Bankruptcy Prediction Using Machine Learning Techniques

Journal of Risk and Financial Management

Shekar Shetty¹  Mohamed Musa²  Xavier Brédart³ 

[1] College of Business Administration, Lamar University, Beaumont, TX 77705, USA;Department of Mathematics & Natural Science, College of Arts & Sciences, Gulf University for Science & Technology, Mishref 32093, Kuwait;Warocqué School of Business and Economics, University of Mons, 7000 Mons, Belgium;

关键词: bankruptcy;    deep learning;    support vector machine;    extreme gradient boosting;    SMEs;   

DOI  :  10.3390/jrfm15010035

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown