Summary The objective of this paper is the comparison of various credit‐scoring models (i.e. binomial logistic regression, decision tree, multilayer perceptron neural network, radial basis function, and support vector machine) in evaluating the risk of small and micro enterprises' (SMEs') loan delinquencies based on accounting data and applicants' specific attributes. Exploiting a representative large data set of SMEs' loans granted by a large Greek commercial bank in the expansion period, we track the evolution of SMEs' delinquencies over the recession period August 2010 to July 2012. This time frame encompasses a period of manageable levels of delays (early recession period: August 2011–July 2012) and a period when delays were increased to a very high degree (deep recession period: August 2011–July 2012). Comparison of the employed credit‐scoring models during the early recession period shows that the multilayer perceptron neural network produces the highest predicting capacity, followed by the support vector machine model. As the crisis deepens, the support vector machine model presents the highest predicting accuracy, followed by the decision tree and then the multilayer perceptron model. Generally, the predictive performance of all credit‐scoring models seems to be substantially reduced as the recession escalates. Our paper has important implications for the proper financing of SMEs given their importance for the European economy.

In this paper we study the effect of independent variables in identifying non-performing loans during crisis period, using a binomial logistic regression. We use a unique data of 2591 loans granted by one of the four systemic banks of Greece in 2005. Specifically we study a sample of loans granted to micro and small enterprises in order to cover working capital needs. Νon-performing loans dramatically increased as the recession of Greek economy deepens. Moreover we prove that in general the variables still affect in the same way the creation of non-performing loans during the studied period. Particularly, binomial logistic regression shows a positive correlation between non-performing loans and factors "Adverse", "Age" and "LTT". In contrast, we find a negative correlation between the probability of classifying a loan as non-performing and the independent variables "Collateral", "Own Facilities", "Property", "Residence" and "Years of operation". Finally the predicted performance of the binomial logistic regression reduced as the crisis deepens.

This paper examines the changes in credit provision for the Greek Banking Sector before and during the financial crisis. Also, it investigates the impact of specific loan characteristics in shaping the overall interest rate of new and existing business loans. A data set with monthly accounting data of Greek Banks for the period January 2003 to June 2011 is utilized, thus incorporating the crisis effects. The study findings reveal the beginning of the credit crunch at the third quarter of 2009. As far as new loans are concerned, it was found that large loans are priced less than the small ones. Moreover, the results show the greater and positive contribution of small loans to the derivation of the total lending interest rate. Furthermore, it is found that the bargaining power of large borrowers during the crisis causes a negative impact of large loans on the total interest rate. Finally, it is shown that in the existing loan portfolio, the crisis significantly reduces the effect of short-term loans, while simultaneously intensifies the positive effect of medium and long-term loans. The findings have important managerial implications for bank managers and policymakers.

In this paper, we analyzed data from publicly listed firms from selected countries of the eurozone between 2008 and 2016, a period of high volatility in the global economy and high uncertainty in financial markets. Economic indicators, such as sales volume, debt accumulation, internationalization, and innovative activity, were combined with firm size and ownership structure to examine their impact on economic performance. We first performed a panel data analysis to associate the economic and non-economic company-specific characteristics with performance and growth, while also examining the possible differences between countries. In the second part of the paper, we focused on ownership structure, an issue of great importance in the literature on business and economics. Through structural equation modeling (SEM), we attempted to shape the profiles of closely held companies, and their impact on performance. Our findings confirmed that significant differences exist not only between firms but also between countries relative to performance indicators; however, a common trend was found relative to debt, size, and ownership. Our findings also revealed a more conservative approach of closely held companies relative to long-term commitment and risky investment projects, compared to their non-closely held counterparts.

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