Stock index prediction: A comparison of MARS, BPN and SVR in an emerging market

Abstract

Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index prediction. In this study, we compare the forecasting performance of MARS, backpropagation neural network (BPN), support vector regression (SVR), and multiple linear regression (MLR) models in Shanghai B-Share stock index. Experimental results show that MARS outperforms BPN, SVR and MLR in terms of prediction error and prediction accuracy.