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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.