Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258759 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 1 [Article No.:] 35 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-10
Publisher: 
MDPI, Basel
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.
Subjects: 
bankruptcy
deep learning
support vector machine
extreme gradient boosting
SMEs
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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