Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam

Kim Long Tran; Hoang Anh Le; Thanh Hien Nguyen; Duc Trung Nguyen

doi:10.3390/data7110160

journal article Open Access Nov 14, 2022

Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam

Kim Long Tran Hoang Anh Le

Thanh Hien Nguyen Duc Trung Nguyen

Data Vol. 7 No. 11 pp. 160 · MDPI AG

View at Publisher Save 10.3390/data7110160

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.

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Details

Published: Nov 14, 2022
Vol/Issue: 7(11)
Pages: 160
License: View

Authors

K

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

H

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

T

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

D

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

Funding

The Youth Incubator for Science and Technology Programe, managed by Youth Development Science and Technology Center - Ho Chi Minh Communist Youth Union and Department of Science and Technology of Ho Chi Minh City Award: 14/2021/ HĐ-KHCNT-VU

Cite This Article

Kim Long Tran, Hoang Anh Le, Thanh Hien Nguyen, et al. (2022). Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam. Data, 7(11), 160. https://doi.org/10.3390/data7110160

Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam

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