DETERMINANTS OF BANK LENDING PERFORMANCE IN GERMANY**

Transcription

1 Schmalenbach Business Review Vol. 52 October 2000 pp Ralf Ewert/Gerald Schenk/Andrea Szczesny* DETERMINANTS OF BANK LENDING PERFORMANCE IN GERMANY** Evidence from Credit File Data ABSTRACT We empirically identify factors that can explain the financial performance of bank lending activities. We also analyze the individual bank s evaluation of a loan s risk. We use our results to test theoretical hypotheses on the impact of certain parameters on credit terms and distress probabilities. We find ratings act as an important factor in the bank s lending policy. Ratings reflecting higher risks lead to higher interest rate premia. The findings on collateralization are less clear and do not fully support any of hypotheses that are advanced to describe the role of collateral and covenants in credit contracts. 1 INTRODUCTION Commercial banks often voice the opinion that the traditional credit business has come under considerable competitive pressure. As a result, credit margins tend to decrease and the profitability of lending becomes problematic. In banks with several lines of business (e.g., German universal banks offering virtually all types of financial services), granting credit to a firm seems to be viewed more as a door opener for other transactions (e.g., investment banking activities), which banks believe may be more advantageous. Thus, arguments of cross-selling are often considered major factors in support of the lending business. The new developments in risk management and bank supervision has exerted additional pressure on the traditional lending business. The Basle commisssion for bank supervision is in process of developing new standards on capital adequacy of financial institutions, which will result in reform this particular area. Among experts there is already widespread agreement on the fact that internal credit ratings will be the future criterion for the equity requirements. Since we include * Prof. Dr. Ralf Ewert, Dipl.-Wirtsch.-Inf. Andrea Szczesny, Lehrstuhl für Controlling & Auditing, Johann Wolfgang Goethe-Universität Frankfurt, Mertonstr. 17, Frankfurt am Main, Prof. Dr. Gerald Schenk, Berufsakademie Heidenheim, Wilhelmstraße 10, Heidenheim. ** This paper is part of a research project on Credit Management in Germany initiated by the the University of Frankfurt s Center for Financial Studies (CFS). We would like to thank all the banks that participated in this project and their representatives for their willingness to cooperate on this research. We also thank Jörg Beißel, Kai Forst, Jan-Pieter Krahnen, Ulrich Rendtel, Bernd Rudolph, Wolfgang Schwerdt, and Martin Weber for comments on earlier drafts of this paper and helpful suggestions. We gratefully acknowledge very helpful comments from one anonymous referee of the Schmalenbach Business Review. We are responsible for all remaining errors. 344 sbr 52 (4/2000)

2 Bank Lending Performance the banks internal credit ratings in our data set, we are able to address the role of ratings in the lending business. In our paper, we study the empirical determinants of bank lending performance. We are particularly interested in answering the following questions: Are there any empirical similarities between characteristic features of credit relationships and measures for the success of the lending business? Do the internal credit ratings predict liquidity crunches or defaults and thus help to improve the profitability by pricing the underlying risk? Answering these questions will yield insights on the empirical validity of theories that try to explain reality and can also help practitioners seeking for alternative ways in which to improve the profitability of their credit transactions. To address these issues, we apply two measures as proxies for the success of lending contracts: The first measure is based on the idea that a loan contract s profit results from an interest rate premium over a rate at which the funds could have alternatively been invested. Hence, it is a rough measure of the surplus the bank could expect if there are no problems during the life of the credit contract. The second measure captures potential problems by looking at the frequency at which disturbances (e.g., delay of principal and/or interest payments by the borrower, technical default by the borrower, or even insolvency) occurred. Such disturbances imply either a definitive loss of payments for the bank, additional costs due to renegotiations, active involvement in the borrower s firm policy, use of collateral, or perhaps all of these factors. Thus, the higher the frequency of such disturbances, the lower the profitability of a credit contract. Our paper is linked to the recent literature on relationship banking 1. It contributes to that literature in several ways: Concerning the surplus question, it augments the existing literature by studying a different sample of data from German banks. Furthermore, we were allowed to use confidential data contained in each respective bank s credit evaluation files. This makes possible the use of various measures (e.g., the bank s internal rating of a borrower) that are somewhat different from (and more comprehensive than) traditional financial accounting measures. In addition, our study tries to incorporate aspects such as cross-selling and intensity of competition as independent variables for the surplus question. Third, up to now and as far as we know, the disturbance question has not been pursued in the relationship banking literature. We study these questions by using regression techniques for panel data and durations analyses. A special feature of our analysis is that it includes not only data from financial statements, but also from the individual credit contracts. Finally, when we combine the results of our two regressions, we are able to test several hypotheses regarding the use of credit contract variables (i.e., collateral and bonding). 1 See e.g. Petersen/Rajan (1994); Berger/Udell (1995); Blackwell/Winters (1997). sbr 52 (4/2000) 345

