Early Warning of Financial Distress in Listed Companies: A Machine Learning Approach

Abstract

Financial distress prediction is critical for maintaining market stability and protecting investor interests. Traditional statistical models often fall short in capturing complex, non-linear patterns in financial data. This study aims to develop an early warning system for financial distress in listed companies by leveraging machine learning algorithms. Using a dataset of financial ratios and market performance indicators from publicly traded firms over a ten-year period, we applied several classification techniques, including logistic regression, support vector machines, and gradient boosting. The gradient boosting model demonstrated superior predictive accuracy, achieving an F1-score of 0.92, significantly outperforming traditional models. Key predictors identified include cash flow volatility, debt-to-equity ratio, and operating margin trends. The findings underscore the potential of machine learning in enhancing the timeliness and reliability of financial distress alerts, offering valuable insights for regulators, investors, and corporate management to take proactive measures.