models in terms of McFadden’s R2, the brier score and the percentage of correctly classified observations, as the probability of observing the opposite is

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Will incur considerably less losses (roughly 10%) due to erroneously accepted borrowers that subsequently default.

very small. With respect to type I and II error rates, model 3 is still, but to a lesser extent, superior. Note that this deterioration of performance should not be overstated since it can be a consequence of the fact that type I and II error estimates are relatively more biased than the other evaluation criteria estimates.

Summarizing, a model that includes both financial and nonfinancial factors leads to a significantly improved prediction of default events than a model that is solely based on either factors or naive forecasts. Various robustness tests confirm that our result is not sensitive to the omission of any bank from the sample, robust on the individual level for two banks, not driven by predetermined weighting schemes, not dependent on whether firms default once or more times, and does not seem to be biased by a reporting lag.

5. Conclusion Over the past twenty years, banks’ uses of internal credit ratings have substantially multiplied. Also, internal rating systems are recognized by banking supervision authorities to determine banks’ capital adequacy (Basel II and Basel III). Given this wide-spread use of credit ratings, the design of sound rating systems is in the interest of banks, borrowers, and supervisors. Whereas the relevance of hard information (financial factors) for rating purposes is widely accepted, the consideration of soft information (non-financial factors) is equally beyond controversy but it has often only holistically been justified.

Our study constitutes a first attempt to explore the role of non-financial factors in credit ratings. Our main result is that the combined use of financial and non-financial factors leads to a significantly more accurate default prediction than the single use of financial or non-financial factors. Default is defined consistently with the definition of the Basel Committee on Banking Supervision; goodness of fit and accuracy of default prediction is measured using McFadden’s R2, the Brier Score, the percentage of correctly classified observations and type I/II error rates.

Although our results are limited in some ways due to the data used, they essentially confirm banking practice and show that holistic justifications for the use of non-financial factors can be confirmed by a quantitative analysis. However,

12 • Hard and soft information in bank-internal credit ratings

since only the benefits of non-financial factors have been analyzed, it is not possible to conclude that their additional use represents a net advantage because we have not examined the costs of acquiring and processing non-financial information. This is left to future research that should proceed with an integrated cost benefit analysis of internal credit rating systems on the individual bank level. Additionally, in particular for pricing issues, it might be instructive to study whether non-financial factors in credit ratings can improve the differentiation between those borrowers disposing of an acceptable degree of creditworthiness. Finally, future research could investigate whether and to what extent there is a relationship between multiple lenders’ rating disagreements for common borrowers, borrower bargaining power, and non-financial factors in credit ratings (e.g., Grunert and Norden, 2012).

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fsrforum • volume 15 • issue #3

Empirical Analysis of Exposure at Default for Unfunded Loan Commitments

By Michael Jacobs, Jr.1

14 • Empirical Analysis of Exposure at Default for Unfunded Loan Commitments

1. Introduction and Summary Committed revolving credit facilities offer borrowers an option to draw funds up to specified limits as dictated by changing circumstances. A great interest in analyzing revolving lines of credit (or unfunded commitments) stems from the unique characteristics relative to other traditional credit or fixed income products, which have implications for pricing and credit risk management. Revolving lines appeal to a ­clientele of borrowers with particular financing strategies (e.g., working capital needs or CP backup for investment grade obligors). There is an attractive return profile from investors’ perspective, as interest and fees on the drawn, and annual fees on the undrawn amount, lead to a high all-in return on invested capital. There is the opportunity to invest in high quality borrowers not otherwise in the loan market.

Finally, there is potential relief on regulatory capital relative to similar investments.2 However, these instruments present a great challenge in valuation and risk management, as estimates of revolving credits facility’s expected usage at default of the borrower need to be formed. We term this quantity the exposure at default (EAD), or the loan equivalent exposure, and recognize this to be a key parameter in estimating expected loss and credit risk capital for unfunded commitments.

A related quantity is the loan equivalency factor (LEQ), which is the proportion of the undrawn commitment that is drawn down upon in the event of default. Financial institutions have a great interest in estimating such quantities from their internal histories, to parameterize credit risk models, as well as to satisfy regulatory requirements. In this study we shall build upon a limited practitioner literature (Asarnow and Marker (1995), Araten and Jacobs (2001)) by empirically estimating EADs from publicly available data, and relating these to a set of variables predictive of realized EAD. As noted in the previous literature, there exists an adverse selection problem in the context of revolving commitments: if a borrower’s credit quality improves, the ability to pay down or negotiate better pricing increases, while when deteriorating there is the tendency to drawdown on the unused portion of the commitment. Various risk mitigants are often in place to cope with this. There are upfront fees to deter prepayment, not the practice for most other credit products.

Covenants are often in place to obtain amendment fees or improved pricing if borrower’s credit quality worsens.

Finally, step-up pricing, or higher undrawn (drawn) spreads

fees for lower credit quality (increased usage), help deter this.

In most applications, it is assumed that the current outstanding is outstanding at default (i.e., the LEQ factor may not be negative) and that the current commitment will not be higher at default (i.e., the LEQ may not exceed 100%), but this is not supported in any of the available empirical evidence.

Furthermore, typically banks assume that EAD does not depend on factors other than an obligor’s credit rating and perhaps the time horizon under consideration, and in some cases there may be only a single EAD assumption by broad product type, but there is limited evidence to support this. It may be the case that alternative variables may be predictive of the EAD (e.g., expected LGD, current usage, size of ­commitment, borrower financials, etc.)

As defined herein, the credit conversion factor (CCF) factor is the multiple of the current utilized amount, while the loan equivalent exposure (LEQ) is the proportion of the current undrawn amount, expected to be outstanding at the time of default. CCF has the potential advantage of incorporating not requiring a positive unused amount at the point of observation prior to default, which is particularly advantageous with respect to certain products for which systems are unlikely to report this or uncommitted lines (e.g., letters of credit).

Alternatively, one may estimate a factor to be applied to the total committed amount, which we call the exposure at default factor (EADF). LEQs, CCFs and EADs may be estimated either directly or indirectly. The direct method starts from a universe of defaulted facilities and traces utilization and credit quality changes going back in time. The problem in this is paucity of data for investment grade obligors. The indirect method studies changes in utilization rates with changes in credit quality for the entire universe of revolving credits, extrapolating to the better risk ratings. A hybrid of these approaches combines information in migrations short of default with observations of default. While in this study we restrict attention to the direct approach, in line with Basel II advanced IRB requirements, one can imagine extensions beyond the defaulted universe. Also, while one may imagine more general approaches to measuring EAD risk that better incorporate the joint dynamics of utilization and commitment as borrowers approach default, in this study we limit ourselves to these three traditional approaches.

»

Empirical Analysis of Exposure at Default for Unfunded Loan Commitments • 15

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It may be the case that alternative variables may be predictive of the EAD.

The exercise of empirically inferring EAD risk measures is fraught with practical, as well as conceptual, challenges. There is the question of defining the optimal estimator of a quantity that may have different meanings depending upon the context. The sampling methodology may also be subject to discretion. The issue of the treatment of outliers and extreme non-normality is pronounced in this setting, which calls into question the validity of standard statistical inference and predictive econometric techniques. Various ad hoc methods of dealing with the latter (e.g., winsorizing or capping / flooring of estimates) lead to further doubts about the efficiency and consistency of this methodology.

2. Econometric Models Various techniques have been employed in the finance and economics literature to classify data in models with constrained dependent variables, either qualitative or bounded in some region.

However, much of the credit risk related literature has focused upon qualitative dependent ­variables, which the case of PD estimation naturally falls into. Maddala (1981, 1983) introduces, discusses and formally compare the different models.3 We follow a variation of that framework here in the case of EAD.

3. Data and Summary Statistics We are working with probably the most extensive loss severity database of defaults (bankruptcies and out-of-court settlements) and recoveries, Moody’s Ultimate Recovery Database™ (February 2008 release; “MURD”). Most of the issuers in MURD have rated instruments (S&P or Moody’s) at some point prior to default, and traded equity, largely representative of the U.S. large corporate loss experience. We have merged MURD with various public sources of information (www.bankruptcydata.com, Edgar SEC filings, LEXIS/NEXIS, Bloomberg, Compustat and CRSP). The release that forms the basis of our research database contains data on 3,886 defaulted instruments from 1985-2007 for 683 borrowers, for which there is information on all classes of debt in the capital structure at the time of default. All instruments are detailed by facility type, seniority ranking, collateral type, position in the capital structure, original and defaulted amount, resolution outcome, instrument price or value of securities at the resolution of default (emergence from bankruptcy, Chapter 7 liquidation, acquisition or out-of-court settlement). The latter includes either the prices of pre-petition instruments at the time of emergence from bankruptcy or new instruments received in settlement of bankruptcy or other distressed restructuring.

In a sub-set of observations, we can obtain the price of traded debt, the equity prices or financial statement data at around the time of default. A smaller sub-set of observations considered in this study consist of revolving loans, for which we can trace the outstanding amounts, limits and ratings in SEC filings (10K and 10Q reports). This subset of MULGD includes 496 obligors, 504 defaults and 544 facilities.

4. Estimation Results Table 1 from Jacobs (2010) tabulates the estimation results for the beta link generalized linear model (BLGLM). Various sets of independent variables were analyzed in a series of univariate and multivariate regressions. The final set chosen for each EAD risk measure was determined based upon a partially quantitative, and partially judgmental, process that weighed the following (sometimes competing) considerations. First, measures of in-sample model performance, predictive accuracy (or goodness-of-fit) such as McFadden Pseudo-Rsquared (MPR2) and log-likelihood

16 • Empirical Analysis of Exposure at Default for Unfunded Loan Commitments

(LL), versus a Spearman rank correlation (SRC) measure of rank ordering (or discriminatory) accuracy. Second, we consider the signs and significance levels of independent variables.

Finally, there is an attempt to find a parsimonious representation that has a large number of variables in common across models.

Results are broadly in line with the univariate analysis, and generally consistent across EAD risk measures. The strongest result that emerges, generally ranking highest in statistical significance (in terms of p-values, or PVs) as well as partial effect (PEs) magnitudes, is the inverse relationship between UTIL (percent utilization) and two of the EAD risk measures. However, UTIL does not enter the EADF model; rather the CR (cutback rate) enters this model and not the others. While all parameters are directly related with the UNDRN (the undrawn amount in dollars), the partial effects are all rather small, and significance is only marginal in the case of EADF. On the other hand, the DRWN (the drawn amount in dollars) only enters the model for CCF, and while highly significant, the economic significance is questionable; this too is in line with the negative univariate Spearman correlation. However, LEQ had a reasonably sized negative correlation with DRAWN in that analysis, but this did not enter the regression model; while the correlation with EADF was the opposite sign, it was rather small.

Let us know consider the key variables of the Araten el al (2001) study, the TTD (time-to-default measured in years) and ORR (the obligor risk rating measured as dummy variables). We see consistently across models that TTD is directly associated with EAD risk, but that the relationship is stronger for LEQ and CCF, having much higher PEs, and much lower PVs, as opposed to EADF. ORR has negative coefficient estimates across all EAD risk measures. However, in some instances they are only marginally or not statistically significant, such as rating CCC-CC in the CCF model, or ratings AAA-BBB and BB in the EADF model. Generally, the pattern in the magnitudes of the partial effects is decreasing as ratings worsen, albeit non-monotonically.

Now let us consider financial ratio variables. Five of six dimensions of the ratios from the univariate analysis survive in the multivariate regressions (measures of leverage, size, liquidity, intangibility and profitability), and have signs consistent with such across all 3 models; whereas a measure of cash flow does not enter any of the models. LTD/MVE, or leverage as measured by the ratio of long-term debt to the market value of equity, is negatively related to EAD risk and at least marginally significant in the LEQ and CCF models. However, in the EADF model LTD/ BVE, the accounting measure of leverage (long-term debt ratio to book value of total assets) enters. This is in line with the univariate results, and consistent with our hypothesis that more highly levered firms may be under closer scrutiny, and hence less able to draw down on unused lines. The BVTA measure of company size (the logarithm of the book value of assets) has positive coefficients across all models; however, it is only highly statistically in one of the models, marginally significant in another model and just short of significant in the third model. This result is what we saw in the correlation analysis, and may be explained by a tendency of banks to monitor larger companies less intensively, as they may be perceived as less likely to require use of their lines. The CR liquidity measure, as in the univariate analysis, is consistently negative across models, in line with the univariate correlation analysis; but it is marginally significant in 2 of the models, and only significant at the 5% level in the LEQ model. As alluded to before, this may be considered a reasonable result, as less liquidity constrained firms may draw

 Empirical Analysis of Exposure at Default for Unfunded Loan Commitments • 17

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less aggressively on their lines as they approach distress. Similarly, the PM (profit margin) profitability measure is consistently negative across models (albeit with small PEs), in line with the univariate correlation analysis, and the expectation that less unprofitable firms on their way to default may pose lower EAD risk; but it is significant at the 5% level in only 2 of the models, being just marginally significant in the EADF model. Finally for the financials, the INTA measure of intangibility (ratio of intangible to total assets) enters only the LEQ and EADF models, having positive PEs, as well as moderate significance levels.