3 R. Ewert/G. Schenk/A. Szczesny The paper is organized as follows: In Section 2 we discuss the main questions and theoretical background. Section 3 provides a short overview of the sampling procedures. Section 4 examines the surplus question. Section 5 concentrates on the disturbance question. Section 6 contains a short summary of the findings and concludes with some suggestions for future research. 2 THEORETICAL FRAMEWORK The theoretical framework of our analysis starts with a model of a neoclassical credit market in which the terms of credits clear the market. If collateral and other restrictions (covenants) remain constant, the interest rate is the only price mechanism. With an increasing demand for credit and a given customer supply, the interest rate rises, and vice versa. If risk is added to this model, the future interest payments and repayments are stochastic. In this case a surcharge on the alternative investment without risk is calculated (interest rate premium) relative to the underlying default probability 2. According to this theory, the corresponding failure risk should affect the pricing. Hypothesis 1: The higher the failure risk of the borrower, the higher the interest rate premium 3. In this context, collateral has no effect on pricing. The interest rate refers only to the amount of credit without collaterization. For this reason, a bad lender who would like to have the same nominal interest rate like a good lender is compelled to offer more collateral 4. This leads to a negative relation between the amount of collateral and the interest rate premium. (See hypothesis 2a further on in this section.) We now add more complexity to the simple credit market described above, taking into account the fact that financial transactions are intrinsically characterized by asymmetric information. Borrowers generally have private (internal) information about their projects that is more accurate than the information possessed by lenders. As a consequence, a lender could still be uncertain about the default risk of a loan contract and have difficulties in assessing and controlling the nature and behavior of the borrower. This framework leads to phenomenons like adverse selection and moral hazard. The adverse selection problem occurs if lenders try to protect themselves against default risk by setting their contractual terms in a manner appropriate for the expected average quality of their loan applicants. In this case, high-risk borrowers could be encouraged to self-select into the loan applicant pool, while at the same time low-risk borrowers could be encouraged to self-select out of the pool. Moral hazard arises when borrowers who have internal information take hidden actions that increase their default probability. The probability for adverse selection or moral hazard increases with rising interest rates. 2 See e.g. Merton (1974). 3 See e.g. Diamond (1984). 4 See, for example, Rudolph (1984) or Saunders (1997). 346 sbr 52 (4/2000)

4 Bank Lending Performance A vast theoretical literature treats these topics 5. Stiglitz/Weiss (1981) show that credit rationing could be one of the strategies that a bank uses to avoid adverse selection. Rationing means that the loan amount granted is less than the amount requested. Other possible reactions manifest themselves in the terms of the credit contracts. Using arguments from the agency and signaling theory, better firms can signal their true quality by offering more collateral or covenants to bondholders 6. Better firms know that they will not suffer severely from offering more collateral and covenants, because they have a relatively low probability for the occurrence of conditions under which covenants are violated, the bank might use the pledged assets, or both. Thus, it pays for better firms to offer more collateral, covenants, or both in exchange for lower interest rates. According to this theory, and already suggested in the neoclassical model, we can formulate the following hypotheses: Hypothesis 2a: A negative relation should hold between the amount of collateral and/or the existence of covenants and the interest rate premium. Hypothesis 2b: We should further observe a negative relation between the amount of collateral and/or the existence of covenants and the distress probability. Practitioners often use a contrary argumentation amounts to a adverse signaling argument. According to their view, banks only require collateral and/or covenants for relatively risky firms 7. If the firm is instead classified as low risk, the bank dispenses with collateral and/or covenants. Hypothesis 3a: According to the adverse signaling argument, a positive relation should hold between the amount of collateral and/or the existence of covenants and the interest rate premium. Hypothesis 3b: A larger amount of collateral and/or the existence of covenants should be linked with a higher probability of default. If pure financial contracting theory 8 is used instead, the resulting impact is only clear for the individual firm but not in a cross-sectional analysis. According to this theory, lenders can form rational and unbiased expectations on a firm s future prospects. There are firm-specific agency-problems that can be mitigated by the use of collateral and/or covenants. Each firm chooses a specially designed credit contract that maximizes firm value by trading off additional monitoring and bonding costs against reductions in interest rate premiums. 5 See e.g. Jensen/Meckling (1976); Stiglitz/Weiss (1981); Bester (1985); Bester/Hellwig (1989). 6 For different contexts of such explanations see, e.g., Leland/Pyle (1977); Bester (1985, 1987); Chan/ Kanatas (1985); John/Kalay (1985); Besanko/Thakor (1987); Ewert (1988). 7 Bester (1994) gives some theoretical justification for this view. 8 See e.g. Jensen/Meckling (1976); Myers (1977); Smith/Warner (1979). sbr 52 (4/2000) 347

