Cross-validation research based on RBF-SVR model for stock index prediction

Abstract

The ups and downs of stock indexes are one of the most concerned issues for investors in the stock market. To improve the accuracy of stock index prediction, this paper compares traditional K-Fold Cross-Validation (KCV) and three cross-validation methods in the Radial Basis Function Support Vector Regression (RBF-SVR) model. They are named as Abandon Tail Cross-Validation (ATCV), Sequential Division Cross-Validation (SCV) and Gap Sequential Division Cross-Validation (GSCV). It is found that KCV has very limited validation ability for stock indexes time series data with no certain relevance. However, SCV and GSCV with small gap perform better, with high accuracy about 88% and small error about 2%. This research shows that the establishment of time series forecasting models for stock indexes needs to pay more attention to cross-validation methods, which cannot randomly dividing training set and test set. It is strongly recommended to use SCV and GSCV instead of KCV. In addition, the choice of the penalty parameter C and the radial basis kernel function parameter γ largely determines the accuracy and reliability of RBF-SVR stock index prediction model.

Keywords

• support vector regression
• parameter optimization
• cross-validation
• stock index prediction
• time series