摘要
首先采用文本挖掘技术、支持向量回归(support vector regression,SVR)方法将财经新闻内容量化为股市波动的一个影响因子,然后采用计量经济学中多元回归分析方法系统地分析了互联网财经新闻信息对中国股市的影响。主要研究了互联网财经新闻对中国股市的影响强度和影响时长,以及对不同规模的公司影响是否相同等一系列问题。研究发现新闻发布对深市股票的影响力度和持续时间均强于沪市股票;规模较小的公司的股票收益受新闻的影响较大。从而推断出难以量化的互联网财经新闻所包含的信息会在一定的时间内反映在股价中,能够对市场产生冲击。
Text mining and support vector regression techniques were adopted to quantify the impact of financial news on Chinese stock market. Then the multiple regression of econometrics was used to analyze how the online financial news affected stock market returns systematically. It focused on the impact intensity and time of online news on the stock market, and its impact on listed firms with different scales. Experimental results show that firms with Shenzhen Stock Exchange are more affected by the Internet-based financial news than those of Shanghai Stock Exchange and firms with smaller size tend to have a stronger impact on the movements of Chinese stock market. Our findings include that Internet-based financial news contains hard-to-quantify information of firms, which investors incorporate into stock prices timely.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2012年第7期70-75,80,共7页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(60803106
61170133)
教育部霍英东高等院校青年教师基础性研究课题项目(121068)
教育部新世纪优秀人才计划项目(NC1T-11-0694)
西南财经大学创新人才培养基金资助项目
关键词
文本挖掘
支持向量回归
新闻
股市
多元回归
text mining
support vector regression
news
stock market
multiple regression