5 R. Ewert/G. Schenk/A. Szczesny Hypothesis 4: For a single firm, the use of collateral and/or covenants should reduce credit costs, resulting in a negative relation between these variables. However, in a cross-section that relation need not hold, because usually the firms with the most severe agency problems (which presumably are high-risk firms) find it advantageous to offer credit contracts including collateral and/or covenants. Thus, there could be a positive cross-sectional relation between the observed interest rate and the use of collateral and/or covenants, depending on which of the two effects (reduction of individual credit risks versus use of collateral and/or covenants by observably riskier firms) is stronger. We can conclue that only the signaling and adverse signaling hypotheses yield clear implications for the impact of collateral and/or covenants. Building a house bank relationship is another way to reduce information asymmetries and thus avoid moral hazard. The closeness of the relationship between firms and banks makes it possible for banks to price the default risk of firms in a more accurate way 9. This leads to the following hypotheses. Hypothesis 5a: Close relationships between banks and lenders are valuable and should lead to a decrease in interest rate premiums. Hypothesis 5b: In the subgroup of firms in a close bank/customer relationship, the default probability should be lower. The length of the bank/borrower relationship should affect the pricing. Petersen/ Rajan (1995) show that banks with a close relationship to borrowers can offer relatively low interest rates at the beginning of the relationship purposely avoiding adverse selection and moral hazard. In later periods they charge higher interest rates to compensate the earlier concessions. Hypothesis 6: Taking the borrower s quality as given, the interest rate premiums should increase in time. As we note in the introduction, the lending business is often viewed as just a door-opener for other transactions. This perception ignores the profits from lending activities. Hypothesis 7: The existence of cross-selling arguments should be linked with a lower interest rate premium. Some smoothing effects blur the relation between the above factors and the interest rate premium. A close relationship between firms and banks gives banks the opportunity to smooth interest rate premiums over time. According to Petersen/ Rajan (1995), banks follow this strategy to avoid adverse selection and moral hazard. 9 Theoretical arguments can be found, for example, in Diamond (1989, 1991). 348 sbr 52 (4/2000)

6 Bank Lending Performance Fried/Howitt (1980) and Berger/Udell (1995) mention further reasons for smoothing. For example, banks could offer insurance services when general interest rate levels are high, or to firms in financial shortage 10. Furthermore, the current interest rate level could affect the spreads 11. Hypothesis 8: Loan rate spreads are relatively small in times of high general interest rate levels, and vice versa. In periods of high interest rates, capital costs are high, so banks have trouble enforcing terms. In addition, the bargaining power implicit in the size of firms affects the terms. Hypothesis 9: With increasing firm size, the interest rate premium should decrease. Low default rates and losses combined with high interest rate premiums do affect the performance of the lending business. Therefore, we also analyze the relation between the observed probability of problems and several determining factors, especially the bank s rating system. The results of both regressions (i.e., the surplus and disturbance questions) must be considered in testing the respective theories, since the analysis for the disturbance question is a direct test of the relations between several determining factors and the observed probability of problems. Thus, these results can either corroborate or contradict the results of the premium-regression. 3 DATA In our study we use the common data set of a research project on credit management in Germany that was initiated by the Center for Financial Studies (CFS) in Frankfurt. This section presents an overview of basic information on the data collection that is necessary for the specific research questions of this paper. The CFS research project on credit policy was performed in cooperation with six leading German universal banks 12. This cooperation enabled researchers to systematically analyze the credit files of bank borrowers for the first time. The research project was restricted to medium-sized firms with an annual sales volumes between 50 and 500 millions DM 13. The sample comprises a randomly chosen cross-section of 260 borrowers over the seven years between 1992 and 1998, and includes an oversampling of potentially 10 See e.g. Fried/Howitt (1980) or Berger/Udell (1995). Fried/Howitt (1980) show that this smoothing is efficent for the bank, because the aquisition of new customers is more costly than customer maintenance. 11 See for a description of the money illusion phenomenon Machauer (1999) and for empirical evidence Berger/Udell (1992) and Machauer/Weber (1998). 12 Bayerische Vereinsbank, Deutsche Bank, Commerzbank, DG Bank, Dresdner Bank, and West LB. 13 Because of possible distortions we exclude East German firms. For such distortions see Harhoff/ Körting (1998). sbr 52 (4/2000) 349

