Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam

Kim Long Tran, Hoang Anh Le (), Thanh Hien Nguyen and Duc Trung Nguyen
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Kim Long Tran: Faculty of Banking, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam
Hoang Anh Le: Institute for Research Science and Banking Technology, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam
Thanh Hien Nguyen: Department of Economic Mathematics, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam
Duc Trung Nguyen: Faculty of Banking, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam

Data, 2022, vol. 7, issue 11, 1-12

Abstract: The past decade has witnessed the rapid development of machine learning applied in economics and finance. Recent evidence suggests that machine learning models have produced superior results to traditional statistical models and have become the driving force for dramatic improvement in the financial industry. However, a much-debated question is whether the prediction results from black box machine learning models can be interpreted. In this study, we compared the predictive power of machine learning algorithms and applied SHAP values to interpret the prediction results on the dataset of listed companies in Vietnam from 2010 to 2021. The results showed that the extreme gradient boosting and random forest models outperformed other models. In addition, based on Shapley values, we also found that long-term debts to equity, enterprise value to revenues, account payable to equity, and diluted EPS had greatly influenced the outputs. In terms of practical contributions, the study helps credit rating companies have a new method for predicting the possibility of default of bond issuers in the market. The study also provides an early warning tool for policymakers about the risks of public companies in order to develop measures to protect retail investors against the risk of bond default.

Keywords: explainable AI; financial distress; machine learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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