Foreign Banks in Transition Economies: Small Business Lending and Internal Capital Markets

On the basis of focused interviews with managers of foreign parent banks and their affiliates in Central Europe and the Baltics, we analyse foreign banks’ small business lending and internal capital markets. This allows us to complement the standard empirical literature, which has difficulty in measuring important variables such as lending technologies and capital allocation systems. We find that the acquisition of local banks by foreign banks has not led to a persistent bias in these banks’ credit supply towards large multinational corporations. Instead, increased competition and the improvement of subsidiaries’ lending technologies have led foreign banks to gradually expand into the SME and retail markets. Second, we show that local bank affiliates are strongly influenced by the capital allocation and credit steering mechanisms of the parent bank. The credit growth of subsidiaries therefore potentially depends on the financial health of the foreign based parent bank.foreign banks, transition economies, small business lending, internal capital markets

Support Vector Machines (SVM) as a Technique for Solvency Analysis

This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and insolvent. The advantages and disadvantages of the method are discussed. The comparison of the SVM with more traditional approaches such as logistic regression (Logit) and discriminant analysis (DA) is made on the Deutsche Bundesbank data of annual income statements and balance sheets of German companies. The out-of-sample accuracy tests confirm that the SVM outperforms both DA and Logit on bootstrapped samples.Company rating, bankruptcy analysis, support vector machines

Developing retail performance measurement and financial distress prediction systems by using credit scoring techniques

The current research develops a theoretical framework based on the ResourceAdvantage Theory of Competition (Hunt, 2000) for the selection of appropriate variables. Using a review of the literature as well as to interviews and a survey, 170 potential retail performance variables were identified as possible for inclusion in the model. To produce a relative simple model with the aim of avoiding over-fitting, a limited number of key variables or principal components were selected to predict default. Five credit-scoring techniques: Naive Bayes, Logistic Regression, Recursive Partitioning, Artificial Neural Network, and Sequential Minimal Optimization (SMO) were employed on a sample of 195 healthy and 51 distressed businesses from the USA market over five time periods: 1994-1998, 1995-1999, 1996-2000, 1997-2001 and 1998-2002.Analyses provide sufficient evidence that the five credit scoring methodologies have sound classification ability in the year before financial distress. Moreover, they still remained sound even five years prior to financial distress. However, it is difficult to conclude which modelling technique has the highest classification ability uniformly, since model performance varied in terms of different time scales. The analysis also showed that external environment influences do impact on default assessment for all five credit-scoring techniques, but these influences are weak. These findings indicate that the developed models are theoretically sound. There is however a need to compare their performance to other approaches.To explore the issue of the model's performance two approaches are taken. First, rankings from the study were compared with those from a standard rating system—in this case the well-established Moody's Credit Rating. It is assumed that the higher the degree of similarity between the two sets of rankings, the greater the credibility of the prediction model. The results indicated that the logistic regression model and the SMO model were most comparable with Moody's. Secondly, the model's performance was assessed by applying it to different geographical areas. The original USA model was therefore applied to a new US data set as well as the European and Japanese markets. Results indicated that all market models displayed similar discriminating ability one year prior to financial distress. However, the USA model performed relatively better than European and Japanese models five years before financial distress. This implied that a financial distress model has potentially better prediction ability when based on a single market.Following this result it was decided to explore the performance of a generic global model, since model construction is time-consuming and costly. A composite model was constructed by combining data from USA, European and Japanese markets. This composite model had sound prediction performance, even up to five years before financial distress, as the accuracy rate was above 85.15% and AUROC value was above 0.7202. Comparing with the original USA model, the composite model has similar prediction performance in terms of the accuracy rate. However, the composite model presented a worse prediction utility based on the AUROC value. A future research direction might be to include more world retailing markets in order to ensure the model's prediction utility and practical applicability

Foreign banks in transition countries. To whom do they lend and how are they financed?

We use focused interviews with managers of foreign parent banks and their affiliates in Central Europe and the Baltic States to analyse the small-business lending and internal capital markets of multinational financial institutions. Our approach allows us to complement the standard empirical literature, which has difficulty in analysing important issues such as lending technologies and capital allocation. We find that the acquisition of local banks by foreign banks has not led to a persistent bias in these banks’ credit supply towards large multinational corporations. Instead, increased competition and the improvement of subsidiaries’ lending technologies have led foreign banks to gradually expand into the SME and retail markets. Second, it is demonstrated that local bank affiliates are strongly influenced by the capital allocation and credit steering mechanisms of the parent bank.foreign banks, transition economies, small-business lending, internal capital markets

Banks, local credit markets and credit supply

The volume collects the papers presented at the Conference on "Banks, Local Credit Markets and Credit Supply" held in Milan, on 24 March 2010. The papers presented at the two sessions of the Conference analyse how banks' lending activities are organized and how this affects the supply of credit to small and medium-sized enterprises (SMEs). The first session focuses on new lending technologies and banking organization. The second session studies how these organizational variables affect the lending activity to SMEs. The papers draw on the results of a sample survey of more than 300 Italian banks conducted by the Bank of Italy in 2007.banking organization, credit scoring, relationship lending, soft information

Credit Scoring for Vietnam’s Retail Banking Market: Implementation and Implications for Transactional versus Relationship Lending

As banking markets in developing countries are maturing, banks face competition not only from other domestic banks but also from sophisticated foreign banks. Combined with a dramatic growth of consumer credit and increased regulatory attention to risk management, the development of a well-functioning credit assessment framework is essential. As part of such a framework, we propose a credit scoring model for Vietnamese retail loans. First, we show how to identify those borrower characteristics that should be part of a credit scoring model. Second, we illustrate how such a model can be calibrated to achieve the strategic objectives of the bank. Finally, we assess the use of credit scoring models in the context of transactional versus relationship lending.financial economics and financial management ;

The information revolution and small business lending: the missing evidence

This paper provides empirical confirmation for Petersen and Rajan's (2002) widely accepted conjecture that information technology was the primary driver of the observed increase in small business borrower-lender distances in the United States in recent years. Using a different data source for small business loans, we show that annual increases in borrower-lender distances were slow and steady prior to 1993 (the end point in Petersen and Rajan's data) but accelerated rapidly after that. Importantly, we are able to assign at least half of this acceleration to the adoption of credit scoring technologies by the lending banks. Our tests also reveal strong statistical associations between lending distances and borrower characteristics, lender characteristics, market conditions, regulatory constraints, moral hazard incentives, and principal-agent incentives.