Application of TWSVR Models in Stock Price Forecast

Abstract

Stock price forecasting is a challenging task. Stock prices are predicted by Twin Support Vector Regression (TWSVR) with two different kernel functions in this paper. The two kernel functions are linear kernel function and polynomial kernel function. The parameters of TWSVR models were selected by genetic algorithm (GA). With the optimized parameters, these models are used to predict the closing prices of the stock in the next day. The predicted results are compared with those obtained by traditional SVR models. The results shown that the TWSVR model with polynomial kernel function has higher accuracy than twin support vector regression with linear kernel. The time consumed by TWSVR is less than that of traditional SVR in prediction.