基于社会媒体的股票行为预测

通过社会媒体信息预测股票行为已经成为近年来金融和知识管理等领域的研究热点。考虑到社会媒体参与人员和讨论话题的多样性,传统的基于整体层面分析社会媒体信息来预测股票行为的方法过于粗糙。本文根据社会媒体信息在写作风格和内容特征上的不同,利用文本特征提取技术、主成分分析法、EM聚类技术等分析参与社会媒体的干系人和他们关注的话题。进一步,我们针对每类干系人和话题,从信息活动强度和情感倾向两个方面提取四个社会媒体变量构建股票行为的回归预测模型,用以分析各干系人和话题在社会媒体上的活动状况对公司股票行为的影响。最后,本文以雅虎金融论坛的Bank of America板块为实验平台进行实验研究,验证了所提出方法的有效性和实用性。

Predicting Stock Behaviorvia Social Media

Predicting stock behavior via social media has attracted a great deal of attentions in the finance and knowledge management disciplines. Due to the diversity of social media participants and discussion topics, it is difficult to improve the accuracy of stock behavior prediction by applying traditional methods which based on whole level of social media information. In this paper, text feature extraction technology, principal components analysis and EM clustering are used to identify stakeholders and topics related to a special firm by social media messages' similar writing style and content feature. Furthermore, four types of social media variable are extracted from information activity intensity and sentiment inclinations to build stock behavior regression models for each stakeholder and topic. Finally, Bank of America Company's message board on Yahoo! Finance forum is chosen as our experimental platform. The validity and practicability of our proposed method are tested in experimental result.