Hybrid Model based on unification of Technical Analysis and Sentiment Analysis for Stock Price Prediction

Abstract

Stock price forecasting phenomenon has been majorly made on the basis of quantitative information. Over the time, with the advent of technology, stock forecasting used technical analysis to get more accurate predictions. Until recently, studies have demonstrated that sentiment information hidden in corporate reports can be effectively incorporated to predict short-run stock price returns. Soft computing methods, like neural networks, fuzzy models and support vector regression, have shown great results in the forecasting of stock price due to their ability to model complex non-linear systems.

In this paper we propose a hybrid method for stock price predication, which is combinational feature from technical analysis and sentiment analysis (SA). The features of sentiment analysis are based on a Point wise Mutual Information (PMI) and we apply  neural network and ε-support vector regression models to predict the yearly change in the stock price.