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Turning over Turnover

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  • K. J. Martijn Cremers
  • Jianping Mei

Abstract

This article applies the methodology of Bai and Ng (2002, 2004) for decomposing panel data into systematic and idiosyncratic components to both stock returns and turnover panels. This approach works well for both returns and turnover, despite the presence of severe heteroscedasticity and nonstationarity of individual stocks' turnover. We test the mutual fund separation model of Lo and Wang (2000). Trading due to systematic risk in returns can account for 66% of systematic turnover. Thus, portfolio rebalancing due to systematic risk is a very important motive for stock trading. Finally, several common turnover measures may understate the impact of stock trading. , Oxford University Press.

Suggested Citation

  • K. J. Martijn Cremers & Jianping Mei, 2007. "Turning over Turnover," The Review of Financial Studies, Society for Financial Studies, vol. 20(6), pages 1749-1782, November.
    • Handle: RePEc:oup:rfinst:v:20:y:2007:i:6:p:1749-1782
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    Cited by:

    1. Azi Ben‐Rephael & Bruce I. Carlin & Zhi Da & Ryan D. Israelsen, 2021. "Information Consumption and Asset Pricing," Journal of Finance, American Finance Association, vol. 76(1), pages 357-394, February.
    2. Andrade, Sandro C. & Bian, Jiangze & Burch, Timothy R., 2013. "Analyst Coverage, Information, and Bubbles," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1573-1605, October.
    3. Hameed, Allaudeen & Xie, Jing, 2019. "Preference for dividends and return comovement," Journal of Financial Economics, Elsevier, vol. 132(1), pages 103-125.
    4. Ľuboš Pástor & Robert F. Stambaugh & Lucian A. Taylor, 2017. "Do Funds Make More When They Trade More?," Journal of Finance, American Finance Association, vol. 72(4), pages 1483-1528, August.
    5. Min Hwang & John Quigley, 2010. "Housing Price Dynamics in Time and Space: Predictability, Liquidity and Investor Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 41(1), pages 3-23, July.
    6. Aragon, George O. & Dieckmann, Stephan, 2011. "Stock market trading activity and returns around milestones," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 570-584, September.
    7. Edelen, Roger M. & Kadlec, Gregory B., 2012. "Delegated trading and the speed of adjustment in security prices," Journal of Financial Economics, Elsevier, vol. 103(2), pages 294-307.
    8. Perez, M. Fabricio & Shkilko, Andriy & Sokolov, Konstantin, 2015. "Factor models for binary financial data," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 177-188.
    9. Marcos Vizcaíno-González & Susana Iglesias-Antelo & Noelia Romero-Castro, 2019. "Assessing Sustainability-Related Systematic Reputational Risk through Voting Results in Corporate Meetings: A Cross-Industry Analysis," Sustainability, MDPI, vol. 11(5), pages 1-11, March.
    10. Rossi, Francesco, 2012. "UK cross-sectional equity data: The case for robust investability filters," European Economic Letters, European Economics Letters Group, vol. 1(1), pages 6-13.
    11. Dion Bongaerts & Richard Roll & Dominik Rösch & Mathijs van Dijk & Darya Yuferova, 2022. "How Do Shocks Arise and Spread Across Stock Markets? A Microstructure Perspective," Management Science, INFORMS, vol. 68(4), pages 3071-3089, April.
    12. Rossi, Francesco, 2011. "U.K. cross-sectional equity data: do not trust the dataset! The case for robust investability filters," MPRA Paper 38303, University Library of Munich, Germany, revised Nov 2011.
    13. Pankaj K. Jain & Mohamed Mekhaimer & Sandra Mortal, 2020. "Commonality in liquidity and multilateral trading facilities," The Financial Review, Eastern Finance Association, vol. 55(3), pages 481-502, August.
    14. Rossi, Francesco, 2011. "Risk components in UK cross-sectional equities: evidence of regimes and overstated parametric estimates," MPRA Paper 38682, University Library of Munich, Germany, revised 31 Mar 2012.

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