Now let us discuss results regarding measures of instrument-level characteristics. The COLL measure of collateral quality is present in all, and the CRED measure of seniority in none, of the regressions, consistent with the larger univariate correlations observed in the former as opposed to the latter. While the signs of the coefficient estimates for COLL are positive across models, only for LEQ do we observe high significance, while for CCF significance is just at the 5% level, and for EADF we are just shy of significance at the 10% level. Second, the CUSH measure of tranche safety attributable to the revolving credit is inversely related to EAD risk across regression models, as in the univariate analysis. In this case, PEs are relatively strong, as compared to some other variables. In this case, significance levels are also notably high, at much better than the 1% level in 2 cases, and at the 5% level in another. These results suggest that while superior collateral does seem to mitigate EAD risk, above and beyond this there is a beneficial effect to be had from greater debt cushion.

Among capital structure variables, only the PERCBNK (percent bank debt) and the PERCSEC (percent secured debt) enters the leading regression models. In the case of PERCBNK, which enters all models, coefficient estimates are economically significant and of positive sign, in line with the observed correlations. However, while highly statistically significant for LEQ and EADF, this variable barely achieves such status in the CCF model. Nonetheless, this result has a rationale in a story that when more banks are present in the creditor group, there may be coordination problems (e.g., this may be associated with a larger syndicate). Alternatively, through the economic incentives of banks at the top of the capital structure, the optimal foreclosure boundary may be set higher than otherwise (Carey and Gordy, 2007), and to the extent that lower LGD rates may be associated with this, an inverse correlation with EAD (if we believe that a tradeoff exists) may be consistent with the empirical result that we are finding. On the other hand, PERCSEC appears in only two models, LEQ and EADF, significantly and having positive signs.

The final variable that we consider is the measure of the economic cycle that made it into the final regression models, the MSG12MTDR (speculative default rate). This is expected, as the univariate Spearman correlations for MSG12MTDR were generally higher than for MAC12MTDR and SPR across all EAD risk measures. We have evidence of counter-cyclicality, as all partial effects are negative. However, in only the EADF model do we have a high degree of statistical significance; whereas in the LEQ and CCF models, significance is marginal. Therefore, we can regard this as only limited evidence against “downturn EAD” or for a counter-cyclicality in EAD risk. If we are willing to put some stock in these results, what economic rationale could be put forward? We could ascribe this to an “LGD-EAD” tradeoff: as the cycle turns downward and banks anticipate both higher default and higher recovery risk, they clamp down on revolving credit exposures, thereby reducing EAD even as loss severities may be rising.

18 • Empirical Analysis of Exposure at Default for Unfunded Loan Commitments

More highly levered firms may be under closer scrutiny, and hence less able to draw down on unused lines.

Finally, we discuss in-sample measures of model quality in Table 8 of Jacobs (2010), measures of predictive accuracy (Log-Likelihood Ratio – LLR and McFadden Pseudo R-Squared – MPR2), discriminatory power (Spearman Rank correlation between actual and predicted values – SRC) and in-sample forecasting ability for dollar EAD (Mean Squared Error of forecasted dollar exposure at default, or MSE-EAD). The LLR and MPR2 are standard diagnostics assessing in-sample fit in non-linear models, having potentially non-normal errors. The SRC here is meant to mimic the Area Under the Receiving Operating Curve (AUROC) or Accuracy Ratio (AR) statistics calculated in binary dependent models, such of probability of default (PD) prediction, and is in fact a generalization of the concept. The MSE-EAD measure is a bit non-standard, in that instead of focusing on how the models can predict or rank order the EAD risk measures, we focus on how the predicted parameters can forecast dollar EAD. Indeed, in the final specifications we observe that most variables are statistically significant, although it is about evenly split between very high levels of significance, and in some cases only marginally significance. However, there is much variation amongst in-sample performance measures, and we see in Table 8 that by these the CCF model performs best, and the EADF model ranks worst: the former model has the highest MPR2 and highest SRC, as well as the smallest p-value on the likelihood ratio test. However, in the exercise of forecasting the dollar EAD based upon these measures, in terms of MSE-EAD the LEQ measure performs best, followed closely by CCF, while EADF performs far worse than the other two, the latter underperforming by about a factor of 100.

Table 2 and Figures 1-2 of Jacobs (2010) present the results of an out-of-sample and out-of-time analysis, which serves as a rigorous validation of the models estimated herein. The bootstrap exercise proceeds as follows: in the first set of cohort years constituting about half the sample (1985-1997), observations are chosen at random with replacement, and the models are estimated. Then the predictions of the models are evaluated in the next year (1998), upon a similarly chosen sample, and this is repeated several times (10,000 replications). Next, a year is added to the estimation set (1998), and this is repeated, predicting EAD risk in bootstrapped 1999 samples based upon bootstrapped samples in the sub-set of years 1987-1998. This is done for 10 years (1998 through 2007), and the results are pooled to create 100,000 out-of –time and out-of-sample observations. The distributions of two model performance statistics, the MPR2 and SRC, are then studied. Table 9 of Jacobs (2010) shows some basic distributional statistics for these measures, while the full distributions of these are compared in Figures 5 and 6 (for MPR2 and SRC, respectively).

We observe that the ordering of the model performance in-sample is not preserved out-of-sample and out-of-time, as now the CCF model is not best by both measures, but now (by looking at the medians of the bootstrapped distributions) LEQ is slightly better by MPR2, and CCF far superior by SRC. However, the distributions of these statistics shifted the furthest to the left for the EADF model and it remains ranked consistently last. However, upon examination of the low and high percentiles from Tables 8, as well as the range of the distributions in Figures 5 and 6, we observe that performance in the CCF model also exhibits the greatest amount of variation out-of-sample for both statistics.

» Empirical Analysis of Exposure at Default for Unfunded Loan Commitments • 19

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The surprising shapes of these distributions highlights the dangers of using finite sample approximations to deriving test statistics for the model performance measures.

Examining the distributions in Figures 5-6 in conjunction with the high / low quantiles in Table 9 of Jacobs (2010) tells a more complete story. First considering MPR2, we see that the distribution for LEQ is closest to bell-shaped, but the other two are multi-model and have longer right tails. Indeed, while CCF has the highest numerical standard error, and the numerical standard errors of EADF is almost the same as LEQ, we see that in many states of the world EADF performs better by the MPR2 measure. We see a similar comparison for SRC in Figure 13, that while EADF has the lowest measure of central tendency and numerical standard error, it has a right tail far elongated relative to LEQ or CCF. The surprising shapes of these distributions highlights the dangers of using finite sample approximations to deriving test statistics for the model performance measures. Indeed, this implies that in 1 out of every 20 years of using this model on a holdout sample and re-rebuilding annually (this exercise being meant to mimic the way a practitioner would implement such models), MPR2s would fall into the single digits and SRCs would be around 20% for all models, which by industry standards would be considered dismal performance.

Notes

1 Corresponding author: Senior Manager, Deloitte and Touche LLP, One World Financial Center, New York, NY 10281, 917-324-2098, mikjacobs@deloitte.com.

The views herein are those of the author and do not necessarily represent the views of Deloitte and Touche LLP nor of and any of its affiliates within the umbrella of Deloitte and Touche Tomatsu.

2 Under BIS 1, no regulatory capital was charged on maturities less than one 1year, and only a 50% risk weight was charged for maturities 1 year or longer.

3 Also see Ohlson (1980), Lo (1986), McCullagh and Nelder (1989) and Firth (1991).

4 The paper can be found at http://michaeljacobsjr.com/Jacobs_EmpiricalStudyOfEAD_JASF_I-1_Summer_2010_31-59

20 • Empirical Analysis of Exposure at Default for Unfunded Loan Commitments

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fsrforum • volume 15 • issue #3

Interview with Sander Bus Executive Vice President at Robeco

Petra van den Akker en Roija Rasuli

What does your position at Robeco entail?

I am head of the credit team and fund manager of the high yield investment fund. As head of the credit team I manage ten credit analysts and six portfolio managers. As fund manager of the high yield investment fund it is my job to outperform other fundmanagers within the high yield bonds category.

At Robeco we are highly active at investing in high yield bonds worldwide. Currently we manage 3.3 billion Euro, which makes us one of the biggest high yield funds in Europe. We have been present from the start, in 1999, when the European high yield market was emerging.

Now the market is booming. The market is worth over 200 billion Euro in Europe. In the U.S. the market is worth over 1,000 billion Euro. The market is growing rapidly and the transactions are huge. This market grows mainly because we see disintermediation occurring. Companies cut the middleman and go to capital markets directly to get credit rather than to banks.

How is it possible to profit from high yield bonds?

Sander Bus, CFA, Executive Vice President, Portfolio Manager Robeco High Yield Bonds Mr. Bus is head of our Credit team and is fund manager of Robeco High Yield Bonds. Prior to joining Robeco in 1998, Mr. Bus worked for Rabobank as a fixed income analyst for three years. Mr. Bus holds a Master's degree in Financial Economics from Erasmus University, Rotterdam. He became a CFA charter holder in 2003 and is registered with the Dutch Securities Institute. Mr. Bus has been active in the industry since 1996.

High yield bonds are better known as junk bonds. Corporate bonds with a rating lower than BBB- are non-investment grade and are awarded junk bond status. Investors can profit from bonds by comparing the spread with the quality of the investment. The spread is the difference in yield between the company’s bond and the underlying government bond with the same maturity. As a bond goes deeper into junk bond status the issuer of the bond is less likely to repay its interest and principal payment. This increases the spread and the yield on the bond.

The higher yield is a reward for higher risk since the issuer of the bond is less likely to repay its interest and principal payment. What I search for as an investor is a mismatch between the quality of the company and what the market price suggests. Phrased differently, whether the risk that is priced corresponds to the risk observed by our analysts. If at Robeco we observe the risk is less than what the market suggests we can earn a return by buying this bond. By buying the bond we take credit exposure to the issuing company. We receive a high interest rate for the risk as we believe that the market overestimates the risk that the bond issuer will not be able to repay.

In order to profit from such a mismatch we need to have the information and seal the deal faster than our competitors such as Aegon or ING. Markets are relatively efficient, but it’s active managers like us that make markets efficient and cause information to be incorporated in the price quickly.

We have just learned how to profit from bonds: compare the actual quality of the company with the quality suggested by the market. How at Robeco do you determine whether quality matches price?

At Robeco we have ten credit analysts who all have their own sector. Every analyst has 40 companies in his portfolio to analyse. It is the analyst’s job to determine whether there are attractive investments in his portfolio based on the mismatch in quality and price. The analyst has to determine the quality of the company. Several factors are taken into account: business risk, financial risk, strategy of management, bond documentation, country risk, and sustainability factors.

For business risk one can think of factors such as which products the company makes, market position, market size, growth, and substitution risk. The strategy of management is also taken

22 • Interview

We purely talk to management to hear them out on strategy, planning, and to gain confidence in management because we want to gain performance for our clients.

under review. It is important to know whether the company is aggressive in mergers and takeovers for instance, and whether the company is shareholder-oriented or do they also take into account the interests of debt holders. As a debt holder I prefer companies to be very careful with cash and that they are conservative in distributing dividends. Debt holders clearly have different interests from shareholders.

We also look at bond documentation. This is a document that lays out how the bond is constructed exactly, what rights the bondholders have and what obligations the company has towards bondholders. It is a very thick document of sometimes a thousand pages. It contains very important information about legislation and where exactly your money is going.

Another important factor is country risk. When a company goes bankrupt you want to be sure to receive your recovery and that you can liquidate the assets. Bankruptcy rules differ across countries. If you lend to a Russian company, for instance, you will be a lot less likely to get your money back when a bankruptcy occurs than in the Netherlands because the rules are poorly documented. As investors we have to charge a margin on that country risk for us to buy that bond.

So a Russian company needs to offer a much higher interest rate for us to decide to invest. Sustainability factors are also taken into consideration. We call them ESG factors: Environmental, Social, and corporate Governance factors. Bonds are long-term so it is important the company can generate sustainable free cash flows. Once a company strides against corporate governance rules, or consistently pollutes the environment you know that it is not sustainable. Even if such companies generate great cash flows now, sooner or later you will get claims in accordance with stronger rules that will cost you profitability.

An example of this is coal-fired power stations, which produce a lot of CO2. An investor should not invest in companies with poor ESG factors unless you get a really high yield.

