A LASSO-based Prediction Model for Financial Distress of the Vietnamese Listed Firms

Abstract

Financial distress is a vexing managerial challenge for businesses worldwide, especially during a turbulent period like the Covid-19 pandemic. Motivated by an increasing number of closed businesses in Vietnam during the recent Covid-19 pandemic, this study is conducted to provide a comprehensive analysis of financial distress for Vietnamese listed firms. Machine learning approaches are employed using the annual data of 492 listed firms from 2012 to 2021. Specifically, we aim to identify the appropriate distress predictors for the Vietnamese listed firms using LASSO, a technique known to be superior compared to other variable selection techniques. Empirical results reveal that there are four key financial distress predictors for the Vietnamese listed firms, namely the ratios of (i) working capital and total assets, (ii) retained earnings and total assets, (iii) earnings before interest and taxes and total assets and (iv) net income and total assets. We also conduct the industry-level analysis and find that Energy sector experienced the highest number of financially distressed firms during Covid-19. In contrast, Communication Services, Health Care, and Utilities had the lowest number of distressed firms. Policy implications have emerged based on these important findings from our analysis.