7 R. Ewert/G. Schenk/A. Szczesny distressed firms. One of the criteria for inclusion in this subset is at least one negative rating (rating 5 or rating 6; see section 4.1) by the borrower during the observation period. Table 1: Descriptive statistics concerning the bank internal ratings Rating Observ. Total lines of Availment Collateral Spread in percent credit in DM in percent in percent in percent million (Mean) (Mean) (Mean) (Mean) 1 (very good) (good/above average) (average) (below average) (problematic borrower) (loan in danger/loss of loan) The complete credit files of each borrower served as the basis for the sample data collection. This information is supplemented by additional information on the borrower provided by different electronic data processing systems of the respective bank. Apart from the bank s internal rating, the data include information about the terms of credits under current account, investment credits, discount credits, credit by way of bank guarantee, and other credits, including all kinds of collaterization. These data are further complemented by some firm characteristics such as the legal form, branch of business, and important data taken from the firms annual reports or balance sheets. In those cases in which a credit decision or investigation was documented for a borrower, all variables of interest 14 were collected. To avoid a survivorship bias, the sample s population had to include all borrowers who matched the sampling criteria at some time during the observation period. That is why some relationships started in the years after Tables 1 and 2 provide some descriptive statistics. 4 THE SURPLUS QUESTION 4.1 VARIABLES This section describes the effects of different determinants on the pricing of credits in current account. Like Berger/Udell (1995) and Harhoff/Körting (1998), we restrict our analysis to this type of credit, since we expect more convincing results than for other forms of credits such as investment credits 16. We choose 14 See the data collection scheme in Elsas et al. (1997). 15 Further the observations of some firms do not cover the whole period due to missing values. 16 Berger/Udell (1995) argue that terms of credits in current account are more influenced by borrower quality than are other types of credits such as investment credits. They point out that other credits in relation to credits in current account tend to be transaction-driven rather than relationship-driven. 350 sbr 52 (4/2000)

8 Bank Lending Performance Table 2: Descriptive statistics Industrial sector (observations in percent): Manufacturing 29 Machinery 22 Construction 9 Trade 16 Other 24 Credit Contracts (observations in percent): Cross-selling (yes / no) 17 / 83 Covenants (yes / no) 66 / 34 Housebank relationships (yes / no) 38 / 62 Mean Std. Dev. Total lines of credit in DM million Availment in percent Collateralization in percent Spread in percent Number of bank relationships Sales in DM million the spread between the interest rate of the loan and the respective (3-month) Frankfurt interbank offered rate (FIBOR) as the dependent variable of our panelregression 17. We regress this interest rate premium (IRP) on firm and credit variables and on additional control variables for bank-specific effects. We examine the interest rate level by using a dummy variable HIGH, coded one in phases with general high interest rate level, and zero otherwise, with a FIBOR of six percent as cut off 18. We choose this dummy construction because during the observation period, the interest rate level decreases along with the average quality of the borrowers. Otherwise, due to this correlation it would have been difficult to separate the two effects. The rating (R12, R3, R4, R5, R6) reflects the bank s individual evaluation of the loan s risk and is essentially a compact and comprehensive measure of various quantitative and qualitative factors (e.g., the quality of the management, the market position of the firm, and its future prospects). The different internal rating systems of the five participating banks do not allow a homogeneous assessment of the quality of the borrowers in the entire data record. Therefore, we had to transform the individual rating systems into a uniform scheme More precisely: We compute the FIBOR-rate for a loan by taking the monthly FIBOR average for the month that the credit was granted to the respective firm. 18 During the observation period HIGH indicates phases of in average nine percent interest rate in contrast to four percent below the cut off. 19 The rating systems of the five banks and the transformation mechanism are described in detail in Elsas et al. (1997). Some descriptive statistics are shown in table 1. sbr 52 (4/2000) 351

