Multiple kernel learning on time series data and social networks for stock price prediction

This paper proposes a stock price prediction model, which extracts features from time series data, news, and comments on the news, for prediction of stock price and evaluates its performance. In this research, we do not take account of text contents of news and user comments, but just consider numerical features of news and communication dynamics appeared in comments on the Web as well as historical time series data. We model the stock price movements as a function of these input features and solve it as a regression problem in a Multiple Kernel Learning regression framework. Experimental results show that our proposed method consistently outperforms other baseline methods in terms of magnitude prediction measures such as MAE, MAPE and RMSE for three companies' stocks. They specifically show that the features other than stock prices themselves improved the performance.