Comparison of Model Performance in Forewarning Financial Crisis of Publicly Traded Companies: Different Algorithmic Models

Abstract

The financial crisis can have adverse effects on a company's development and even on the entire industry. Early warning and prevention of such crises through specific methods holds significant importance. This paper focuses on the prewarning of financial crises in publicly traded companies. Samples were selected from the CSMAR database to analyze data from the T-2 year and T-3 year. Thirty indicators were screened from perspectives such as levels of debt repayment. The performance of six different algorithmic models, including support vector machine, XGBoost, long short-term memory (LSTM), gate recurrent unit (GRU), bi-directional LSTM, and bi-directional GRU (BiGRU), were compared using the indicators screened by significance tests. The results indicated that the T-2-year data outperformed the T-3-year data in early warning. Among the various algorithmic models, BiGRU exhibited the best early warning performance, with an accuracy of 0.934, a true positive rate of 0.975, a true negative rate of 0.82, and an area under the curve of 0.986. Furthermore, the inclusion of non-financial indicators effectively enhanced model performance. These findings highlight the advantages of utilizing BiGRU for early detection of financial crisis, offering practical applications.