IDEAS home Printed from https://ideas.repec.org/a/bla/jfinan/v76y2021i5p2249-2305.html
   My bibliography  Save this article

Tracking Retail Investor Activity

Author

Listed:
  • EKKEHART BOEHMER
  • CHARLES M. JONES
  • XIAOYAN ZHANG
  • XINRAN ZHANG

Abstract

We provide an easy method to identify marketable retail purchases and sales using recent, publicly available U.S. equity transactions data. Individual stocks with net buying by retail investors outperform stocks with negative imbalances by approximately 10 bps over the following week. Less than half of the predictive power of marketable retail order imbalance is attributable to order flow persistence, while the rest cannot be explained by contrarian trading (proxy for liquidity provision) or public news sentiment. There is suggestive, but only suggestive, evidence that retail marketable orders might contain firm‐level information that is not yet incorporated into prices.

Suggested Citation

  • Ekkehart Boehmer & Charles M. Jones & Xiaoyan Zhang & Xinran Zhang, 2021. "Tracking Retail Investor Activity," Journal of Finance, American Finance Association, vol. 76(5), pages 2249-2305, October.
    • Handle: RePEc:bla:jfinan:v:76:y:2021:i:5:p:2249-2305
    DOI: 10.1111/jofi.13033
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jofi.13033
    Download Restriction: no
    File URL: https://libkey.io/10.1111/jofi.13033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ron Kaniel & Shuming Liu & Gideon Saar & Sheridan Titman, 2012. "Individual Investor Trading and Return Patterns around Earnings Announcements," Journal of Finance, American Finance Association, vol. 67(2), pages 639-680, April.
    2. Barrot, Jean-Noel & Kaniel, Ron & Sraer, David, 2016. "Are retail traders compensated for providing liquidity?," Journal of Financial Economics, Elsevier, vol. 120(1), pages 146-168.
    3. Hansen, Lars Peter & Hodrick, Robert J, 1980. "Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis," Journal of Political Economy, University of Chicago Press, vol. 88(5), pages 829-853, October.
    4. Marshall Blume & Robert Stambaugh, "undated". "Biases in Computed Returns: An Application to the Size Effect (Revision of 2-83)," Rodney L. White Center for Financial Research Working Papers 11-83, Wharton School Rodney L. White Center for Financial Research.
    5. Kwan, Amy & Masulis, Ronald & McInish, Thomas H., 2015. "Trading rules, competition for order flow and market fragmentation," Journal of Financial Economics, Elsevier, vol. 115(2), pages 330-348.
    6. Blume, Marshall E. & Stambaugh, Robert F., 1983. "Biases in computed returns : An application to the size effect," Journal of Financial Economics, Elsevier, vol. 12(3), pages 387-404, November.
    7. Maureen O'Hara & Chen Yao & Mao Ye, 2014. "What's Not There: Odd Lots and Market Data," Journal of Finance, American Finance Association, vol. 69(5), pages 2199-2236, October.
    8. Brad M. Barber & Terrance Odean & Ning Zhu, 2009. "Do Retail Trades Move Markets?," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 151-186, January.
    9. Menkveld, Albert J. & Yueshen, Bart Zhou & Zhu, Haoxiang, 2017. "Shades of darkness: A pecking order of trading venues," Journal of Financial Economics, Elsevier, vol. 124(3), pages 503-534.
    10. 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.
    11. Campbell, John Y. & Ramadorai, Tarun & Schwartz, Allie, 2009. "Caught on tape: Institutional trading, stock returns, and earnings announcements," Journal of Financial Economics, Elsevier, vol. 92(1), pages 66-91, April.
    12. 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.
    13. Tarun Chordia & Sahn-Wook Huh & Avanidhar Subrahmanyam, 2007. "The Cross-Section of Expected Trading Activity," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 709-740.
    14. Eric K. Kelley & Paul C. Tetlock, 2013. "How Wise Are Crowds? Insights from Retail Orders and Stock Returns," Journal of Finance, American Finance Association, vol. 68(3), pages 1229-1265, June.
    15. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
    16. Ekkehart Boehmer & Charles M. Jones & Xiaoyan Zhang, 2008. "Which Shorts Are Informed?," Journal of Finance, American Finance Association, vol. 63(2), pages 491-527, April.
    17. Chordia, Tarun & Subrahmanyam, Avanidhar, 2004. "Order imbalance and individual stock returns: Theory and evidence," Journal of Financial Economics, Elsevier, vol. 72(3), pages 485-518, June.
    18. Lee, Yi-Tsung & Liu, Yu-Jane & Roll, Richard & Subrahmanyam, Avanidhar, 2004. "Order Imbalances and Market Efficiency: Evidence from the Taiwan Stock Exchange," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(2), pages 327-341, June.
