Study on Business Failure Prediction Based on an Optimized Support Vector Machine Model

According to feature of Chinese capital market and samples,the paper designs an integrated scheme including sampling preparation,optimization of models and model comparison for business failure prediction of listed companies.The effects of parameter-adjusting and selection of kernel functions on model performance by simulation experiments is discussed.Then an optimized support vector machine model is build.Empirical results show that kernel function and parameters have effect on the performance of the support vector machine models.It is also suggested that the optimized support vector machine model for business failure prediction outperforms statistical methods and neural network.support vector machine is suitable as business prediction model for small samples of some industries of listed companies.Chinese "special treat" event as cut-off standard of business failure sample may have some effect on classification models.