Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction

摘要

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 factors 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 as speed up prediction process, and propose a new prediction model which employs mutual information- based sentimental analysis methodology with extreme learning machine to enhance the prediction performance. The two major contributions of our work are (1) as the words in the news documents are not absolutely negative or positive, and the lengths of the financial news documents are various; here, we propose a new sentimental analysis methodology based on mutual information to improve the efficiency of feature selection, which is different from the traditional sentimental analysis algorithm, and a new weighting scheme is also used in the feature weighting process; (2) since ELM is a fast learning model and has been successfully applied in many research fields, we propose a prediction model which combined mutual information-based sentimental analysis with kernel-based ELM named as MISA-K-ELM. This model has the benefits of both statistical sentimental analysis and ELM, which can well balance the requirements of both prediction accuracy and prediction speed. We take experiments on HKEx 2001 stock market datasets to validate the performance of the proposed MISA-K-ELM. The market historical price and the market news are implemented in our MISA-K-ELM. To test the efficiency of MISA, we first compare the prediction accuracy of ELM model using MISA with ELM model using traditional sentimental analysis. Then, we compare our proposed MISA-K-ELM with existing state-of-the-art learning algorithms, such as Back-Propagation Neural Network (BP-NN), and Support Vector Machine (