    19. Fong, Kingsley Y. L. & Gallagher, David R. & Lee, Adrian D., 2014. "Individual Investors and Broker Types," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(2), pages 431-451, April.
    20. Lee, Charles M. C. & Radhakrishna, Balkrishna, 2000. "Inferring investor behavior: Evidence from TORQ data," Journal of Financial Markets, Elsevier, vol. 3(2), pages 83-111, May.
    21. Robert Battalio & Shane A. Corwin & Robert Jennings, 2016. "Can Brokers Have It All? On the Relation between Make-Take Fees and Limit Order Execution Quality," Journal of Finance, American Finance Association, vol. 71(5), pages 2193-2238, October.
    22. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Ardia & Cl'ement Aymard & Tolga Cenesizoglu, 2024. "Revisiting Boehmer et al. (2021): Recent Period, Alternative Method, Different Conclusions," Papers 2403.17095, arXiv.org.
    2. Reza Bradrania & Andrew Grant & Peter Joakim Westerholm & Wei Wu, 2017. "Fool's mate: What does CHESS tell us about individual investor trading performance?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(4), pages 981-1017, December.
    3. Fung, Scott & Obaid, Khaled & Tsai, Shih-Chuan, 2024. "Information acquisition and processing skills of institutions and retail investors around information shocks," Journal of Empirical Finance, Elsevier, vol. 77(C).
    4. Wang, Qin & Zhang, Jun, 2015. "Does individual investor trading impact firm valuation?," Journal of Corporate Finance, Elsevier, vol. 35(C), pages 120-135.
    5. Barardehi, Yashar H. & Bernhardt, Dan & Da, Zhi & Mitch Warachka, Mitch, 2022. "Institutional Liquidity Demand and the Internalization of Retail Order Flow : The Tail Does Not Wag the Dog," The Warwick Economics Research Paper Series (TWERPS) 1394, University of Warwick, Department of Economics.
    6. Wolff, Christian, 2017. "Trading in style: Retail investors vs. institutions," CEPR Discussion Papers 12462, C.E.P.R. Discussion Papers.
    7. Qin Wang & Jun Zhang, 2015. "Individual investor trading and stock liquidity," Review of Quantitative Finance and Accounting, Springer, vol. 45(3), pages 485-508, October.
    8. Tse-Chun Lin & Xin Liu, 2018. "Skewness, Individual Investor Preference, and the Cross-section of Stock Returns [Illiquidity and stock returns: cross-section and time-series effects]," Review of Finance, European Finance Association, vol. 22(5), pages 1841-1876.
    9. Choy, Siu-Kai, 2015. "Retail clientele and option returns," Journal of Banking & Finance, Elsevier, vol. 51(C), pages 26-42.
    10. Wang, Albert Y. & Young, Michael, 2023. "Mood, attention, and household trading: Evidence from terrorist attacks," Journal of Financial Markets, Elsevier, vol. 66(C).
    11. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 4-2015, January-A.
    12. Chen, Zhijuan & Lin, William T. & Ma, Changfeng, 2019. "Do individual investors demand or provide liquidity? New evidence from dividend announcements," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    13. Zhijuan Chen & William T. Lin & Changfeng Ma & Kent Wang, 2020. "Are individual investors liquidity providers around earnings announcements? Evidence from an emerging market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3447-3475, December.
    14. Umut c{C}etin & Alaina Danilova, 2022. "Order routing and market quality: Who benefits from internalisation?," Papers 2212.07827, arXiv.org.
    15. Hwang, Yoontae & Park, Junpyo & Kim, Jang Ho & Lee, Yongjae & Fabozzi, Frank J., 2024. "Heterogeneous trading behaviors of individual investors: A deep clustering approach," Finance Research Letters, Elsevier, vol. 65(C).
    16. Lawrence, Alastair, 2013. "Individual investors and financial disclosure," Journal of Accounting and Economics, Elsevier, vol. 56(1), pages 130-147.
    17. Ivo Welch, 2022. "The Wisdom of the Robinhood Crowd," Journal of Finance, American Finance Association, vol. 77(3), pages 1489-1527, June.
    18. Danny Lo, 2015. "Essays in Market Microstructure and Investor Trading," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 22, July-Dece.
    19. Gamble, Keith Jacks & Xu, Wei, 2017. "Informed retail investors: Evidence from retail short sales," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 59-72.
    20. Farrell, Michael & Green, T. Clifton & Jame, Russell & Markov, Stanimir, 2022. "The democratization of investment research and the informativeness of retail investor trading," Journal of Financial Economics, Elsevier, vol. 145(2), pages 616-641.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jfinan:v:76:y:2021:i:5:p:2249-2305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/afaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.