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Cross-Sectional Stock Return Predictability in China


Nusret Cakici


Fordham University - Graduate School of Business

Kalok Chan


Hong Kong University of Science & Technology (HKUST) - Department of Finance

Kudret Topyan


Manhattan College - Department of Economics and Finance

November 10, 2011


Abstract:     
Cross-sectional stock return predictability has always been an intriguing issue for the researchers as it relates to a number of resilient puzzles in finance. This paper provides a comprehensive analysis on the stock return predictability in China form January 1994 to March 2011 by employing both portfolio method and cross-sectional regressions. We find strong predictive power of size, price, book-to-market ratio, cash-flow-to-price ratio, and earnings-to-price ratio. The total as well as idiosyncratic volatility are also consistent stock return predictors in China. The results exist for stocks listed in Shanghai Stock Exchange as well as Shenzhen Stock Exchange. Unlike evidence for the other markets (e.g. U.S), the momentum fails to qualify as a useful predictor in the portfolio method. It is only when used with other predictors that it exhibits predictive power for the Chinese stocks. Overall, the variables related to cheapness of stocks such as book-to-market ratio and cash-flow-to-price ratio demonstrate reliable forecast power, but earnings-to-price ratio is less reliable.

Number of Pages in PDF File: 42

Keywords: Chinese stock returns, Stock return predictors, Momentum, Stock Cheapness

JEL Classification: G10, G11,G12

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Date posted: April 11, 2012  

Suggested Citation

Cakici, Nusret, Chan, Kalok and Topyan, Kudret, Cross-Sectional Stock Return Predictability in China (November 10, 2011). Available at SSRN: http://ssrn.com/abstract=2038497 or http://dx.doi.org/10.2139/ssrn.2038497

Contact Information

Nusret Cakici
Fordham University - Graduate School of Business ( email )
Kalok Chan
Hong Kong University of Science & Technology (HKUST) - Department of Finance ( email )
Clear Water Bay, Kowloon
Hong Kong
852 2358-7680 (Phone)
852 2358-1749 (Fax)
Kudret Topyan (Contact Author)
Manhattan College - Department of Economics and Finance ( email )
Riverdale, NY 10471
United States
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