Stock volatility prediction using multi-kernel learning based extreme learning machine

Abstract

Stock price volatility prediction is regarded as one of the most attractive and meaningful research issues in financial market. Some existing researches have pointed out that both the prediction accuracy and the prediction speed are the most important facts in the process of stock prediction. In this paper, we focus on the problem of how to design a methodology which can improve prediction accuracy as well speed up prediction process, and propose a multi-kernel learning based extreme learning machine (MKL-ELM) model to enhance the prediction performance. ELM is a fast learning model and has been successfully applied in many research fields. Based on ELM, this MKL-ELM has the benefits of both multiple kernel learning and ELM, which can well balanced the requirements of both prediction accuracy and prediction speed. To validate the performance of the proposed MKL-ELM, we take experiments on HKEx 2001 stock market datasets. The market historical price and the market news are implemented in our MKL-ELM. We Compare our proposed MKL-ELM with Back-Propagation Neural Network(BP-NN), Support Vector Machine(SVM), Basic ELM and K-ELM. Experimental results show that, 1) MKL-ELM, K-ELM and SVM get higher prediction accuracy than BP-NN and B-ELM; 2) Both MKL-ELM and K-ELM can achieve faster prediction speed than SVM in most cases; 3) MKL-ELM has higher prediction accuracy in some cases than K-ELM and SVM.