Finally, analysts construct a financial analysis based on all previously mentioned factors. The financial analysis is a prognosis of the future, where you as a fixed income investor are particularly interested in whether the company is able to generate sufficient free cash flows to repay its interest and principle. We look ahead for five to six years and see what we think will happen with the leverage. If leverage goes down, the next question is whether the credit quality will increase, and will the rating go up to which the spreads will eventually converge and are we subsequently able to make some performance?

What are interesting sectors to invest in?

In general sectors that are interesting to invest in are sectors in which companies are able to generate cash flow even when there is little economic growth. These are companies that have market power, price setters. They are independent of commodity markets. For instance, the packaging industry is a nice sector to invest in. There is relatively little competition and companies are able to maintain good margins, partly because packing companies deliver to the food industry, which is fairly stable and not cyclical. Another nice sector in Europe is the cable industry because there are very high cash flows. For instance, in the Netherlands we have Ziggo and UPC, companies with a high yield rating. They are high yield because of their high leverage. 4.5 times EBITDA worth of debt is on their accounts, while such a company is usually worth around 9 times EBITDA. Half of the company is financed with debt. However, in principle, they have enough free cash flow to keep servicing their debts so as a high yield investor this is an interesting sector. As a consumer you might have a different point of view.

When it turns out a company might not have enough free cash flow to repay bondholders, does Robeco step in to help these companies generate more cash flow?

Well, we are not advisors. We purely talk to management to hear them out on strategy, planning, and to gain confidence in management because we want to gain performance for our clients. It is important to know who you lend your money to.

Contact with management teams is very important to judge whether the company is an interesting investment opportunity.

However we do not guide companies to perform better on the verge of downgrade, we would simply sell this investment.

We do start a dialogue when a company scores badly on the previously mentioned ESG factors. We have a separate engagement department that tries to put pressure on companies to improve on ESG factors when they are unacceptably poor.

Robeco solely does this from a societal point of view. This happened, for instance, for Wal-Mart when they treated their employees badly. By talking to the management of Wal-Mart the engagement department tries to change the business culture. In the extreme case when that does not work we have to put such a company on the exclusion list, which means we do not invest in them anymore.

 Interview • 23

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What would your advice be to private investors who are interested in bonds?

As a private investor I think you need to be very careful with investing in bonds on your own initiative. Government bonds are safe if you go for Dutch or German bonds. For corporate bonds you need to be very careful. There are very few corporate bonds available for private investors to invest in, in the first place, since most bonds are traded over-the-counter.

And when things go wrong you lose a lot of money. When things go well you only receive interest and the principal.

Bonds are not anything like stocks, where you can double your payoffs. In a bond portfolio it is very important to diversify. In a portfolio of 100 bonds you might get 3 per cent extra interest over government bond interest. At a certain point one of your 100 bonds might go bust and you maybe lose 0.25 per cent on that one. What you end up with is still a good yield. If a private investor only buys one or two bonds and one of the two happens to go bust you lose a lot of money.

Only private investors with a lot of cash are capable to diversify a bond portfolio sufficiently and even then you need to hire analysts. In sum, they are better off investing their money in Robeco’s high yield fund.

What role do ratings play for credit investments?

A rating portrays the probability of default. Its most important use is as a risk benchmark. It is not important for your investment decision. We do not fare blindly on ratings. A rating might be wrong. A CCC rated company might perform better than a B rated company, while the rating would suggest otherwise. This is because rating agencies lag behind a halfyear to one year on the market. Ratings are mainly backward looking and are revised only when there is substantial evidence the rating should be revised. Once the rating is revised the market impact is not visible anymore. That is why ratings are not so important for investment decisions. Investors try to look forward years in time, not backward. When we suspect the credit quality is improving, we already start buying.

For risk management ratings are relevant. In risk management we use them, for instance, to decide how much of each company we can afford to have in our portfolio from a risk perspective.

So for a CCC rated company, which is riskiest, we do not buy more than 0.75 per cent of our entire portfolio. For B and BB rated companies we limit ourselves at 1.5 per cent and for investment grade to 2 per cent. Our clients, those for whom

24 • Interview

we invest, use them in a similar way. Let’s say a big pension fund allows Robeco to invest on their behalf. The pension fund will mandate us to build a portfolio within investment grade only, for example. If an investment grade bond downgrades to high yield, the imposed restrictions on downgrades require us to sell that bond. So ratings can sometimes be a trigger to sell.

To make our investment decisions we determine our own ratings. An internal Robeco score called the Fundemental score (F-score). The score is based on the financial analysis described earlier. It ranges from -3 for the worst companies to +3 for the best companies. To score our investments we break up the group of all investments in rating categories.

Within each rating category each investment is scored based on the F-score. In the end we compare the F-score to the relative value and determine what interest we would receive. If it is an attractive interest rate and we assign a high fundamental score to the investment, we decide to buy.

As a frequent user of ratings, what role do you think rating agencies should play within the economy?

Rating agencies have been a subject of debate recently for everything they would have done wrong. I cannot blame them for everything. I cannot blame them for lagging behind, for instance. Rating agencies have to base themselves on facts and are backward-looking. The market always leads.

What I do blame them for is that rating agencies are paid by the companies they assign ratings. In this way companies are in the position to bargain a higher rating. It somewhat resembles to what we have seen within accountancy. A company hires an accountant to check its accounts for which the accountant gets paid. It is as if you pay the police officer. Who should hire the supervisor? I think measures should be taken to keep apart the rating agencies and the ordering party. That is my main concern about rating agencies.

Apart from that, rating agencies will always play an important role within society because there exists a demand for an independent risk standard. Take for instance banks, you do not want banks to dictate themselves how risky their bonds are.

Someone in the economy has to determine the risk profile of an investment portfolio of a bank or of a fund, in a very objective light. Take as an example our clients at Robeco for whom we invest, they also want to know how risky the Robeco high

From my point of view the ratings of structured products should be supervised more.

yield fund is. They want to know how many CCCs are in there and how many BBBs. Hence, a rating is important but they have to be determined as objectively as possible and independent of the client.

Currently there are three big rating agencies: S&P, Moody’s, and Fitch. What is your opinion about more or less competition among rating agencies?

I think such a sector will always gravitate towards an oligopolistic market structure. These three rating agencies have build up reputations. Within Robeco we only invest in companies that have at least one rating from one of these three agencies and preferably two. In the end I do not think more competition is the answer. When the market structure that companies hire rating agencies does not change, more competition can even be dangerous. This opens a door for new rating agencies to market themselves as being more lenient and to give higher ratings. So a more important problem than competition is the cord between company and rating agency.

How do you suggest the cord between company and rating agency can be cut?

Currently, there is a proposal by a commission of the European union for the rotation of rating agencies, say every 3 years. This means that when a company has been rated by 1 agency for the last three years it is obliged to switch to a ­different agency. That is one of the possibilities you could think of. Another possibility is to let the government or a group of institutional investors hire the rating agency for a company instead of the company itself.

In your view what can we expect for rating agencies in the future?

anymore. It is actually very similar to what happened with accountancy. Advice and approval of the accounts has been separated. This will also happen for rating agencies.

In Europe we have seen many downgrades for ­overeigns. What is your opinion on these downgrades and what can be done to restore confidence?

Rating agencies actually have a problem right now. A problem which they underestimated with the introduction of the Euro. Before the Euro every country had their own currency so that when something were to occur they were able to turn on the money press. This is what we often see happening in the U.S. This way you can actually never go into bankruptcy.

Now, no matter how much indebted you are it is not possible to print money. Euro member states are being limited in their actions compared to other countries. They are forced to impose austerity measures. Hence, a European country can actually go bankrupt. This is what we have seen for Greece that did not repay its bonds. Only since the Euro crisis have rating agencies woken up and realized there is a difference between Europe and other countries that do have their own currency.

I see two scenarios. Either the situation in Europe persists and the ratings of European countries will structurally be lower than those of the U.S. and Japan. Or Europe will eventually issue Eurobonds, European government bonds issued

by Europe. In the latter case, Europe will regain its access to the money press, which is a very important tool in your macro economy. In order to reach the latter case Europe needs to integrate drastically fiscally, politically and monetarily. Economically this plan would be very viable but there is no political support for it in Europe.

I think the pressure is running high at the moment. Therefore I think the rotation proposal of the European union will be implemented at a certain point. Also I think there will also be more supervision although I am not entirely happy with that development. From my point of view the ratings of structured products should be supervised more. For structured products rating agencies really did make mistakes.

They did not just give ratings on these products but they also gave advice to construct structured products. Hence, they had mixed interests and they profited from their advisory task. In the future I think they will not be allowed to do that

Interview • 25

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fsrforum • volume 15 • issue #3

Determinants and Impact of Sovereign Credit Ratings

By Richard Cantor and Frank Packer

28 • Determinants and Impact of Sovereign Credit Ratings

1. Introduction In recent years, the demand for sovereign credit ratings—the risk assessments assigned by the credit rating agencies to the obligations of central governments—has increased dramatically. More governments with greater default risk and more companies domiciled in riskier host countries are borrowing in international bond markets. Although foreign government officials generally cooperate with the agencies, rating assignments that are lower than anticipated often prompt issuers to question the consistency and rationale of sovereign ratings.

How clear are the criteria underlying sovereign ratings?

Moreover, how much of an impact do ratings have on borrowing costs for sovereigns?

To explore these questions, we present the first systematic analysis of the determinants and impact of the sovereign credit ratings assigned by the two leading U.S. agencies, Moody’s Investors Service and Standard and Poor’s. Such an analysis has only recently become possible as a result of the rapid growth in sovereign rating assignments. The wealth of data now available allows us to estimate which quantitative indicators are weighed most heavily in the determination of ratings, to evaluate the predictive power of ratings in explaining a cross-section of sovereign bond yields, and to measure whether rating announcements directly affect market yields on the day of the announcement.

Our investigation suggests that, to a large extent, Moody’s and Standard and Poor’s rating assignments can be explained by a small number of well-defined criteria, which the two agencies appear to weigh similarly. We also find that the market—as gauged by sovereign debt yields—broadly shares the relative rankings of sovereign credit risks made by the two rating agencies. In addition, credit ratings appear to have some independent influence on yields over and above their correlation with other publicly available information. In particular, we find that rating announcements have immediate effects on market pricing for non-investment-grade issues.

2. What are sovereign ratings?

Like other credit ratings, sovereign ratings are assessments of the relative likelihood that a borrower will default on its obligations. Governments generally seek credit ratings to ease their own access (and the access of other issuers domiciled within their borders) to international capital markets, where many investors, particularly U.S. investors, prefer rated securities over unrated securities of apparently similar credit risk.

In the past, governments tended to seek ratings on their foreign currency obligations exclusively, because foreign ­ currency bonds were more likely than domestic currency offerings to be placed with international investors. In recent years, however, international investors have increased their demand for bonds issued in currencies other than traditional global currencies, leading more sovereigns to obtain domestic currency bond ratings as well. To date, however, foreign currency ratings—the focus of this article—remain the more prevalent and influential in the international bond markets.

Sovereign ratings are important not only because some of the largest issuers in the international capital markets are national governments, but also because these assessments affect the ratings assigned to borrowers of the same nationality.

For example, agencies seldom, if ever, assign a credit rating to a local municipality, provincial government, or private company that is higher than that of the issuer’s home country.

3. Determinants of sovereign ratings In their statements on rating criteria, Moody’s and Standard and Poor’s list numerous economic, social, and political factors that underlie their sovereign credit ratings (Moody’s 1991; Moody’s 1995; Standard and Poor’s 1994). Identifying the relationship between their criteria and actual ratings, however, is difficult, in part because some of the criteria are not quantifiable. Moreover, the agencies provide little guidance as to the relative weights they assign each factor. Even for quantifiable factors, determining the relative weights assigned by Moody’s and Standard and Poor’s is difficult because the agencies rely on such a large number of criteria.

As a first step we describe the variables in next session’s regression and identify the measures we use to represent them in our quantitative analysis. We explain below the relationship between each variable and a country’s ability and willingness to service its debt: • Per capita income. The greater the potential tax base of the borrowing country, the greater the ability of a government to repay debt. This variable can also serve as a proxy for the level of political stability and other important factors.

• GDP growth. A relatively high rate of economic growth suggests that a country’s existing debt burden will become easier to service over time.

• Inflation. A high rate of inflation points to structural problems in the government’s finances. When a government appears unable or unwilling to pay for current budgetary expenses

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Determinants and Impact of Sovereign Credit Ratings • 29

fsrforum • volume 15 • issue #3

Sovereign ratings are assessments of the relative likelihood that a borrower will default on its obligations.

through taxes or debt issuance, it must resort to inflationary money finance. Public dissatisfaction with inflation may in turn lead to political instability.

• Fiscal balance. A large federal deficit absorbs private domestic savings and suggests that a government lacks the ability or will to tax its citizenry to cover current expenses or to service its debt.

