In this paper, we compare some traditional statistical methods for predicting financial distress to some more "unconventional" methods, such as decision tree classification, neural networks, and evolutionary computation techniques, using data collected from 200 Taiwan Stock Exchange Corporation (TSEC) listed companies. Empirical experiments were conducted using a total of 42 ratios including 33 financial, 8 non-financial and 1 combined macroeconomic index, using principle component analysis (PCA) to extract suitable variables. This paper makes four critical contributions: (1) with nearly 80% fewer financial ratios by the PCA method, the prediction performance is still able to provide highly-accurate forecasts of financial bankruptcy; (2) we show that traditional statistical methods are better able to handle large datasets without sacrificing prediction performance, while intelligent techniques achieve better performance with smaller datasets and would be adversely affected by huge datasets; (3) empirical results show that C5.0 and CART provide the best prediction performance for imminent bankruptcies; and (4) Support Vector Machines (SVMs) with evolutionary computation provide a good balance of high-accuracy short- and long-term performance predictions for healthy and distressed firms. Therefore, the experimental results show that the Particle Swarm Optimization (PSO) integrated with SVM (PSOSVM) approach could be considered for predicting potential financial distress.
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics