Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction
Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction
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DOI：
10.1109/dasc.2011.138
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Shangkun Deng;Takashi Mitsubuchi;Kei Shioda;Tatsuro Shimada;A. Sakurai
中科院分区：
文献类型：
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作者：
Shangkun Deng;Takashi Mitsubuchi;Kei Shioda;Tatsuro Shimada;A. Sakurai
本文提出了一种股票价格预测模型，该模型从时间序列数据和社交网络中提取特征来预测股票价格并评估其性能。在这项研究中，我们使用了新闻和评论的数值动态（频率）、新闻和评论的整体情绪分析以及历史价格和交易量的技术分析等特征。我们将股票价格变动建模为这些输入特征的函数，并将其作为多核学习回归框架中的回归问题来解决。实验结果表明，我们提出的方法在对美国股市上三个著名日本公司股票的 RMSE、MAE 和 MAPE 等幅度预测指标方面优于其他基线方法。结果表明，除了从股票价格本身进行挖掘之外，其他功能也提高了性能。
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.