Predicting Financial Distress of Chinese Listed Firms Using Machine Learning Models: A Comparative Study
Abstract
Financial distress prediction remains a critical concern for investors, regulators, and stakeholders in China's rapidly evolving capital markets. This study presents a comprehensive comparative analysis of machine learning models for predicting financial distress among Chinese listed firms. Using a dataset of 2,847 Chinese A-share listed companies from 2015 to 2023, this study evaluates the predictive performance of Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting Machine (GBM). The results demonstrate that ensemble methods, particularly Gradient Boosting Machine, achieve superior predictive accuracy (94.2%) compared to traditional statistical approaches (Logistic Regression: 86.7%). Feature importance analysis reveals that profitability ratios, leverage metrics, and liquidity indicators constitute the most significant predictors of financial distress. The findings contribute to both academic literature and practical applications in credit risk assessment within the Chinese regulatory context.
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References
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Dang, T. L., & Vu, V. T. T. (2019). Financial distress prediction for Vietnamese listed firms using machine learning techniques. Journal of Asian Finance, Economics and Business, 6(2), 133–142. https://doi.org/10.13106/jafeb.2019.vol6.no2.133
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1–2), 3–34. https://doi.org/10.1111/jbfa.12218
Li, H., & Sun, J. (2012). Forecasting business failure in China using case-based reasoning with hybrid genetic algorithms. Journal of Forecasting, 31(8), 707–721. https://doi.org/10.1002/for.1237
Liang, D., Lu, C. C., Tsai, C. F., & Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561–572. https://doi.org/10.1016/j.ejor.2016.01.012
Lin, H. W. W., Lo, H. C., & Wu, R. S. (2016). Modeling default prediction with earnings management: Empirical evidence from Chinese listed companies. Pacific-Basin Finance Journal, 40, 306–322. https://doi.org/10.1016/j.pacfin.2016.10.005
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101–124. https://doi.org/10.1086/209665
Tinoco, M. H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419. https://doi.org/10.1016/j.irfa.2013.02.013
Zhang, Y., & Wu, D. (2011). Financial distress prediction for Chinese listed companies. International Journal of Information Technology & Decision Making, 10(3), 553–574. https://doi.org/10.1142/S0219622011004464
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