Prediction of Financial Distress in Vietnam Using Multi-Layer Perceptron Artificial Neural Network

Oanh T. K. Nguyen, Dinh V. Nguyen (), Truong Cong Doan, Anh H. L. Nguyen and Anh T. M. Ta
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Oanh T. K. Nguyen: Vietnam National University, International School
Dinh V. Nguyen: Vietnam National University, International School
Truong Cong Doan: Vietnam National University, International School
Anh H. L. Nguyen: Vietnam National University, International School
Anh T. M. Ta: Vietnam National University, International School

A chapter in Proceedings of the 11th International Conference on Emerging Challenges: Smart Business and Digital Economy 2023 (ICECH 2023), 2023, pp 264-290 from Springer

Abstract: ABSTRACT Research purpose: This study attempts to use artificial neural networks to predict financial distress measured by EBIT lower than interest expenses for two consecutive years of the listed firms in Vietnam, which has no study conducted before. Research motivation: Although research on financial distress prediction has a long history, prediction methods have been updated along with information technology’s advancement to improve the accuracy of the predictive model. This study is designed to enhance the predictive power of the financial distress model for listed firms on the Vietnam Stock Exchange. Research design, approach, and method: The multi-layer perceptron artificial neural network (MLP-ANN) was employed to analyze data collected data from 509 companies with a total of 6617 observations. Financial ratios are collected on the Hanoi Stock Exchange and Ho Chi Minh Stock Exchange for the period 2007–2019 by using the FiinPro Platform. Main findings: The result of the empirical analysis shows that the model can correctly classify up to 93.9% of the company’s financial position into financial distress and financial health. In addition, the average classification result by sector shows that the manufacturing sector has the highest percent correct classification with 94.7%, followed by the service sector with 94.2%, and the trade sector presents the lowest correct classification among the 3 industries with 90.6%. Moreover, the model is suitable and can be applied to make early forecasts to avoid the risks of financial distress in Vietnam. Practical/managerial implications: The model is suitable and can be applied to make early forecasts of financial distress in Vietnam.

Keywords: Financial distress prediction; Multi-layer perceptron artificial neural network (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-348-1_22

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DOI: 10.2991/978-94-6463-348-1_22

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