2011/12/01 by Shangkun Deng, Takashi Mitsubuchi, Kei Shioda +2 · 2 citations
Decision Sciences · Economics, Econometrics and Finance · Computer Science · #Stock Market Forecasting Methods #Complex Systems and Time Series Analysis #Time Series Analysis and Forecasting
paper · doi:10.1109/dasc.2011.138
openalex publication_date 2011/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
This paper proposes a stock price prediction model, which extracts features from time series data and social networks for prediction of stock prices and evaluates its performance. In this research, we use the features such as numerical dynamics (frequency) of news and comments, overall sentiment analysis of news and comments, as well as technical analysis of historic price and volume. 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 outperforms other baseline methods in terms of magnitude prediction measures such as RMSE, MAE and MAPE for three famous Japan companies' stocks in US stock market. The results indicate that features other than mining from stock prices themselves improved the performance.