9 R. Ewert/G. Schenk/A. Szczesny We constructed a system with six categories: 1 equals very good, 2 equals good/above average, 3 equals average, 4 equals below average, 5 equals problematic borrower, and 6 equals loan in danger/loss of loan. The variable R12 reflects categories 1 and 2, and variables R3 to R6 represent the categories 3 to 6. Although other papers integrate collateral requirements by using dummy variables 20 (e.g., one if collateral is pledged, zero if the loan is unsecured), because we had access to the banks credit files, we were able to incorporate collateral in a more detailed form. We use the collateralized percentage of the total lines of credit (COLLAT). As a value for collateral we take the internal evaluation of the liquidation value of collateralized assets on which banks base their decisions. For covenants we use a dummy (COV) to account for the existence of such provisions (e.g., direct and/or indirect dividend constraints 21 ). For collaterization in our specifications, we assume a sequential negotiation of terms. In the first step, banks fix collateral and/or covenants and then negotiate interest rates 22. To consider cross-selling arguments, we form a dummy variable (CS) which we code as one in case of cross-selling arguments in the credit file, and zero otherwise. Since cross-selling arguments probably take effect at the beginning of a relationship, we also include a dummy that reflects observations in the first five years of the relationship (CSYOUNG). To account for possible house bank aspects (HB), we incorporate a dummy coded one if the bank itself marked the relationship as a house bank relationship, and zero otherwise 23. HBBAD examines bad borrowers in a house bank relationship. To have this HB variable is an advantage in comparison with earlier studies in which the duration (DURATION) of the bank/customer relationship is used as a measure for the closeness of that relationship. We also use the latter variable in our study to account for time effects in the relationship. We further distinguish between the duration of the relationship in case of a house bank relationship (HBDURAT) and without this relationship (NBDURAT). In addition, the number of banks which a firm borrows from, NUMBANK, is a proxy for the closeness of the relationship between the bank and the borrower. This variable can also be viewed as a measure of the quality of the borrower and as an important indicator of firm size. Sometimes we use the number of bank relationships as a proxy that investigates if bank competition is reflected in loan terms 24. Firm size must also be considered by the standardized amount of total assets (LN_TA) This is the procedure used by Berger/Udell (1995) and Blackwell/Winters (1997). 21 For a conceptual analysis of the efficacy of such constraints see John/Kalay (1982), Kalay (1982), Ewert (1986, 1987), Berkovitch/Kim (1990) and Leuz (1996). 22 This assumption is also made in the analysis of Harhoff/Körting (1998). 23 For a more detailed procedure concerning the house bank definition see Elsas/Krahnen (1998). 24 For a discussion of the number of banking relationships see Harhoff/Körting (1998) and Machauer/Weber (2000). 25 LN_TA = ln(total assets). 352 sbr 52 (4/2000)

10 Bank Lending Performance Table 3: Hypotheses and Variables concerning the interest rate premium Hypotheses Variable (Sign) hypothesis 1: risk premium R3, R4, R5, R6 (+) (relative to R12) hypothesis 2a: neoclassical credit market as well as agency and signaling theory COLLAT ( ) COV ( ) hypothesis 3a: adverse signaling theory COLLAT (+) COV (+) hypothesis 4: pure financial contracting theory COLLAT ( ) COV ( ) hypothesis 5a: housebank relationship, especially in case of borrowers with relative low quality HB ( ) HBBAD ( ) NUMBANK ( ) hypothesis 6: compensation over time, especially in case of a housebank relationship DURATION (+) HBDURAT (+) NBDURAT (+) hypothesis 7: cross selling arguments, especially in case of young customers CS ( ) CSYOUNG ( ) hypothesis 8: money illusion HIGH ( ) hypothesis 9: bargaining power of large firms LN_TA ( ) NUMBANK ( ) To identify whether banks apply different procedures to the pricing of loans and/or to control for different sampling procedures, we use dummy variables for the six banks (B1 B6) that participated in our research project. Table 3 gives an overview of the variables described above together with hypotheses itemized in section RESULTS The advantage of using panel data is that we can estimate a random effects model. A random effects model is an appropriate specification if a number of individuals (firms in this case) are randomly drawn from large populations (here, bank customers). This method offers the advantage of eliminating borrower- and timespecific effects by adding separate random error terms for borrowers and time 26. The results are shown in table See Baltagi (1995). sbr 52 (4/2000) 353

11 R. Ewert/G. Schenk/A. Szczesny Table 4: Regression of the interest rate premium (IRP) Specification 1 Specification 2 HIGH *** (0.0893) *** (0.0881) R * (0.1510) * (0.1499) R *** (0.1642) *** (0.1630) R *** (0.1711) *** (0.1989) R *** (0.2432) *** (0.2590) LN_TA ** (0.0766) ** (0.0768) COLLAT ** (0.0012) ** (0.0012) COV (0.1228) (0.1229) CS (0.1418) (0.1494) CSYOUNG *** (0.3663) HB (0.1428) (0.2191) HBBAD *** (0.2344) DURATION * (0.0041) HBDURAT ** (0.0058) NBDURAT (0.0052) NUMBANK (0.0163) (0.0164) B * (0.2151) ** (0.2200) B (0.2662) (0.2690) B (0.2531) (0.2557) B (0.2646) (0.2665) B (0.2890) ** (0.2940) CONST *** (0.8836) *** (0.8854) Obs R Sq. within R Sq. between R Sq. overall Significant at the * 1 percent level / ** 5 percent level / *** 10 percent level. Standard deviations in brackets. The first specification shows a simple model, and the second specification presents the detailed hypotheses. Both specifications show clear results concerning the ratings, thus supporting hypothesis 1. The coefficients are strongly significant for all rating dummies and increase in the rating number. The higher the risk, the higher the interest rate premium. A lowering of one rating class goes hand in hand with an increase in interest rate premiums of about 0.2 or 0.3 percent points. When we test hypothesis 8 (if the loan rate spreads decrease with a rising interest rate level), we find the expected negative sign. Phases of high general interest rate levels differ from phases of low general interest rate levels by about 4 percent points, which is generally accompanied by a 1 percent point difference in the spreads. The coefficient for the collateral variable COLLAT is positive and significant in each of these regressions. The coefficient indicates only a slight effect on the 354 sbr 52 (4/2000)

