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Follow the smart money: Factor forecasting in China

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  • Chen, Qinhua
  • Chi, Yeguang
  • Qiao, Xiao

Abstract

We present novel evidence of factor timing in the Chinese stock market. Actively managed Chinese stock mutual funds have larger exposure to the size factor when it performs well and smaller exposure when it performs poorly. By constructing a proxy for the size preference of active stock funds, we can forecast size factor returns in the subsequent periods. A one-standard-deviation increase in the size factor loading of active stock funds is associated with an increase in the size factor return of 1.2% in the next month and 10.8% in the next year. The result is not driven by industry rotation, price impact of mutual funds, or factor momentum. Actively managed stock mutual funds do not appear to time value or momentum factors.

Suggested Citation

  • Chen, Qinhua & Chi, Yeguang & Qiao, Xiao, 2020. "Follow the smart money: Factor forecasting in China," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:pacfin:v:62:y:2020:i:c:s0927538x1930753x
    DOI: 10.1016/j.pacfin.2020.101368
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    References listed on IDEAS

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    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. Edelen, Roger M. & Ince, Ozgur S. & Kadlec, Gregory B., 2016. "Institutional investors and stock return anomalies," Journal of Financial Economics, Elsevier, vol. 119(3), pages 472-488.
    4. Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
    5. Hodrick, Robert J, 1992. "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," The Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 357-386.
    6. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    7. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    8. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    9. Akbas, Ferhat & Armstrong, Will J. & Sorescu, Sorin & Subrahmanyam, Avanidhar, 2015. "Smart money, dumb money, and capital market anomalies," Journal of Financial Economics, Elsevier, vol. 118(2), pages 355-382.
    10. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    11. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    12. Chen, Qinhua & Chi, Yeguang, 2018. "Smart beta, smart money," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 19-38.
    13. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    Full references (including those not matched with items on IDEAS)

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