• External balance. A large current account deficit indicates that the public and private sectors together rely heavily on funds from abroad. Current account deficits that persist result in growth in foreign indebtedness, which may become unsustainable over time.

• External debt. A higher debt burden should correspond to a higher risk of default. The weight of the burden increases as a country’s foreign currency debt rises relative to its foreign currency earnings (exports).

• Economic development. Although level of development is already measured by our per capita income variable, the rating agencies appear to factor a threshold effect into the relationship between economic development and risk. That is, once countries reach a certain income or level of development, they may be less likely to default. We proxy for this minimum income or development level with a simple indicator variable noting whether or not a country is classified as industrialized by the International Monetary Fund.

• Default history. Other things being equal, a country that has defaulted on debt in the recent past is widely perceived as a high credit risk. Both theoretical considerations of the role of reputation in sovereign debt (Eaton 1996) and related empirical evidence indicate that defaulting sovereigns suffer a severe decline in their standing with creditors (Ozler 1991). We factor in credit reputation by using an indicator variable that notes whether or not a country has defaulted on its international bank debt since 1970.

4. Quantifying the relationship between ratings and their determinants In this section, we assess the individual and collective significance of our eight variables in determining the September 29, 1995, ratings of the forty-nine countries listed in Table 2.

The sample statistics, broken out by broad letter category, show that five of the eight variables are directly correlated with the ratings assigned by Moody’s and Standard and Poor’s. In particular, a high per capita income appears to be closely related to high ratings: among the nine countries

30 • Determinants and Impact of Sovereign Credit Ratings

assigned top ratings by Moody’s and the eleven given Standard and Poor’s highest ratings, median per capita income is just under $24,000. Lower inflation and lower external debt are also consistently related to higher ratings. A high level of economic development, as measured by the indicator for industrialization, greatly increases the likelihood of a rating of Aa/AA. As a negative factor, any history of default limits a sovereign’s ratings to Baa/BBB or below.

Three factors—GDP growth, fiscal balance, and external balance—lack a clear bivariate relation to ratings. Ratings may lack a simple relation to GDP growth because many developing economies tend to grow faster than mature economies. More surprising, however, is the lack of a clear correlation between ratings and fiscal and external balances. This finding may reflect endogeneity in both fiscal policy and international capital flows: countries trying to improve their credit standings may opt for more conservative fiscal policies, and the supply of international capital may be restricted for some low-rated countries.

Because some of the eight variables are mutually correlated, we estimate a multiple regression to quantify their combined explanatory power and to sort out their individual contributions to the determination of ratings. Like most analysts who transform bond ratings into data for regression analysis (beginning with Horrigan 1966 and continuing through Billet 1996), we assign numerical values to the Moody’s and Standard and Poor’s ratings as follows: B3/B- = 1, B2/B = 2, and so on through Aaa/AAA = 16. When we need a measure of a country’s average rating, we take the mean of the two numerical values representing Moody’s and Standard and Poor’s ratings for that country. Our regressions relate the numerical equivalents of Moody’s and Standard and Poor’s ratings to the eight explanatory variables through ordinary least squares.

The model’s ability to predict large differences in ratings is impressive. The first column of Table 5 shows that a regression of the average of Moody’s and Standard and Poor’s ratings against our set of eight variables explains more than 90 percent of the sample variation and yields a residual standard error of about 1.2 rating notches. Note that although the model’s explanatory power is impressive, the regression achieves its high R-squared through its ability to predict large rating ­differences. For example, the specification predicts that Germany’s rating (Aaa/AAA) will be much higher than Uruguay’s (Ba1/BB+). The model naturally has little to say about small

rating differences—for example, why Mexico is rated Ba2/BB and South Africa is rated Baa3/BB. These differences, while modest, can cause great controversy in financial markets.

The regression does not yield any prediction errors that exceed three notches, and errors that exceed two notches occur in the case of only four countries. Another way of measuring the accuracy of this specification is to compare predicted ratings rounded off to the nearest broad letter rating with actual broad letter ratings. The average rating regression predicts these broad letter ratings with about 70 percent accuracy, a slightly higher accuracy rate than that found in the literature quantifying the determinants of corporate ratings (see, for example, Ederington [1985]).

Of the individual coefficients, per capita income, GDP growth, inflation, external debt, and the indicator variables for economic development and default history all have the anticipated signs and are statistically significant. The coefficients on both the fiscal and external balances are statistically insignificant and of the unexpected sign. As mentioned earlier, in many cases the market forces poor credit risks into apparently strong fiscal and external balance positions, diminishing the significance of fiscal and external balances as explanatory variables. Therefore, although the agencies may assign substantial weight to these variables in determining specific rating assignments, no systematic relationship between these variables and ratings is evident in our sample. Quantitative models cannot explain all variations in ratings across countries: as the agencies often state, qualitative social and political considerations are also important determinants. For example, the average rating regression predicts Hong Kong’s rating to be almost three notches higher than its actual rating. Of course, Hong Kong’s actual rating reflects the risks inherent in its 1997 incorporation into China. If the regression had failed to identify Hong Kong as an outlier, we would suspect it was misspecified and/or overfitted.

Our statistical results suggest that Moody’s and Standard and Poor’s broadly share the same rating criteria, although they weight some variables differently. The general similarity in criteria should not be surprising given that the agencies agree on individual ratings more than half the time and most of their disagreements are small in magnitude. The fourth column of Table 5 reports a regression of rating differences (Moody’s less Standard and Poor’s ratings) against these variables. Focusing only on the statistically significant coefficients, we find that Moody’s appears to place more weight on external

debt and less weight on default history as negative factors than does Standard and Poor’s. Moreover, Moody’s places less weight on per capita income as a positive factor.

In addition to the relationship between a country’s economic indicators and its sovereign ratings, the effect of ratings on yields is of interest to market practitioners. Although ratings are clearly correlated with yields, it is far from obvious that ratings actually influence yields. The observed correlation could be coincidental if investors and rating agencies share the same interpretation of a body of public information pertaining to sovereign risks. In the next section, we investigate the degree to which ratings explain yields. After examining a cross-section of yields, ratings, and other potential explanatory factors at one point in time, we examine the movement of yields when rating announcements occur.

5. The cross-sectional relationship between ratings and yields In the fall of 1995, thirty-five countries rated by both Moody’s and Standard and Poor’s had actively traded Euro-dollar bonds. For each country, we identified its most liquid Eurodollar bond and obtained its spread over U.S. Treasuries as reported by Bloomberg L.P. on September 29, 1995. A regression of the log of these countries’ bond spreads against their average ratings shows that ratings have considerable power to explain sovereign yields. The single rating variable explains 92 percent of the variation in spreads, with a standard error of 20 basis points. We also tried a number of alternative regressions based on Moody’s and Standard and Poor’s ratings, but none significantly improved the fit.

Sovereign yields tend to rise as ratings decline. This pattern is evident in Chart 1, which plots the observed sovereign bond spreads as well as the predicted values from the average rating specification. An additional plot of average corporate spreads at each rating shows that sovereign bonds rated below A tend to be associated with higher spreads than comparably rated U.S. corporate securities. One interpretation of this finding is that although financial markets generally agree with the agencies’ relative ranking of sovereign credits, they are more pessimistic than Moody’s and Standard and Poor’s about sovereign credit risks below the A level.

Our findings suggest that the ability of ratings to explain ­relative spreads cannot be wholly attributed to a mutual correlation with standard sovereign risk indicators. A regression of spreads against the eight variables used to predict credit

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Determinants and Impact of Sovereign Credit Ratings • 31

fsrforum • volume 15 • issue #3

ratings explains 86 percent of the sample variation. Because ratings alone explain 92 percent of the variation, ratings appear to provide additional information beyond that contained in the standard macroeconomic country statistics incorporated in market yields.

In addition, ratings effectively summarize the information contained in macroeconomic indicators. The third column in Table 6 presents a regression of spreads against average ratings and all the determinants of average ratings collectively. In this specification, the average rating coefficient is virtually unchanged from its coefficient in the first column of Table 6, and the other variables are collectively and individually insignificant. Moreover, the adjusted R-squared in the third specification is lower than in the first, implying that the macroeconomic indicators do not add any statistically significant explanatory power to the average rating model.

The results of our cross-sectional tests agree in part with those obtained from similar tests of the information content of corporate bond ratings (Ederington, Yawitz, and Roberts 1987) and municipal bond ratings (Moon and Stotsky 1993).

Like the authors of these studies, we conclude that ratings may contain information not available in other public sources. Unlike these authors, however, we find that standard indicators of default risk provide no useful information for predicting yields over and above their correlations with ratings.

6. The impact of rating announcements on dollar bond spreads We next investigate how dollar bond spreads respond to the agencies’ announcements of changes in their sovereign risk assessments. Certainly, many market participants are aware of specific instances in which rating announcements led to a change in existing spreads. Table 7 presents four recent examples of large moves in spread that occurred around the time of widely reported rating changes.

Of course, we do not expect the market impact of rating changes to be this large on average, in part because many rating changes are anticipated by the market. To move beyond anecdotal evidence of the impact of rating announcements, we conduct an event study to measure the effects of a large sample of rating announcements on yield spreads. Similar event studies have been undertaken to measure the impact of rating announcements on U.S. corporate bond and stock returns. In the most recent and most thorough of these

32 • Determinants and Impact of Sovereign Credit Ratings

studies, Hand, Holthausen, and Leftwich (1992) show that rating announcements directly affect corporate securities prices, although market anticipation often mutes the average effects.

To construct our sample, we attempt to identify every announcement made by Moody’s or Standard and Poor’s between 1987 and 1994 that indicated a change in sovereign risk assessment for countries with dollar bonds that traded publicly during that period. Altogether, we gather a sample of seventy-nine such announcements in eighteen countries.

Thirty-nine of the announcements report actual rating changes—fourteen upgrades and twenty-five downgrades.

The other forty announcements are “outlook” (Standard and Poor’s term) or “watchlist” (Moody’s term) changes: twentythree ratings were put on review for possible upgrade and seventeen for possible downgrade.

We then examine the average movement in credit spreads around the time of negative and positive announcements.

Chart 2 shows the movements in relative yield spreads— yield spreads divided by the appropriate U.S. Treasury rate— thirty days before and twenty days after rating announcements. We focus on relative spreads because studies such as Lamy and Thompson (1988) suggest that they are more stable than absolute spreads and fluctuate less with the general level of interest rates.

Agency announcements of a change in sovereign risk assessments appear to be preceded by a similar change in the market’s assessment of sovereign risk. During the twenty-nine days preceding negative rating announcements, relative spreads rise 3.3 percentage points on an average cumulative basis. Similarly, relative spreads fall about 2.0 percentage points during the twenty-nine days preceding positive rating announcements. The trend movement in spreads disappears approximately six days before negative announcements and flattens shortly before positive announcements. Following the announcements, a small drift in spread is still discernible for both upgrades and downgrades.

Do rating announcements themselves have an impact on the market’s perception of sovereign risk? To capture the immediate effect of announcements, we look at a two-day window—the day of and the day after the announcement— because we do not know if the announcements occurred before or after the daily close of the bond market. Within this window, relative spreads rose 0.9 percentage points for negative announcements and fell 1.3 percentage points for positive

Lower inflation and lower external debt are also consistently related to higher ratings.

announcements. Although these movements are smaller in absolute terms than the cumulative movements over the preceding twenty-nine days, they represent a considerably larger change on a daily basis. These results suggest that rating announcements themselves may cause a change in the market’s assessment of sovereign risk.

Statistical analysis confirms that for the full sample of seventynine events, the impact of rating announcements on dollar bond spreads is highly significant. Table 8 reports the mean and median changes in the log of the relative spreads during the announcement window for the full sample as well as for four pairs of rating announcement categories: positive versus negative announcements, rating change versus outlook/ watchlist change announcements, Moody’s versus Standard and Poor’s announcements, and announcements concerning investment-grade sovereigns versus announcements concerning speculative-grade sovereigns. Because positive rating announcements should be associated with negative changes in spread, we multiply the changes in the log of the relative spread by -1 when rating announcements are positive. This adjustment allows us to interpret all positive changes in spread, regardless of the announcement, as being in the direction expected given the announcement.

Roughly 63 percent of the full sample of rating announcements are associated with changes in spread in the expected direction during the announcement period, with a mean change in the log of relative spreads of about 2.5 percent.

This finding is consistent with the announcement effect for U.S. corporate bonds documented by Hand, Holthausen, and Leftwich (1992). In fact, the share of responses in the expected direction is consistently above 50 percent regardless of the category of rating announcement. Moreover, the mean changes are always positive regardless of category.

