Forecasting financial condition of Chinese listed companies based on support vector machine

Due to the radical changing and specialty of Chinese capital market, it is challenging to develop a powerful financial distress prediction model. In this paper, we first analyzed the feasibility of Chinese special-treated companies as distressed sample by using statistical methods. Then we developed a prediction model based on support vector machines (SVM) for an unmatched sample of Chinese high-tech manufacture companies. The grid-search technique using 10-fold cross-validation is used to find out the best parameter value of kernel function of SVM. The experiment results show that the proposed SVM model outperforms conventional statistical methods and back-propagation neural network. In general, SVM provides a robust model with high prediction accuracy for forecasting financial distress of Chinese listed companies. It is also suggested that Chinese special-treated event adopted as cut-off line has some effect on the prediction accuracy of the models.