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Decomposing Short-Term Return Reversal


Zhi Da


University of Notre Dame - Mendoza College of Business

Qianqiu Liu


University of Hawaii at Manoa - Shidler College of Business

Ernst Schaumburg


Federal Reserve Banks - Federal Reserve Bank of New York

August 25, 2011


Abstract:     
The profit to a standard short-term return reversal strategy can be decomposed analytically into four components related to (1) across-industry return momentum; (2) within-industry variation in expected returns; (3) underreaction to within-industry cash flow news; (4) and a residual. Only the residual component, which isolates reaction to recent "non-fundamental" price changes, is significant and positive in the data. A simple short-term return reversal trading strategy designed to capture the residual component generates a highly significant risk-adjusted return three times the size of the standard reversal strategy during our 1982-2009 sampling period. Our decomposition suggests that short-term return reversal is pervasive, much greater than previously documented, and driven by investor sentiment on the short-side and liquidity shocks on the long-side.

Number of Pages in PDF File: 44

Keywords: return reversal, liquidity, cash flows, discount rate

JEL Classification: G12

working papers series


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Date posted: February 12, 2010 ; Last revised: August 30, 2011

Suggested Citation

Da, Zhi, Liu, Qianqiu and Schaumburg, Ernst, Decomposing Short-Term Return Reversal (August 25, 2011). Available at SSRN: http://ssrn.com/abstract=1551025 or http://dx.doi.org/10.2139/ssrn.1551025

Contact Information

Zhi Da (Contact Author)
University of Notre Dame - Mendoza College of Business ( email )
Notre Dame, IN 46556-5646
United States
Qianqiu Liu
University of Hawaii at Manoa - Shidler College of Business ( email )
2404 Maile Way, E602f
Honolulu, HI 96822
United States
808-956-8736 (Phone)
808-956-9887 (Fax)
Ernst Schaumburg
Federal Reserve Banks - Federal Reserve Bank of New York ( email )
33 Liberty Street
New York, NY 10045
United States
Feedback to SSRN (Beta)


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