Abstract
In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector machine (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small and Medium Enterprises (SME’s) during the period 2002–2012. Using the above-mentioned machine learning techniques, we predict bankruptcies with a global accuracy of 82–83% using only three easily obtainable financial ratios: the return on assets, the current ratio, and the solvency ratio. While the prediction accuracy is similar to several previous models in the literature, our model is very simple to implement and represents an accurate and user-friendly tool to discriminate between bankrupt and non-bankrupt firms.
Highlights
Bankruptcy detection is a major topic in finance
Altman employed multiple discriminant analysis (MDA) techniques to determine the probability of bankruptcy on a sample of firms
Different models provide roughly the same level of global accuracy of about 85% for correctly predicting whether a specific firm is bankrupt
Summary
Bankruptcy detection is a major topic in finance. For obvious reasons, many actors such as shareholders, managers or banks are interested in the likelihood of bankruptcy of firms. Many studies have been carried out on the topic of bankruptcy prediction. In the late 1960s, Beaver (1966) introduced a univariate analysis, providing the first statistical justification for the ability of financial ratios to account for default. Altman (1968) developed the Z-score model by using five financial ratios to predict the bankruptcy of U.S firms. Altman employed multiple discriminant analysis (MDA) techniques to determine the probability of bankruptcy on a sample of firms
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