Analysis of Outliers and Public Information Arrivals Using Wavelet Transform Modulus Maximum

摘要：

The financial data are usually highly noisy and contain outliers,while detecting outliers is important but hard problem.On the other hand,efficient markets hypothesis demonstrates that market prices fully reflect all available information.Furthermore,previous studies suggest that public information arrivals could lead to volatility of stock prices.Therefore,the study of analvzing the relation between outliers and public information has attracted more and more attention.In this paper,the authors employed wavelet transform modulus maximum to analyze the aforementioned relation using daily data from 2007 to 2010 of the Shanghai Stock Exchange Composite Index (SSE Composite Index).The empirical results show that there exists relatively clear correspondence between outliers and public information arrivals.

关键词： outlier financial data detecting public information arrivals wavelet transform modulus maximum

作者: Xiao-Di LIU Wen-Gang CHE Kai CHI Qing-Jiang ZHAO2

作者单位: Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunmi Department of Physics and Technology Kunming University Kunming,P.R.China

会议类型: 国际会议

会议名称: 2010 2nd IEEE International Conference on Information and Financial Engineering(2010年第二届IEEE信息与金融工程国际会议 ICIFE 2010)

会议地点: 重庆

会议语种:英文

页码: 176-179

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）