Bankruptcy Prediction Using Machine Learning Techniques

Shekar Shetty, Mohamed Musa and Xavier Brédart
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Shekar Shetty: College of Business Administration, Lamar University, Beaumont, TX 77705, USA
Mohamed Musa: Department of Mathematics & Natural Science, College of Arts & Sciences, Gulf University for Science & Technology, Mishref 32093, Kuwait
Xavier Brédart: Warocqué School of Business and Economics, University of Mons, 7000 Mons, Belgium

JRFM, 2022, vol. 15, issue 1, 1-10

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.

Keywords: bankruptcy; deep learning; support vector machine; extreme gradient boosting; SMEs (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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