Tests of statistical significance do suggest some differences between categories, however. Most strikingly, by both the mean change and percent positive measures, rating announcements have a highly significant impact on speculative-grade sovereigns but a statistically insignificant effect on investment-grade sovereigns. (By contrast, Hand, Holthausen, and Leftwich find that rating announcements have a significant impact on both investment-grade and speculative-grade corporate bonds.) Table 8 also reveals that the mean change statistics are not significant for negative announcements, outlook/watchlist announcements, and Standard and Poor’s announcements, although the percent positive statistics are

significant for those categories. Because the statistical inferences for certain categories are ambiguous, and because the various categories overlap, we employ a multiple regression to sort out which categories of rating announcements imply meaningfully different effects on spreads.

We run a regression of the change in relative spreads against four indicator variables that take on the value 1 (or 0) depending on whether (or not) the rating announcements involve actual rating changes, positive events, Moody’s decisions, or speculative-grade sovereigns. As might be expected from Table 8, the estimated coefficients are all positive. Only the coefficients on the Moody’s and speculative-grade indicator variables, however, are statistically significant. Thus, the multiple regression indicates that the immediate impact of an announcement on yield spreads is greater if the announcement is made by Moody’s or if it is related to speculativegrade credit. By contrast, the impact of announcements does not appear to rely on the distinction between rating changes and outlook/watchlist changes or the distinction between positive and negative announcements.

We have established the impact of certain rating announcements on dollar bond spreads, but a second question arises: to what extent does anticipation by the market dilute the impact of these announcements? The presence of many wellanticipated events in our dataset could obscure highly significant responses to unanticipated announcements— including, perhaps, announcements by Standard and Poor’s or announcements concerning investment-grade sovereigns.

To pursue this issue, we construct three proxies for anticipation—changes in relative spreads, rating gaps between the agencies, and other rating announcements— all of which measure conditions before the announcement. The first proxy measures the change in relative spread (in the direction of the anticipated change) over the sixty days preceding the event. Prior movements in the relative spread may reflect the market’s incorporation of information used by the agency in making the announcement. The second proxy indicates the sign of the gap between the rating of the agency making the announcement and the other agency’s rating. An announcement that brings one agency’s rating into line with the other’s may be expected by market participants. In our regressions, the rating gap equals 1 (0) if the announcement moves the two agencies’ risk assessments closer together (further apart). The third proxy is an indicator variable that equals 1 if another rating announcement of the same sign

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Determinants and Impact of Sovereign Credit Ratings • 33

fsrforum • volume 15 • issue #3

had occurred during the previous sixty days. This proxy is motivated by considerable evidence that rating announcements tend to be positively correlated—that is, positive announcements are more likely to be followed by positive announcements than by negative announcements and vice versa.

We use each of the anticipation proxies in turn as a fifth explanatory variable in a multiple regression that includes the four indicator variables for actual rating changes, positive events, Moody’s decisions, or speculative- grade sovereigns. A final regression adds all three anticipation proxy variables simultaneously to the basic regression.

Our earlier results are robust to the addition of the proxy ­variables. Announcements by Moody’s and announcements pertaining to speculative-grade sovereigns continue to have a larger impact than announcements by Standard and Poor’s or announcements pertaining to investment- grade sovereigns.

(Note, however, that the statistical significance of the differences between the effects of the different rating agencies declines below the 10 percent level in three of the four new specifications.)

Contrary to our expectations, however, the results reported in Table 9 suggest that market anticipation does not reduce significantly, if at all, the impact of a sovereign rating announcement. The estimated coefficient on the change in the relative spreads variable has the expected negative sign, but it is not statistically significant. Moreover, the estimated coefficients on both the rating gap and the other rating announcement indicators are unexpectedly positive and highly significant. According to these two measures, the impact of one agency’s announcement is greater if the announcement confirms the other agency’s rating or a previous rating announcement.

7. Conclusion Sovereign credit ratings receive considerable attention in financial markets and the press. We find that the ordering of risks they imply is broadly consistent with macroeconomic fundamentals. Of the large number of criteria used by Moody’s and Standard and Poor’s in their assignment of sovereign ratings, six factors appear to play an important role in determining a country’s rating: per capita income, GDP growth, inflation, external debt, level of economic development, and default history. We do not find any systematic relationship between ratings and either fiscal or current deficits, perhaps because of the endogeneity of fiscal policy and international capital flows.

34 • Determinants and Impact of Sovereign Credit Ratings

Our analysis also shows that sovereign ratings effectively summarize and supplement the information contained in macroeconomic indicators and are therefore strongly correlated with market-determined credit spreads. Most of the correlation appears to reflect similar interpretations of publicly available information by the rating agencies and by market participants. Nevertheless, we find evidence that the rating agencies’ opinions independently affect market spreads.

Event study analysis broadly confirms this qualitative conclusion: it shows that the announcements of changes in the agencies’ sovereign risk opinions are followed by bond yield movements in the expected direction that are statistically significant.

Although our event study results largely corroborate the findings of corporate sector studies, a few of our observations are surprising and invite further investigation. Our finding that the impact of rating announcements on spreads is much stronger for below-investment-grade than for investmentgrade sovereigns is one puzzle. Another surprising result is that rating announcements that are more fully anticipated, at least by our proxy measures, have, if anything, a larger impact than those that are less anticipated.

In sum, although the agencies’ ratings have a largely predictable component, they also appear to provide the market with information about non-investment-grade sovereigns that goes beyond that available in public data. The difficulty in measuring sovereign risk, especially for below-investmentgrade borrowers, is well known. Despite this difficulty—and perhaps because of it—sovereign credit ratings appear to be valued by the market in pricing issues.

Het Financieel Traineeship van PostNL Je bent een financieel talent, je ziet jezelf wel aan tafel met de CFO van een beursgenoteerd bedrijf en je hebt stevige ambities. Ons Financieel Traineeship biedt een perfecte opstap naar een carrière in Finance en Management. Je krijgt direct de verantwoordelijkheid voor twee stevige finance projecten. En via het Young Executive Programme (YEP) werk je aan je managementvaardigheden en krijg je individuele coaching en begeleiding. Maak werk van je toekomst. Start als Financieel Trainee bij PostNL. Inschrijven voor 26 mei 2013.

Het programma start in september 2013.

Check je kansen op vacaturesbijpostnl.nl/ft

het bezorgen van morgen

‘Dat ik de ruimte krijg om te groeien, dat spreekt mij aan in Grant Thornton.’

Marc Buijs, gevorderd assistent accountant Het einde van je studie komt in zicht en straks ga je de eerste stappen zetten in het bedrijfsleven. Hoe vind je als young professional je plek in een organisatie? Wat kan je verwachten van de werkzaamheden en niet te vergeten de begeleiding? Allemaal vragen waar Marc Buijs, gevorderd assistent accountant bij Grant Thornton, graag antwoord op geeft. Na het afronden van zijn master accountancy is Marc in dienst getreden bij Grant Thornton, op de vestiging in Rotterdam. Grant Thornton is een sterk groeiende middelgrote Accountancy- en Adviesorganisatie geworteld in de Randstad en verankerd in een groot internationaal netwerk.

Wat heb je gestudeerd en wat heb je verder naast je studie gedaan?

Na een bachelor bedrijfseconomie heb ik een master accountancy gevolgd. Vanaf het moment dat ik bij Grant Thornton in dienst ben gekomen, ben ik ook begonnen aan mijn post-master aan de Universiteit van Tilburg. Inmiddels heb ik het theoretische deel afgerond. Naast mijn studie ben ik actief lid geweest van zowel een studie- als een studentenvereniging. Tijdens mijn actieve periode bij de verenigingen heb ik in verscheidene commissies gezeten. Daarnaast werkte ik parttime voor een accountantskantoor.

Waarom ben je bij Grant Thornton gaan werken?

Tijdens mijn studie was ik al met meerdere kantoren in aanraking gekomen, ook met Grant Thornton. Belangrijke pijlers voor het maken van mijn keuze waren: de sfeer, de doorgroeimogelijkheden en het cliëntenpakket. Vooral het laatste punt vond ik erg belangrijk. Voor zowel mijn bachelor- als masterscriptie deed ik onderzoek naar familiebedrijven. Een cliëntenpakket met familiebedrijven past bij mij. Medewerkers van familie zijn erg betrokken en enthousiast. Ook hun ondernemersgeest spreekt mij aan. Daarnaast leek het me interessant om te gaan werken in de Randstad. Momenteel werk ik met name voor middelgrote en grote private internationaal opererende familiebedrijven. Mijn cliëntenpakket bestaat voornamelijk uit bedrijven gerelateerd aan de haven in Rotterdam. Te denken

valt aan bedrijven in de scheepvaart, offshore, transport en op- en overslag.

Wat is je huidige functie bij Grant Thornton?

Mijn huidige functie is gevorderd assistent accountant. In deze functie houd ik mij bezig met het coördineren van het werk van (junior) assistent accountants en begeleid ik hen in de ontwikkeling in het vak. Daarnaast voer ik de risicoanalyse uit voor de jaarrekeningcontrole van mijn klanten en controleer ik de meer complexe posten in de jaarrekening. Dit alles natuurlijk onder goede begeleiding van meer ervaren accountants.

Hoe zou je de werkcultuur bij Grant Thornton omschrijven?

De vestiging in Rotterdam is een typisch Rotterdams no-nonsense kantoor. Dit ervaar ik doordat de communicatielijnen kort zijn en de besluitvorming snel is. Ook is er veel aandacht voor vaktechnische en persoonlijke nlijke ontwikkeling. Hier in Rotterdam wordt ordt van alle medewerkers een proactieve actieve houding verwacht. Het type ype cliënten dat wij bedienen verlangt langt deze instelling ook van je. Het geven van je mening wordt zeer eer op prijs gesteld.

Daarnaast komt samenwerken met verschillende disciplines ciplines veelvuldig voor, bijvoorbeeld ld met fiscalisten, bedrijfsjuridisch adviseurs en consultants van de afdeling specialist advisory ry services.

Veel werk uit hett internationale netwerk wordt op de vestiging in Rotterdam otterdam opgevangen. Hierdoor erdoor krijg je ook de kans om m internationaal samen te werken. Het internationale netwerk twerk van Grant Thornton International geeft je de kans om internationaal samen men te werken met collega’s ega’s in circa 100 landen.

Welke ondersteuning uning krijgen nieuwe werknemers bij Grant Thornton?

Starters krijgen een en mentor toegewezen. Hiermee rmee bespreek je zaken n zoals

voortgang van je studie en punten waar je gedurende het werk tegenaan loopt. Het is prettig om een centraal aanspreekpunt te hebben. Daarnaast kun je door de informele sfeer en persoonlijke aandacht bij alle andere collega’s terecht.

Wat vind je het leukste aan het werk bij Grant Thornton?

De mogelijkheid voor het volgen van een post-master is dé ideale overgang naar het bedrijfsleven. Daarnaast vind ik het interessant om te werken in een organisatie die een groei doormaakt. De organisatie is altijd in beweging! Wat mij verder aanspreekt in Grant Thornton is dat de mogelijkheid en de ruimte wordt geboden om door te kunnen groeien. Om samenwerking tussen de landen te bevorderen, wordt uitwisseling van werknemers gestimuleerd.

Dit spreekt mij bijzonder aan omdat ik mezelf graag wil blijven ontwikkelen. Daarnaast word je breder opgeleid.

Traineegame 2011/2012: op bezoek bij Grant Thornton International in London

Tot slot, kan je iets meer vertellen over de mogelijkheden voor studenten/starters bij Grant Thornton?

Voor masterstudenten zijn er in februari tot juli op aanvraag werkstudentplekken beschikbaar om kennis te maken met werkzaamheden in de accountancy en om tegelijkertijd je scriptie te schrijven. Ook treden elk jaar weer afgestudeerde wo accountancy studenten in dienst. Naast vier dagen werken zal je op de vrijdag je post-master volgen. Voor starters organiseert Grant Thornton jaarlijks een traineegame. Gedurende de traineegame kruip je in de huid van een accountant van een fictieve cliënt.

In een levensechte case leerde ik hoe je in de praktijk maximale toegevoegde waarde kunt creëren. Tegelijkertijd maakte ik kennis met de vestigingen, specialismen en het internationale netwerk.

Traineegame 2012/2013: het werken aan casussen

Meer informatie over Grant Thornton

Grant Thornton bij jou in de buurt:

Grant Thornton is een sterk groeiende middelgrote accountancyen adviesorganisatie met ruim 500 medewerkers, geworteld in de Randstad en verankerd in het gerenommeerde internationale netwerk van Grant Thornton International. Je hebt hiermee toegang tot de expertise van meer dan 30.000 professionals wereldwijd in circa 100 landen.

Alphen aan den Rijn - Amsterdam - Boskoop - Enschede Gouda - Leiden - Rijswijk - Rotterdam - Woerden

Meer informatie is te vinden op www.carrierebijGT.nl. Of neem contact op met Ilona Douma, Recruiter, T 0172 – 41 68 68 of E ilona.douma@carrierebijGT.nl

Accountancy - Belastingen - Advies

fsrforum • volume 15 • issue #3

AAA-rated or XXX-rated?

