The number of filings for bankruptcy procedures has exploded since 2007 and governance has been pointed out as one of the causes. We took a dataset of 312 US firms and asked the following research question: does the board of directors configuration have an impact on financial distress? We used a matched-pair sample of US quoted firms with half of the sample filing for Chapter 7 (liquidation) or 11 (reorganization) of the United States Bankruptcy Code and conducted logit regression analysis. We found that some board size was significantly different for firms that opted for legal protection from those that did not. This study uses corporate governance perspective to analyse the configuration of the board and its impact on a the decision of a company to resort to a bankruptcy protection law. By demonstrating that corporate governance matters in terms of financial distress, this study offers guidance to shareholders and financial institutions.

Purpose-The aim of this paper is to develop a bankruptcy prediction model for the Belgian smalland medium-sized enterprises (SMEs) through the building of a logit model that includes a selection of financial ratios. Design/methodology/approach-Using a sample of 7,152 Belgian SMEs among which 3,576 were declared bankrupt between 2002 and 2012, the model, which includes control variables such as firm size and age, aims to test the predictive power of ratios reflecting the financial structure, the profitability, the solvency and the liquidity of firms. Findings-The results report a satisfactory prediction accuracy and show that ratios as profitability and liquidity are excellent predictors of bankruptcy for Belgian SMEs. Research limitations/implications-Although the results seem to be conclusive, it could be noted that the healthy sample was not paired with the bankrupt sample. Other studies show that the use of paired samples makes it possible to increase the already good prediction rate. Also, further research could focus on intra-sectorial analysis. Practical implications-Beside its contribution to the academic literature on bankruptcy prediction of Belgian SMEs, this study may be of interest for investors or managers to help them to anticipate bankruptcy risks. It can also be useful for banks and other credit institutions in the assessment of credit risk of firms. Thanks to such models, they could better identify firms with a higher risk of failure in their lending decisions. Social implications-Given the increasing number of SMEs in Belgium, their significant role in the economy, the specific characteristics of the country in terms of political decision making, the institutional differences between regions and the current uncertain economic circumstances, bankruptcy prediction seems to be a necessity for the country. Originality/value-The originality of this paper lies in the fact that Belgian SMEs have been studied. This study may also be of interest to investors or managers because it may help them highlight accounting measures they should closely follow up to avoid bankruptcy.

Belgium has faced an important number of corporate bankruptcies during the last decade. The aim of this paper is to develop a model that predicts bankruptcy using three financial ratios that are simple and easily available, even for small businesses. We used a sample of 3,728 Belgian Small and Medium Enterprises (SME's) including 1,864 businesses having been declared bankrupt between 2002 and 2012 and conducted a neural network analysis. Our results indicate that the neural network methodology based on three financial ratios that are simple and easily available as explanatory variables shows a good classification rate of more or less 80 percent. Results of this study may be of interest for financial institutions and for academics.

United States has faced a growing number of corporate bankruptcies since the subprimes crisis. This paper aims to develop an econometric forecasting model constructed from three simple and easily available financial ratios. We used a matched-pair sample of US quoted firms with half of the sample filing for chapter 11 (reorganization procedure) of the United States Bankruptcy Code for the period 2000-2012 and conducted logit regression analysis. We found that this model using three simple, few correlated and easily available financial ratios as explanatory variables shows a prediction accuracy of more than 80 percent. Besides the academic contribution to the research relative to the bankruptcy prediction, the empirical results of this study may be useful for practitioners, particularly for financial institutions interested in the probability of default of their partners.

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