12 Bank Lending Performance spread: An increase from zero to 100 percent collateralization leads to an increase in the interest rate premiums of only 0.3 percent points. The coefficient for COV is not significant. These results are not consistent with the combined agency and signaling arguments outlined in hypothesis 2a. According to these arguments, we should observe higher interest rate premiums for firms with lower collateral and fewer covenants, contradicting the results in table 3. On the other hand, if the adverse signaling hypothesis holds, then good firms are not required either to pledge much collateral or to install covenants. Therefore, they should receive better credit terms. This view is confirmed by our regression (hypothesis 3a). The empirical results from using the pure contracting theory are consistent if we hypothesize that the riskier firms find it more profitable to use collateral and to install covenants, and that this effect cross-sectionally dominates the individual interest-reducing effect of using such mechanisms (hypothesis 4). However, as mentioned above, the premium-regression alone does not allow for a final judgement on the three competing hypotheses. Consistent with hypothesis 7, cross-selling in the first years of a relationship carries a negative sign, indicating that chances of cross subsidies between lines of business leads the bank to reduce the interest rates. Later on, it makes no difference. Across all observations, we can identify a slight effect of the duration. This effect is shown by specification 1. When there is a house bank relationship, the effect is greater. Duration together with the house bank characteristic (HBDURAT) displays a positive sign and an increase of the interest rate premium of 0.01 percent points per year. This must be interpreted together with the result concerning the house bank status in general. A firm with a close relationship to the bank (HB) can expect better terms than other firms, but this advantage gets smaller over time. This finding supports the findings of other empirical studies (hypotheses 5a and 6) 27. For bad borrowers the advantage of house bank relationships is considerable (HBBAD). This confirms the supposition that banks are likely to help firms in financial distress, expecting future earnings because of their house bank status. Without a house bank status, competition among banks prevents such concessions. Since we exclude observations of firms with obvious financial problems, all results concerning house bank effects lose their significance. The variable NUMBANK is not significant. Since Machauer/Weber (2000) show that NUMBANK is essentially an indicator for firm size, the result does not come as a surprise, because we include total assets as a direct measure of firm size in our analysis. As the size of the firm increases, its bargaining power as a borrower grows and lowers the interest rate premiums. Beyond the risk-reducing effect of firm size covered by the ratings, an increase in total assets leads to a decrease in the spreads (hypothesis 9) See Petersen/Rajan (1994); Berger/Udell (1995); Blackwell/Winters (1997). 28 This is consistent with the findings of Blackwell/Winters (1997). sbr 52 (4/2000) 355

13 R. Ewert/G. Schenk/A. Szczesny Spreads vary greatly between different banks. There is also a high variance within each banks loan portfolio. Several reasons could account for this. On the one hand, our standardization of the ratings may be not as accurate as we thought. On the other hand, differences in the sampling process could have caused the relation between spreads and the respective banks. Alternatively, real differences in the arrangements of terms could be the reason. By assessing the goodness of fit of the regressions, we must consider that we have increased the variance of terms by raising the number of potentially distressed firms. This oversampling stands out from other empirical analysis 29. Nevertheless, our explanatory power with a R 2 (overall) of about 0.20 lies in the middle range of other results. If financial problems are obvious, banks react and change the terms, for instance by deferment of payments or demanding additional collateral. It is obvious that this procedure increases the variance of the terms of the contracts. If we exclude these problematic oberservations, the R 2 (overall) rises for example to a value of DETERMINANTS OF DISTRESS-PROBABILITIES 5.1 PROCEDURE AND VARIABLES To gain some insights into the determinants of the frequency of potential disturbances we perform a panel logit analysis with random effects. Our aim is to investigate whether there is any systematic relation between characteristic features of the loans and the occurence of problems. We first flag those observations with obvious problems 30. An observation is marked if one of the following events occured: Initiation of formal insolvency proceedings Utilization of collateral by the bank Valuation adjustments of the bank s claims Initiation or planning of restructuring activities by the bank Termination of the bank s engagement These criteria comprise a broad spectrum of potential problems that might occur during a credit engagement. Each criterion involves specific costs that lower the 29 For example Petersen/Rajan (1995) and Berger/Udell (1995), who have no oversampling of potentially bad borrowers. 30 The analysis in this section concentrates on the complete credit engagement (i.e., the firm that is granted credit by a bank) rather than looking at single credits. The reason is that potential disturbances can hardly be traced to any single credit, but are regularly caused by the firm s total debt and the investment program. 356 sbr 52 (4/2000)