Jan Frederik Slijkerman PhD1

Rating agencies seem to have lost much credibility with the wider public, according to many newspaper articles. They are blamed for much of what went wrong in the recent crisis. As a frequent user of credit ratings and as an academic, my view of their work tends to be a little more nuanced. Firstly, one should distinguish between the three different sorts of ratings that are published: for countries, companies and structured assets. Secondly, I wish to make the following comment on their use. Investors should form their own opinions about investments and rating agencies can provide important input. Compare ratings to adult entertainment movies that are being XXX-rated. Only investors who understand how to use the ratings and are aware of their limitations and risks should rely on them.

Consider the three main classes of debt: sovereign debt, corporate debt and structured assets.

The rating agencies have had extensive, public, methodologies for the first two classes for a long time.

The sovereign rating includes factors such as the amount of debt compared to the GDP of a country, the current account surplus and many more macro variables to help determine countries’ ability to repay debt. Additionally, qualitative factors such as political stability and competitiveness are taken into account.

Only investors who understand how to use the ratings and are aware of their limitations and risks should rely on them.

The corporate rating involves the characteristics of the business a firm is active in and an analysis of its financial position. Factors that are included to determine the quality of the business profile are: the profitability of the industry, the size of the firm and the stability of margins. Factors to include in the financial analysis are the amount of debt compared to cash generation and free cash flow generation. Since rating agencies also compare companies with each other, they are able to review whether or not the assigned rating is appropriate.

Their work is hindered by two choices the agencies make. Firstly, they want to provide ratings that are valid through an economic cycle. So they prefer not to change ratings frequently.

Therefore changes in the ratings may lag the developments of credit profiles. Please note that the written report that accompanies a rating contains valuable information as to the further direction of the credit quality. Secondly, agencies rely partially on information and commitments from management teams. Only when management teams are reliable will this be an efficient tool to enhance the accurateness of ratings.

38 • AAA-rated or XXX-rated?

Rating agencies review regularly whether the assigned ratings reflect default risk appropriately.

They do this by investigating if the default rate of companies with a specific rating is stable over time. This is the case in general.

Evaluating if sovereign ratings are correct is much more difficult. Defaults of developed countries were quite rare until recently. Moreover, the appropriateness of an assigned rating should be evaluated based on the framework used to assign ratings. An important element in this context is the political will of countries not to default or the willingness by other countries to help a country not to default (such as in the euro zone). Rating agencies therefore have to evaluate the political situation, which is a far more difficult task than the evaluation of corporate financial statements or longer-term viability of businesses. Also note that corporate ratings have been assigned for long and that also country ratings are around for considerable time.

The latest asset class, structured products, is of more recent age. Technological, legal and financial innovations have created a new asset class with structured products. These products are often claims on certain parts of pools of assets. Rating agencies used advanced tools to gauge the expected default frequency of the rated instrument. This is where things went wrong for the rating of some investment pools. The agencies made wrong assumptions on the dependencies among certain types of risk, while in specific cases the credit history was too short in hindsight to validate a rating model. Another issue was that the rating agencies used the default rates corresponding to a rating class, while banks selected investments in this class with the highest spreads and hence a higher than expected default risk. The rating agencies missed this adverse selection effect. There is justified criticism on their willingness to rate more products which they may not have fully understood. However, these were mostly niche products. At the moment, requirements for ratings have become stricter for many structured assets and criminal investigation is underway to evaluate to what extent the agencies were aware of the issues with certain ratings.

Have rating agencies done a good job? I would be inclined to say yes, they did. It is clear that there have been irregularities, especially in the domain of rated structured products. However, in the domain of corporate ratings and sovereign ratings the analysis was good overall. Maybe the assigned rating was a bit too high for some corporate bonds before the crisis, yet this was in line with the outlook of a continuation of the healthy economic growth rates that existed before the crisis. Higher growth facilitated companies to grow into their capital structure. In the cold light of day, it is clear that there have been serious misjudgements when ratings for structured products were determined. I believe that the revisited internal policies of rating agencies contribute a lot to restoring trust in ratings for these products. Nevertheless, it is necessary for the US Department of Justice to investigate any intentional wrongdoing.

Some niche products, often so called “structured garbage”, were bought by investors who did not verify what they bought and only relied on the rating. My main argument would be that, once one understands the methodology of agencies, one can determine if the rating is appropriate and one can disagree with the assigned rating. In many cases investors did not invest in certain structured products that failed investors’ scrutiny. Hence, investors should only engage in investments when they fully understand the product and when they are willing to bear the ­consequences of their own errors in judgement.

Notes 1 Guest lecturer Money, Credit and Banking at the Erasmus School of Economics and Senior Credit Analyst at AEGON Asset Management.

AAA-rated or XXX-rated? • 39

fsrforum • volume 15 • issue #3

“De Rotterdam”; een feuilleton in vele delen K(r)anttekening | Drs. Joost Groeneveld RA RV1

Maandag 18 februari 2013 brengt Het Financieele Dagblad op haar voorpagina de moderne tijd treffend in beeld. Het “Nieuwsoverzicht” bevat de volgende kopjes: Surseance in bloemen – Economie Italië krimpt – Pensioen Elsevier – Corruptie in Spanje – Veolia in de verkoop – Univé schrapt banen – Chips uit Twente – Falend risicomodel. Afzonderlijke artikelen hebben de volgende koppen: Sociaal overleg op springen door interne strijd bonden – Shell: Soros niet eerlijk over transparantie – Miljoenendans rond ‘de Rotterdam’, cruiseschip blijft molensteen.

Als dit een representatieve steekproef is van het nieuws, en waarom niet, staan we er niet best voor. De inzakkende economie lijkt een katalysator voor ruzie, bedrog en domheid.

Of zijn die er altijd wel, maar wordt de sluier daarover door de omstandigheden weggetrokken? Hebben we onvoldoende speling om de zaken toe te dekken en komen ze nu aan het licht? In zekere zin zou dat een zonzijde kunnen zijn van de huidige teruggang.

Drs. Joost G. Groeneveld RA RV is directeur van Wingman Business Valuators B.V. te Breda en voorzitter van de Stichting WBO (register van business valuators).

Hij was hoofddocent aan de Economische Faculteit van de Erasmus Universiteit te Rotterdam.

Onder “Pensioen Elsevier” wordt gemeld dat de deelnemersraad een rechtszaak overweegt tegen het bestuur van het fonds. Zij wil daarmee het besluit aanvechten om 3,3% te korten op de pensioenen in april 2013. Na jaren zonder inflatievergoeding hebben de oudjes al heel wat ingeleverd van wat zij hebben gespaard. Toch lijkt er bij de jongeren geen gêne te bestaan. En evenmin bij de besturen die echt niet allemaal zo wijs hebben belegd. En zijn de werkgevers van weleer wel voldoende aangespoord om de fondsen aan te vullen? Dankzij de recessie leent de Nederlandse staat spotgoedkoop. Met die miljarden-meevallers op de staatsleningen zou er dus geld moeten zijn om de pensioenen van haar voormalige werk­ nemers te compenseren.

v­ erkopen die zij ontwerpt. Dat is dus een aardig nieuwtje. Ze hebben dan nog wel een “kapitaalinjectie” nodig. Dat laatste maakt me weer wat minder opgewekt. Zulke injecties zijn niet overal te koop. Misschien wel heel verstandig van “medeoprichter Daniel Schinkel” om zijn belang in Axiom IC te verkopen.

En wat Shell betreft is volgens Dick Benschop (presidentdirecteur Shell Nederland) “de Amerikaanse miljardair en filantroop George Soros niet eerlijk over transparantie en probeert (hij) op dubieuze wijze het gelijk aan zijn zijde te krijgen. Hij gaat bovendien voorbij aan de juridische werkelijkheid en beseft onvoldoende de gevolgen van zijn pleidooi voor de concurrentiepositie”. Zou Soros geen aandelen hebben in Shell? En terzijde: bij hoeveel geld is hij filantroop geworden?

Als je daar te vroeg mee begint, word je nooit miljardair en word je dus ook nooit filantroop genoemd.

Maar de meeste plaats op de voorpagina is ingeruimd voor “de Rotterdam”. Als het niet zo’n drama was, zou het een klucht zijn. Het is verleidelijk om nu in beeldspraak te vervallen. Iets over woelige baren, opstekende stormen, en dan vooral iets erbij over stranden en aan de grond lopen, ofwel het schip in gaan. Misschien stonden deze keer de beste stuurlui niet aan wal. Het FD spreekt van een scheepsramp.

Veolia gaat in de verkoop omdat de Franse moedermaatschappij zich wil concentreren op drie kernactiviteiten: water, afval en energie. Alle vervoerscontracten lopen gewoon door. Ja, dat zal het bekende liedje zijn geweest: te laag inschrijven, respectievelijk te veel betalen om die contracten te krijgen. Kun je dan nog van “tegenvallende resultaten” spreken? Ben benieuwd welke strategische aandeelhouder met ons Openbaar Vervoer gaat lopen.

Met de verkoop van het schip aan “Westcord” voor zo’n e 30 mln dacht ik dat het lek boven water was. En wat blijkt nu: er is nog een tweede koper die claimt dat het schip aan hem is verkocht voor e 42 mln. Saillant detail: vader en zoon Westers staan hier als respectieve kopers tegenover elkaar. Is ook hier – zoals bij Shell - sprake van een “juridische werkelijkheid”?

Althans volgens directeur Wybenga van Woonbron (verkoper van het schip) was er met zoon Westers en diens compagnon “geen definitief akkoord”. Dan zou het een juridische onwerkelijkheid zijn. Als het een beetje tegenzit, wordt dat een rechtszaak over mogelijke wanprestatie. Als die wordt toegewezen, zijn we jaren verder en moet vervolgens de schade worden bepaald. Een kolfje naar de hand van gerechtelijke deskundigen op het terrein van business valuation. Waarbij (wettelijke) rente niet uit het oog mag worden verloren.

Gratis advies: probeer nu te schikken.

“Chips uit Twente” suggereert het tegendeel van wat daar aan de hand is. Axiom IC blijft in Twente en gaat zelf de chips

De transactie wordt in het FD min of meer toegelicht: “… zou Woonbron over een aantal jaren voor e 20 mln meege-

40 • “De Rotterdam” ; een feuilleton in vele delen

nieten van een fiscaal voordeel omdat de kopers de verliezen op het schip zouden overnemen om die van hun belastbare winst af te trekken. Dat bedrag werd bij de verkoopopbrengst opgeteld. … Overigens maakt deze constructie ook deel uit van de overeenkomst met Westcord. Dat is afgesproken met de fiscus en daar is helemaal niets verkeerd aan, aldus Wybenga”.

De vraag is natuurlijk of Woonbron het na dit debacle zo ver moet zoeken, respectievelijk het publiek zo ver moet laten zoeken.

Woonbron zal zelf geen vennootschapsbelasting betalen. Dus het verlies was netto. In feite wordt met het schip een verlies verkocht. Andere winst van de kopers wordt met dit verlies gecompenseerd. En wat de compensatie waard is, hangt dan af van onder andere hoeveel winst kan worden gecompenseerd en op welke tijdstippen. Dat is een kwestie van ‘contante waarde’berekening. Voor die ene koper dus geschat op e 20 mln. Dat kost de overheid dus e 20 mln aan gemiste belastingopbrengst. En waarschijnlijk veel meer. Want die e 20 mln is alleen maar het deel van het “meegenieten” door Woonbron. Je gaat je dan afvragen wat er nu per saldo voor het schip is betaald. Weer die e 1,5 mln die in 2005 door Woonbron aan de RDM-groep van Joep van den Nieuwenhuyzen werd betaald? Dan is de cirkel wat dat betreft weer helemaal rond.

Woonbron rekent dit bedrag van e 20 mln tot de verkoopopbrengst. Zoals Van der Moolen (directeur Centraal Fonds Volkshuisvesting en hier de toezichthouder) opmerkt, is dat “nogal ver gezocht”. De vraag is natuurlijk of Woonbron het na dit debacle zo ver moet zoeken, respectievelijk het publiek zo ver moet laten zoeken. Dan zijn we weer terug bij ‘transparantie’.

En heeft Woonbron al een voorziening getroffen voor de claim van de ene koper als de andere wint? Die zou je zonder zo ver te zoeken dan toch op de verkoopopbrengst in mindering kunnen brengen. Maar als je in de jaarrekening een voorziening treft, kan daaruit worden afgeleid dat je verwacht de onderliggende procedure te verliezen. Ze hadden bij Woonbron die fiscale winst en die schadeclaim tegen elkaar kunnen wegstrepen. Die winst is toch ook nog lang niet gerealiseerd. Met een tamelijk gerust hart kan hier wel worden beloofd: wordt vervolgd.

