Using SVM with Financial Statement Analysis for Prediction of Stocks

Abstract

At present, there are many technical analyses for prediction in stock market. However, the technical indices are fluctuated with the quantity of stock exchanges. The financial indices are more reliable, nonvolatile and valid compared with the technical indices. In this paper, we propose an original and universal method by using SVM with financial statement analysis for prediction of stocks. We applied the SVM to construct the prediction model and select Gaussian radial basis function (RBF) as the kernel function. The experimental results show our method not only improve the accuracy rate, but also meet the different stockholders' expectations.