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This paper analyzes loan pricing when there is multiple banking and borrower distress. Using a unique data set on SME lending collected from major German banks, we can instrument for effective coordination between lenders, carrying out a panel estimation. The analysis allows to distinguish between rents that accrue due to single bank lending, rents that accrue due to relationship lending, and rents that accrue due to the elimination of competition among multiple lenders. We find the relationship lending to have no discernible impact on loan spreads, while both single lending and coordinated multiple lending significantly increase the spread. Thus, contrary to predictions in the literature, multiple lending does not insure the borrower against hold-up. JEL Classification: D74, G21, G33, G34
Financial theory creates a puzzle. Some authors argue that high-risk entrepreneurs choose debt contracts instead of equity contracts since risky but high returns are of relatively more value for a loan-financed firm. On the contrary, authors who focus explicitly on start-up finance predict that entrepreneurs are the more likely to seek equity-like venture capital contracts, the more risky their projects are. Our paper makes a first step to resolve this puzzle empirically. We present microeconometric evidence on the determinants of debt and equity financing in young and innovative SMEs. We pay special attention to the role of risk for the choice of the financing method. Since risk is not directly observable we use different indicators for financial and project risk. It turns out that our data generally confirms the hypothesis that the probability that a young high-tech firm receives equity financing is an increasing function of the financial risk. With regard to the intrinsic project risk, our results are less conclusive, as some of our indicators of a risky project are found to have a negative effect on the likelihood to be financed by private equity.
In recent years new methods and models have been developed to quantify credit risk on a portfolio basis. CreditMetrics (tm), CreditRisk+, CreditPortfolio (tm) are among the best known and many others are similar to them. At first glance they are quite different in their approaches and methodologies. A comparison of these models especially with regard to their applicability on typical middle market loan portfolios is in the focus of this study. The analysis shows that differences in the results of an application of the models on a certain loan portfolio is mainly due to different approaches in approximating default correlations. That is especially true for typically non-rated medium-sized counterparties. On the other hand distributional assumptions or different solution techniques in the models are more or less compatible.
During the last years the lending business has come under considerable competitive pressure and bank managers often express concern regarding its profitability vis-a-vis other activities. This paper tries to empirically identify factors that are able to explain the financial performance of bank lending activities. The analysis is based on the CFS-data-set that has been collected in 1997 from 200 medium-sized firms. Two regressions are performed: The first is directed towards relationships between the interest rate premiums and various determining factors, the second aims at detecting relationships between those factors and the occurrence of several types of problems during the course of a credit engagement. Furthermore, the results of both regressions are used to test theoretical hypotheses regarding the impact of certain parameters on credit terms and distress probabilities. The findings are somewhat “puzzling“: First, the rating is not as significant as expected. Second, credit contracts seem to be priced lower for situations with greater risks. Finally, the results do not fully support any of three hypotheses that are often advanced to describe the role of collateral and covenants in credit contracts.
We studied information and interaction processes in six lending relationships between a universal bank and medium sized firms. The study is based on the credit files of the respective firms. If no problems occur in these lending relationships, bank monitoring is based mainly on cheap, retrospective and internal data. In case of distress, more expensive, prospective and external information is used. The level of monitoring and the willingness to renegotiate the lending relationship depends on what the lending officers can learn about the future prospects of the firm from the behaviour of the debtors. We identify both signalling and bonding activities. Such learning from past behaviour seems to allow monitoring at low cost, whereas the direct observation of the firm's investment outlook seems to be very costly. Also, too much knowledge about the firm's investments might leave the bank in a very strong bargaining position and distort investment incentives. Therefore, the traditional view of credit assessment as observation of the quality of a borrower's investment programme needs to be reconsidered.
Despite the relevance of credit financing for the profit and risk situation of commercial banks only little empirical evidence on the initial credit decision and monitoring process exists due to the lack of appropriate data on bank debt financing. The present paper provides a systematic overview of a data set generated during the Center for Financial Studies research project on "Credit Management" which was designed to fill this empirical void. The data set contains a broad list of variables taken from the credit files of five major German banks. It is a random sample drawn from all customers which have engaged in some form of borrowing from the banks in question between January 1992 and January 1997 and which meet a number of selection criteria. The sampling design and data collection procedure are discussed in detail. Additionally, the project's research agenda is described and some general descriptive statistics of the firms in our sample are provided.