Notes 1 Directeur Wingman Business Valuators B.V., Breda

“De Rotterdam” ; een feuilleton in vele delen • 41

fsrforum • volume 15 • issue #3

Companypresentation

Flow Traders is a leading international automated proprietary trading house. We are honoured to have been chosen by industry participants as ETF Market Maker Europe 2011 by the Global ETF Awards for the fifth consecutive year and have also been selected as ETF Market Maker Asia Pacific for the first time. Founded in 2004 and headquartered in Amsterdam, The Netherlands, Flow Traders trades a range of securities, futures, options, ETF and ETC instruments, foreign exchange instruments, and bonds on exchanges around the world. Distinguishing itself with its state of the art technology, Flow stays ahead of the competition by focusing on speed and niche competencies in markets where every microsecond counts. Our business expands each day by adding new products across an ever-broadening range of markets around the globe. We are a lean organization with a non-hierarchical approach, keeping interaction as informal as possible to enhance transparency and minimizing turnaround time in the current financial landscape.

In order to maximize our performance and facilitate our international growth, Flow Traders heavily invest in its employees. The backbone of our success is the collection of creative doers, thinkers, and above all, believers who form our company.

Flow Traders is always looking for creative and driven trading talent!

In this challenging position you manage and optimize our daily position (pricing and trading) in a wide range of financial products, including equities, bonds, derivatives and complex structured products. You formulate innovative trading strategies and, in close collaboration with our software engineers, develop trading models and tools that will be implemented on the trading desk. Because of the specific nature of the work-environment, we do not expect you to plunge headfirst into your new job. Instead, you will start by following a three months intensive in-house training program that covers all the intricate details of the trading processes. As a member of an informal team, you will then gradually take on more responsibilities, start monitoring markets and only then start making split-second portfolio adjustments that are at the heart of our success.

The Junior Trader Profile: • A relevant university degree preferably in finance, econometrics, mathematics or economics • Demonstrable interest in global financial markets • Knowledge of Excel and a keen interest in IT systems in general • Innovative & creative high potential with excellent mathematical and analytical skills • Competitive, outgoing, communicative, creative and able to deliver under pressure • A combination of distinct ability to spot arising opportunities and the assertiveness to seize them • Desire to work in a fast-paced work environment that offers great challenges and immediate rewards • Fluency in Dutch and English

What we offer Flow Traders offers you an exciting job and lots of opportunities within the most dynamic of environments with an excellent compensation package. We encourage employees to take advantage of opportunities to work internationally within the firm to maintain and grow our success as a worldwide leading proprietary trading house. Providing you with a chance to develop and succeed in one of our offices abroad is part of our employee investment strategy.

We believe it is important for our employees to experience different cultures and pursue professional as well as personal development opportunities to become well rounded contributors to

42 • Companypresentation

Flow Traders’ atmosphere and ambitions. Within Flow Traders there is the possibility for a transfer to our New York or Singapore offices for a stay abroad for three years or more.

The application process If you are a recent graduate and interested in the Junior Trader position you may apply through the following Application Process: • Apply to the Junior Trader Amsterdam position on www.flowtraders.com.

• A Recruiter will contact you if you are selected for the first round assessment.

• First-round is a numerical test and capacity test at our Amsterdam office.

• Second-round interviews are conducted right after finishing the tests (only when test results are positive) by one or two Traders and the Recruiter • Third-round is a case interview conducted by two traders

The In-house Trading Challenge Flow Traders offers students and potential candidates the opportunity to learn more about our company and our way of trading by organizing several Trading Challenges during the academic year. During a Trading Challenge one of our Senior Traders presents and walks through an analysis of real world examples of products and market strategies. During the challenge participants will compete against each other by identifying trading opportunities in a series of market simulations. The “winner” of each challenge will receive a prize. After the challenge the participants have the opportunity to speak one-on-one with our Junior Traders during an informal lunch.

For more information on the Junior Trader position or our Trading Challenge, please contact Dainahara Polonia, on +31 20 799 6799 or apply via our website; www.flowtraders.com.

Companypresentation • 43

fsrforum • volume 15 • issue #3

Word of the chairman

Sep Vermeulen

Dear members, Spring has arrived and as we progress through the academic year many of the planned events see the light of day. It is very rewarding for our board to see that the events we worked on are now successfully taking place. The next two months will bring you the European Finance Tour, Female Business Tour, Traders Masterclass, Asset Management Tour and the finals of the Dutch CleanTech Challenge 2013!

Looking back at the past half year I can say that I am proud of what we have organized as board of the FSR. However, we could not do it without our committee members, which work very hard for all the activities the FSR has to offer during this year. As a reward for all the hard work we will travel to a beautiful European city during our Active Members weekend, the exact location is still top secret. If we look at past years’ experience it will be a great weekend.

The Female Business Tour is taking place at 11th and 12th of April this year. Twenty female top

FSR News

Column Tycho van der Gugten

46

students will be invited to meet the companies and work on cases for BCG, APG and ING during this two-day event. It is already the third edition as the first two proved to be a great success.

One of the new events this year is the Traders Masterclass. In cooperation with Alex Academy we will show 80 motivated students all the ins and outs of private trading. Turbos, straddles and futures, everything will be explained. The other new event will be ‘Valuation’. During this event, 24 selected students will solve M&A cases in cooperation with BDO Corporate Finance and Ernst & Young Transaction Advisory Services. In advance they will get a renowned training including the newest valuation methods, provided by ‘Training the Street’. The full reports of these events will be presented in the next FSR Forum.

Column Merel Vlasveld

47

As our board year is officially halfway we have started to look for our successors, the XVIth FSR board! If you would like to challenge yourself, get in touch with numerous companies, students and professors then an FSR board year is your next step! We are looking for six highly motivated and capable students to take over and lead the FSR for a full year. If you are interested in a position you can visit www.fsr.nl for more information regarding positions, application and deadlines.

We are currently heading towards the end of the year, however this will be the time to start planning your next move and I am sure the FSR can be very helpful with that. I hope to see you all during one of our master classes, workshops, cycles or other events. Also feel free to come to our office on H14-06, if you have any questions, or just for a coffee and a chat!

Financial Business Cycle

50

Best regards, Sep Vermeulen Chairman XVth FSR Board

Dinners

44 • FSR news

51

fsrforum • volume 15 • issue #3

Loss of triple-A credit rating 'largely symbolic' Most recent news update about the subject.

Business secretary attempts to shrug off Moody's decision to downgrade UK, while former chancellors back George Osborne.

Vince Cable told the Andrew Marr Show there was 'no reason why the downgrade should have any impact'.

The business secretary, Vince Cable, has dismissed the loss of Britain's triple-A credit rating as "largely symbolic", while two former Conservative chancellors have offered support to the beleaguered George Osborne.

With financial markets braced for sterling to fall when the City returns to work on Monday, Cable attempted to shrug off Moody's decision to downgrade the UK to AA1 from AAA on Friday night.

"In terms of the real economy, there is no reason why the downgrade should have any impact," Cable told BBC1's Andrew Marr Show. "These things do not necessarily affect the real economy, but they do reflect the fact that we are going through a very difficult time."

Cable pointed out that rating agencies – whom he dismissed as "tipsters" at one point – had also downgraded the US and France recently without causing panic, and he dismissed suggestions that the government should change its economic course. "There are some positive things happening," he insisted, pointing to recent employment data.

But Osborne's predecessor, Alistair Darling, argued that Moody's move should be a wakeup call for the UK. "We're absolutely stuck. There's been no growth for the last two years," Darling told Sky News. He argued that the government's inexperience and "perhaps a touch of recklessness" had led Osborne to repeatedly cite the UK's credit rating as such an important yardstick.

"I'm sure he [Osborne] will reflect on whether it was wise to go on about the triple-A rating so much," Darling said.

Lord Lawson said it was vital that Osborne reassured the financial markets that he was committed to his deficit reduction strategy. "Standing firm is a pretty good policy at the present time," Lawson said.

Ken Clarke predicted it would take "several more years" before Britain recovered its triple-A rating with Moody's and returned to solid economic growth.

"People have far more confidence in Britain than in many other western countries who have got into trouble through profligate economic policies," he said.

Some City experts fear the pound could tumble on Monday, when investors get their first chance to respond to Moody's downgrade. Gilts, or UK sovereign debt, could also be hit, pushing up Britain's cost of borrowing.

However, a full-blown panic is not expected.

"It will be interesting to see how investors react," said Paul Griffiths, co-global head of fixed income at Aberdeen Asset Management.

"The immediate concern is likely to be a further weakening of the pound, which has already had a move lower in the first few weeks of this year.

"This will be immediately beneficial for our experts, but the medium-term risk is this sees inflation imported with the rising cost of goods and services from abroad," Griffiths explained.

Howard Archer, chief UK economist at IHS Global Insight, agreed that the pound was "particularly vulnerable", having already fallen almost 7% against the US dollar since the start of the year.

Bron: http://www.guardian.co.uk/politics/2013/feb/24/triple-a-credit-ratingvince-cable

Darling's argument that Osborne should rethink his strategy was dismissed by two other previous inhabitants of No 11.

FSR news • 45

fsrforum • volume 15 • issue #3

FSR Former board member

Tycho van der Gugten

It has been approximately seven years since I was a board member of the FSR. In the 8th board of the FSR I performed the function commissioner International Research Project (IRP).

I look back at a period full of exciting activities which has been a great learning experience for me. By performing a board-membership next to my studies I have been able to develop myself in areas such as organisational, leadership and communication skills. Furthermore, during the board year I got to know a large variety of companies, which was real added-value to the theoretical knowledge learned during my studies. Next to these achievements, above all it was a very fun year with my fellow board members in which we build a strong friendship with each other. After many years we still meet at least every month for dinner and drinks and go on a city trip once every (other) year.

The most important event in my board function was at the end of the academic year: the International Research Project to Buenos Aires. Together with my project-committee, I set-up the research project during the academic year by determining the research subject (Mergers & Acquisitions), recruiting participants and organizing several company visits in Buenos Aires. During the planning of company visits, we experienced the ‘mañana mañana” culture of Argentina: while we had some trouble to plan and arrange visits a few months in advance to our arrival in Argentina, in the last weeks before the start of the company visits program we received many confirmations and as a result had to prevent the program not to become too intensive. During the 2,5 weeks that we spend in Buenos Aires, we visited a number of accountancy firms, banks, corporates, legal institutions, the Dutch Chamber of Commerce and the stock exchange of Buenos Aires. Next to these visits we had various social activities. I strongly remember the guided bike tour that we had with the whole group (which is pretty unusual in a crowded city such as Buenos Aires) and the tango lesson that we took.

In particular I remember the closing dinner of the IRP, where we as an organising committee received high appreciation for the project. After a year of hard work it was really rewarding to see the enthusiasm and gratitude from all participants.

After such an intensive but exciting year it was pretty hard to flow back into the life of being a full-time student again.

46 • FSR news

­ ortunately my fellow board members were in the same F phase of their studies, and we therefore shared most lectures.

A few months after my graduation, I started at Capgemini Financial Services as a consultant, where I now work for almost two years. Although Capgemini is an IT consultancy firm, there are several opportunities for students with an economic background. Capgemini is an international firm with a large group of young, motivated employees and multiple study backgrounds. The projects at Capgemini Financial ­Services are balanced between business and IT. Up until now, I performed projects at ASR, ABN AMRO and ING in the field of business analysis and business process management.

Working for Capgemini gives me the opportunity to develop my knowledge within several fields of expertise and particular in the area of general project management.

I strongly advice everyone to become active at the FSR: It will contribute to your further career as it enables you to build a professional network and develop your personal skillset. But above all it gives you a great year with many friendships as a result!

Passport Name Tycho van der Gugten Age 29 Residence Rotterdam Employed at Capgemini Current position Consultant Which FSR Board 8th (2005 – 2006) Board function Commissioner International Research Project Study Financial Economics Year of graduation 2010 Which car do you drive Fiat Punto Evo What do you drink on a Friday night Beer Life Motto You won’t regret the things in life you have done, but you will regret the things in your life that you did not do.

fsrforum • volume 15 • issue #3

FSR Member

Merel Vlasveld

Passport Name Merel Vlasveld Age 24 Residence Amsterdam Study Master Accounting, Auditing & Control FSR event Bachelor Accountancy Day, Female Business Tour, Big 4 Cycle, International Research Project Job at KPMG Department of job Audit Which car do you drive A red Audi A1 Life motto I don’t mind living in a man’s world, as long as I can be a woman in it.

How I came into contact with the FSR? Well, being quite the feminine type, it was the FSR’s Female Business Tour that immediately appealed to me. And, being a 3rd year Bachelor student and not too sure yet about my which master to choose, it was a great opportunity to get a taste of the business world. Visiting companies from various lines of business gave me a good impression of my career perspectives.