14 Bank Lending Performance bank s return from lending to the respective firm. We note that our classification procedure does not rely on the firm rating. When we apply these criteria to our sample, we find that problems occur in 27 percent of our observations, (coded 1 ) and 73 percent of our observations are without problems (coded 0 ). We have multiple failure-per-subject data, since in some cases problems do not end with the termination of the bank s engagement. In these cases, restructuring activities have been successful. Table 5: Hypotheses and Variables concerning the default probability Hypotheses Variable (Sign) hypothesis 1: risk premium R3, R4, R5, R6 (+) (relative to R12) hypothesis 2b: agency and signaling theory COLLAT ( ) COV ( ) hypothesis 3b: adverse signaling theory COLLAT (+) COV (+) hypothesis 4: pure financial contracting theory COLLAT ( ) COV ( ) hypothesis 5b: House bank relationship HB ( ) NUMBANK ( ) We base our set of independent variables on the set of variables for the premiumregressions in section 3, but we make some modifications. We do not include an indicator for firm size, since the risk-reducing effect of firm size is captured by the rating, and bargaining power should not affect the probability of problems. We exclude DURATION in the logit analysis (specification 1) due to endogeneity. The duration models (specifications 2-4) specify the duration effect by assumption. In addition to the COLLAT-variable we include two variables that describe the form of collaterization: MORTGAGE indicates the securization by mortgages and GUA- RANT is coded 1 if guarantee commitments are given by other persons or firms. 5.2 RESULTS Table 6 shows the results of four specifications. In the first specification we estimate a logit model with a lagged dependent variable in which the time lag is one year, i.e., the period between the observed values of the ratings, collateral and so on and the occurence of problems is one year. A simultaneous examination of the dependent and independent variables is not advisable, since the ratings are set if problems become visible. Thus, the causality is inverted. Specifications 2-4 show the results of a duration analysis. We formalize duration models by specifying a probability density function for the problem-free period in the credit engagement. We also include explanatory variables such as collateral to incorporate additional determinants of this probability. sbr 52 (4/2000) 357

15 R. Ewert/G. Schenk/A. Szczesny A coefficient with a positive sign in table 4 indicates a positive effect on the probability of default. For example, a firm with rating of 6 is likely to have problems earlier than a firm with rating of 5. Different assumptions about the effect over time lead to different models. Since we do not know the exact effect of duration, we choose three specifications. The exponential duration (specification 2) says that the probability of the customer leaving the non-problem period is the same no matter how long the relationship is problem-free. The Weibull density (specification 3) accounts for an increasing or decreasing probability. The Cox proportional model (specification 4) needs no special assumption 31. The panel logit analysis requires regularly distributed observations over time. Therefore, we include in the analysis only the last observation per year and per firm. In contrast, the duration models consider all observations per year and per firm. The ratings are highly significant in all specifications. The higher the rating classification, the higher the probability for the occurence of problems. Like the spreads in the previous section, the differences of distress probabilities among the banks are large. As we did in section 4.2, we offer different explanations. Our standardization of the ratings could be responsible for the the differences. Or differences in the sampling process could have caused this result. But the scales and the significance indicate that there are large differences in the existing rating systems, even after standardization. In light of the latest discussion of new standards on capital adequacy of financial institutions, this result emphasizes the oft-stated doubts about using internal ratings as criteria for equity requirements. Many financial experts believe use of these criteria threatens the principle same business, same risk, same rules 32. Furthermore, the collateral variable COLLAT is significantly related to the probability of distress in specification 1. A higher percentage of collateralized credits is associated with a lower probability of problems in the following year. In the duration models, the sign indicates the same effect even if the result is not significant. Our results on securization by mortgage (MORTGAGE) indicates that banks demand this form of securization if the engagement involves more risk. This is not surprising, since mortgages are mostly easier to use and might improve the recovery rate if there is a default. The duration models show clear results concerning covenants (COV). If the contract involves covenants, the probability of problems occuring is less than if there are no covenants. Thus, the results for the monitoring and bonding variables contradict the adverse signaling hypothesis but are consistent with the combined agency-signaling theory. However, when we return to the results of the premium regressions, if the 31 For an applied survival analysis see e.g. Hosmer/Lemeshow (1999). Kiefer (1988) gives a brief overview. 32 The results of a survey among financial experts in Germany to the subject of the new capital adequacy framework (ZEW, April 2000) show that 51 percent of the experts express this opinion. 358 sbr 52 (4/2000)

