By Topic

Analysis of outliers and public information arrivals using wavelet transform modulus maximum

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xiao-Di Liu ; Fac. of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China ; Wen-Gang Che ; Kai Chi ; Qing-Jiang Zhao

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 analyzing 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.

Published in:

Information and Financial Engineering (ICIFE), 2010 2nd IEEE International Conference on

Date of Conference:

17-19 Sept. 2010