By far the biggest FSR-event I was involved in during my years at Erasmus was the International Research Project 2012. Triggered by the 15m² Ho Chi Minh billboard on the campus, I decided to apply. Not knowing who and what I would encounter at the other side of the world, I was a bit scared at first. Together with twenty other students, I got on a eleven hour flight to Bangkok. What followed was a week of company visits, luxury dinners and some crazy nights. After having spent about ten days in Thailand, we flew to Vietnam.

After the official FSR-program, eight of us continued our journey by backpacking through Cambodia. The trip was a great experience and I made some good friends.

By this time, I had discovered that I was most passionate about Audit. Then, having to choose between the four big players in this field, the Big 4 Cycle was the absolute best way to get in touch with potential employers. In a month time, I had visited Deloitte, PwC, Ernst & Young and KPMG. And after these inhousedays, I was able to strike out two of them.

Continuing close contact with recruiters and staff from both KPMG and Ernst & Young, I was in serious doubt!

Somewhere in 2011, I applied for the KPMG Business Course.

The admission procedure consisted of several rounds and included a check on my CV, online tests and an interview with both a recruiter and a supervisor. Within a week, I received notification that I was selected to go to Madrid! I had the time of my life, driving around Madrid in Go-Carts with thirty students and KPMG-staff. Besides the sight-seeing and partying, there was also a serious side to it. We worked really hard on a case, having to present our solution in front of KPMG-partners within two days. Overall, it was a great experience and I got to know a lot about auditing, KPMG and their staff. It was during this week, that I noticed how much fun the KPMG-staff had among each other, and I decided that I wanted to be part of that!

The feeling was mutual and KPMG offered me an internship, right after Madrid. I gladly accepted and wrote my thesis at KPMG. And, long before my internship was over, they offered me a job at their headquarters in Amstelveen!

I graduated, moved to Amsterdam and started working as a KPMG-auditor on the Corporate Clients department. My portfolio is quite diverse – holding a Fortune 500 telecommunications and television company, an Indian oil and gas company and a famous museum in Amsterdam.

What I like about the diversity in clients that I serve, is that I can broaden as well as deepen my skills at the same time. For example, at the Fortune 500 company I deepened my know­ ledge by spending an entire month auditing their complex tax position. Quite a challenge, I can tell you that! The month after that, however, I had seen every aspect of the financial statements of the museum, as it was a lot more compact.

Hence, I develop broad auditing skills at smaller clients, as it is easier to oversee.

As an auditor, the scenery changes every week since we spend a lot of time at our clients’ office. And it is the difference in workspaces, client businesses, team compositions and specific auditing issues that keep the work interesting. I believe that starting in the field of audit sets a great base for your career.

With the title of RA (Registered Accountant) behind your name, there is a world to discover.

Working at KPMG for six months, I have begun to get really close with my colleagues. In fact, I am going (après-)skiing with sixty of them in April! The corporate culture at KPMG in short: Work hard, play hard. My team and I really pulled up our sleeves during ‘busy season’ (January through March), but compensate this by going to France for a couple of days to relax. Overall, I find the KPMG staff to be young, intelligent and dedicated people. The commitment and drive of those around me foster my own ambitions – helping me to grow and learn how to fly!

FSR news • 47

fsrforum • volume 15 • issue #3

Update International Research Project 2013

The International Research Project is in the midst of its busiest and most exciting period! After our intensive promotion campaign we selected the participants in December, and soon after we had our official kick-off at the headquarter of Ernst & Young. During the Christmas break we had some time to relax, however, not for long because the first session with the Erasmus professors was already around the corner! In January the group first met with Sophie Hoozee and Ronald Huisman, the professors from the Erasmus University, who will be guiding the research.

After the brainstorming session the research really started to take shape. At the end of the session we were able to arrange the topics of interest, which we received from Right To Play (RTP), into five sub-questions. In short, the groups will be looking at fundraising models, benchmarking, the Chinese perception of charity and RTP, the availability of capital in Beijing and lastly the possible impediments of the Chinese business climate.

Besides the research for RTP, we have also had our first inhousedays. Firstly, Varova gave a guest lecture at the campus. In their lecture they explained more about hands-on investments and the proceedings of the investors. At the closing drink, the participants had the opportunity to ask all their remaining questions and became more familiar with this sector. Thereafter, we visited Robeco at their office in Rotterdam. During the day, several experts presented their knowledge on China. They also provided some feedback on the research and offered the participants some new insights on their topics. During the inhouseday of PON, the employees gave the students a glimpse of the world of family owned businesses. After the case, we received a guided tour at the area of Volkswagen, Seat and Porsche and learned about their wide range of activities. The last inhouseday before publication of the FSR Forum was at PwC Rotterdam. The participants were challenged with a case on charities and also gained some knowledge on the subject of corporate responsibility. During the informal drink they had the opportunity to learn more about the Finance and Accountancy starter functions. Next on the program are company visits at Ernst & Young, KPMG and a guest lecture of Laudame. We are very pleased with these inhousedays that offer the students the companies’ expertise on China and fundraising in China and also the possibilities as a starter at one of these companies.

In the meanwhile, we are getting closer and closer to our departure date: May 5th. We have already arranged for a number of interesting company visits at our international destination Beijing, providing us with a sneak peak in the Chinese way of doing business.

With the first informal drinks and inhousedays in the back of our mind, we are confident that we have a great group of students and we cannot wait for the final stage of the IRP to start!

48 • FSR news

BECOME A BOARD MEMBER AT THE FINANCIAL STUDY ASSOCIATION ROTTERDAM!

Deadline: 12th of May

As a board member you have the opportunity to distinguish yourself from other students and to get connected to the corporate world. You will develop a highly valued set of skills and gain insight into the job market, making it easier to choose your own career path. If you want to know more about a board year at the FSR, make sure to contact us, either by sending an e-mail to chairman@fsr.nl, visiting our website www.fsr.nl or dropping by at our office at H14-06.

Interested?

Visit our drink on 18th of April

fsrforum • volume 15 • issue #3

Financial Business Cycle 2013

Last December and January the Financial Business Cycle (FBC) took place. During the FBC we visited eight different companies: two banks, two trading houses, two consultants and two multinationals.

The first week started with a visit at Kempen&Co, then Shell and finally Flow Traders. At Kempen&Co, we were welcomed with some coffee and tea, after which we were given several presentations. After that they told us more about some of the main divisions Kempen&Co has: Securities, Corporate Finance and Investments. After the presentations, an interesting securities case was presented and finally we had a valuation case. The day ended with a drink and dinner with a great view at one of the top floors of the building.

The next company visit was at Shell. After being informed about the safety instructions the program began with several presentations given by the recruiters, trainees and more experienced employees. The case Shell prepared for the participants was an original manner to show the difficulties of the multicultural environment of Shell which every Shell employee has to cope with. After the game the recruiter told us more about the application process at Shell and invited us for drinks.

The last company we visited that week was the Dutch trading company Flow Traders. During the first presentation of the day it was clear that working at Flow Traders requires a lot of mathematical skills. The relatively young company wants to grow substantially, which takes a lot of hard work. After this presentation we got a tour on the trading floor. During this tour we got the opportunity to really experience the working atmosphere at Flow Traders. Next, we had to do some quick thinking in a bid and offer game and one of the participants went home with a bottle of champagne. Finally we had some drinks with some trainees and traders.

The next week a visit at Ahold, McKinsey and Optiver was planned. At Ahold we had a presentation about the possibilities at as a starter. After lunch there was a case about investment decisions in foreign countries. After the case we had a drink in the bar of Ahold where we could see the newest innovations.

The next day the visit at McKinsey was planned. We arrived at the Amsterdam office and started with a presentation about

50 • FSR news

McKinsey and could ask anything about the company. After the presentation, someone from McKinsey gave a presentation about a micro financing organization which he wanted to start. The case was about that company how he could launch the website successfully. The day ended with drinks, where even more employees joined us to share their experiences.

The last inhouseday of this week was at Optiver. The day started with an introduction about Optiver and the possibilities within Optiver followed by an Optiver trader talking about his experiences. This was followed by a tour on the trading floor and then there was a trading game in which the participants could test their “Bid/Ask” spread skills. The day ended with some nice drinks and “Friday afternoon pizzas”.

In January, the last two inhousedays were planned. First we went to Rabobank. The day started with a tour through their central office in Utrecht. Getting started with a presentation about the structure of Rabobank, which is a bit different from other banks as it is a ‘cooperative’ bank. The next presentation explained the different ‘paths into Rabobank’. After this presentation a couple of trainees and managers joined us to tell more about the Global Financial Markets Traineeship, the Corporate Management Traineeship and The Young Professional Program followed by a short speed-dating session with trainees and employees of the different departments and traineeships. This allowed the participants to ask questions and listen to the different experiences of the trainees. The next part was a hedging case. Together with your team you had to make decisions about hedging risks. Around 5 pm we had some drinks and talks about the experiences of the persons working at Rabobank.

The last inhouseday of the Financial Business Cycle was at OC&C. During this last day, we had a presentation about the possibilities at OC&C, the culture and what kind of work you will do as a consultant. After the presentation we had a case about the prediction of Jenever consumption in the upcoming years. During the case you could see what kind of decisions you have to make as a consultant. At the end we had some drinks and snacks while meeting employees of OC&C.

fsrforum • volume 15 • issue #3

FSR Multinational Dinner and FSR Banking Dinner

This year the FSR organized both the Multinational and the Banking Dinner. Both dinners took place in restaurant ‘De Harmonie’ where a selected group of students got the opportunity to meet various renown companies. During a three course dinner the students met potential employers in an informal setting.

On the 22nd of January the FSR Multinational Dinner took place where students got to meet ExxonMobil, KPN and PostNL. One week later on the 29th of January the FSR Banking Dinner took place where students got to meet ABN AMRO, Kempen & Co, NIBC and SNS REAAL. After everyone was welcomed with a drink, the evening started with a short introduction by representatives of the participating companies. The introduction was followed by the dinner itself, which consisted of three delicious courses. After each course the students switched tables to join another company. This way the students could meet the representatives of the companies and ask their burning questions about the experiences of the employees. If there were any questions left unanswered, the students had the chance to have a chat and a beverage with the representatives at the closing drink which took place after the dinner.

Both the FSR Multinational Dinner and the FSR Banking Dinner have given the students the opportunity to extensively become acquainted with the corporate culture, activities and differences of the participating companies in an informal setting. From the perspective of the FSR we considered the dinners to be very successful and therefore we would like to thank all the involved parties!

FSR news • 51

fsrforum • volume 15 • issue #3

Opening Stock Exchange at Beursplein 5

To settle our cooperation with Duisenberg School of Finance (DSF) they invited us to come over to Amsterdam to open the stock exchange on Thursday the 7th of March. We arrived around 8.30 am at Beursplein 5 and after a quick introduction to the c­ eremony and the Ney York Stock Exchange (NYSE) Euronext, we were heading to the gong. Exactly at 9.00 am we stroke the gong and opened the day’s trade. Afterwards we received our very first ‘tombstone’ that displays the Financial Study association Rotterdam and Duisenberg School of Finance at

FSR Agenda

Beursplein 5.

CleanTech Challenge

January-April Grow your green ideas!

April The Valuation One day theory, one day practice

Female Business Tour It might be a men’s world but it would be nothing without women

Traders Masterclass Beat the Bear, be the Bull

FSR Interest Drink Are you up for a new challenge?

April-May International Research Project Using your intellect for a charity!

May Bachelor Accountancy Day Will you choose for a career in accounting?

Asset Management Tour What’s your investment strategy?

European Finance Tour Exploring European financial world

52 • FSR news

www.werkenbijpwc.nl

Soms ben je tevreden

Soms drijf je jezelf tot het uiterste Je hebt tijdens je studie alle mogelijke kennis opgedaan.

En nu wil je aan de slag. Op een plek waar je al je ambities kwijt kunt. Waar de lat hoog ligt en waar je samenwerkt met professionals. Je start je carrière vliegend en gaat recht op je doel af. Dat is: het beste in jezelf naar boven halen.

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Š 2012 PricewaterhouseCoopers B.V. (KvK 3412089) Alle rechten voorbehouden.

© 2012: Ernst & Young Nederland LLP. All Rights Reserved.

Vallen. Opstaan.

Vallen. Opstaan.

Vallen. Opstaan.

Het ideale carrièrepad.

Vergeet het. Een kaarsrecht, steil omhooglopend carrièrepad bestaat niet. Je krijgt te maken met ups én downs. En het mooie is: je wordt er alleen maar sterker van. Zeker als je werkt bij een organisatie die je helpt en stimuleert; ook in moeilijke tijden. Want die blijken achteraf vaak het belangrijkst te zijn.

Zo’n organisatie is Ernst & Young.

Kijk voor jouw mogelijkheden op ey.nl/carriere