16 Bank Lending Performance Table 6: Regressions of default probability Specification 1 Specification 2 Specification 3 Specification 4 (Logit) (Exponential) (Weibull) (Cox) R *** * * (0.8450) (0.5697) (0.5693) (0.5727) R *** *** *** *** (0.8962) (0.5176) (0.5174) (0.5193) R *** *** *** *** (0.9416) (0.5142) (0.5140) (0.5183) R *** *** *** *** (1.3630) (0.5165) (0.5162) (0.5194) COLLAT * (0.0050) (0.0015) (0.0015) (0.0016) MORTGAGE *** ** ** ** (0.5566) (0.1518) (0.1523) (0.1659) GUARANT (0.4565) (0.1273) (0.1283) (0.1376) COV ** ** ** (0.4233) (0.1246) (0.1245) (0.1347) HB (0.5239) (0.1345) (0.1348) (0.1461) NUMBANK (0.0545) (0.0149) (0.0152) (0.0165) B *** *** *** *** (1.2298) (0.2605) (0.2619) (0.2819) B *** *** *** *** (1.1693) (0.2922) (0.3029) (0.3219) B (1.2180) (0.3498) (0.3512) (0.3717) B ** * * * (1.1266) (0.2912) (0.2956) (0.3066) B *** *** *** *** (1.3623) (0.2803) (0.2807) (0.3102) CONST *** *** *** (1.6354) (0.5778) (0.6508) Obs Pseudo R Sq Significant at the * 1 percent level / ** 5 percent level / *** 10 percent level. Standard deviations in brackets. combined agency- and signaling hypothesis holds we should observe a negative relation between the IRP and the use of collateral and/or covenants. Since this observation is not supported by the premium regressions, the empirical results of both regressions do not completely corroborate the combined agency- and signaling theory. 33 It is problematic to calculate pseudo R-squareds because of a high percentage of censored data. For this reason the R-squareds seem low. For the calculation and discussion of the Pseudo R- squareds see Hosmer/Lemeshow (1999), p sbr 52 (4/2000) 359

17 R. Ewert/G. Schenk/A. Szczesny One possible interpretation of the sign of COLLAT is that the monitoring and bonding devices are actually useful in reducing debt-related agency problems. Therefore, the incidence of disturbances should decrease the more collateral and/ or covenants are used. This finding is confirmed by our empirical results. At first glance, this argument is consistent with the pure contracting theory, but only because the predictions of that theory are somewhat vague regarding the sign of the coefficients in a cross-section. We could obtain corroboration for our arguments if the premium regressions in section 4 revealed a negative relation between COLLAT and/or COV and the IRP, but this is not the case. When we take all empirical results together, we obtain interpretations that are both consistent and inconsistent with either of the three hypotheses. For the time being, any clearcut statement is impossible. 6 SUMMARY This paper uses a unique data set from credit files of six leading German banks to provide some empirical insights into determinants of bank lending performance in Germany. We use panel techniques and methods for duration analyses to analyze structural relations for interest rate premiums and for (broadly defined) distress probabilities. For the premium regressions we find that the banks rating is significant and positively related to the interest rate premium (as expected). Cross-selling arguments are relevant mainly for the first years of a relationship and lead to a reduction of the interest rate spread. In addition, our results for the interest rate premiums are consistent with hypotheses derived from adverse signaling theories, i.e., higher collateralization is associated with a higher premium. For distress probabilities, the results for the rating variables are structurally consistent with those of the premium regression, i.e., the higher the rating, the higher the empirically observed occurrence of problems. However, the results for collateralization do not corroborate those for the interest rate premiums. More collateral and the existence of covenants are significantly associated with lower distress probabilities. This is consistent with the combined agency and signaling theory, but contradicts the adverse signaling argument that has proven to be useful in explaining the premium results. Taken together, the results of both regressions seem to imply that credit contracts are priced lower where the risks are greater. This constitutes an empirical puzzle that should be further analyzed by future research. We also find that in both regressions, bank dummies are significant. This suggests that there are important idiosyncratic elements in the rating and evaluation systems of the respective banks. Thus, in light of the recent discussion on equity requirements using internal ratings, we might need to harmonize some of the internal rating process if we are to expect the principle same business, same risk, same rules to hold. 360 sbr 52 (4/2000)

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