IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v86y2023ics1057521922004185.html
   My bibliography  Save this article

Tracking investor gambling intensity

Author

Listed:
  • Zhu, Hongbing
  • Yang, Lihua
  • Xu, Changxin

Abstract

We provide a simple method to track firm-specific investor gambling intensity based on the publicly available transaction data. This identification approach effectively incorporates information on what and how much to buy in the trading decision of an investor with a gambling preference. With empirical analysis based on data of the Chinese stock market from January 2003 to May 2021, we document that investor gambling intensity is strongly persistent and significantly predicts future stock returns, which is not a rediscovery of the well-known lottery effect. Stocks with high aggregate gambling intensity underperform stocks with low aggregate gambling intensity by approximately 117 basis points over the following month. Several potential explanations for such empirical findings are examined, and we document support for the explanation based on information diffusion.

Suggested Citation

  • Zhu, Hongbing & Yang, Lihua & Xu, Changxin, 2023. "Tracking investor gambling intensity," International Review of Financial Analysis, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:finana:v:86:y:2023:i:c:s1057521922004185
    DOI: 10.1016/j.irfa.2022.102468
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521922004185
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2022.102468?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhou, Xuemei & Liu, Qiang & Guo, Shuxin, 2021. "Do overnight returns explain firm-specific investor sentiment in China?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 451-477.
    2. Paul A. Gompers & Andrew Metrick, 2001. "Institutional Investors and Equity Prices," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 229-259.
    3. Xuemin (Sterling) Yan & Zhe Zhang, 2009. "Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?," The Review of Financial Studies, Society for Financial Studies, vol. 22(2), pages 893-924, February.
    4. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    5. Bali, Turan G. & Cakici, Nusret & Whitelaw, Robert F., 2011. "Maxing out: Stocks as lotteries and the cross-section of expected returns," Journal of Financial Economics, Elsevier, vol. 99(2), pages 427-446, February.
    6. Barberis, Nicholas & Thaler, Richard, 2003. "A survey of behavioral finance," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 18, pages 1053-1128, Elsevier.
    7. Ng, Lilian & Wu, Fei, 2006. "Revealed stock preferences of individual investors: Evidence from Chinese equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 14(2), pages 175-192, April.
    8. Nicholas Barberis & Ming Huang, 2008. "Stocks as Lotteries: The Implications of Probability Weighting for Security Prices," American Economic Review, American Economic Association, vol. 98(5), pages 2066-2100, December.
    9. Lee, Charles M.C. & Sun, Stephen Teng & Wang, Rongfei & Zhang, Ran, 2019. "Technological links and predictable returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 76-96.
    10. David Hirshleifer & Siew Hong Teoh & Jeff Jiewei Yu, 2011. "Short Arbitrage, Return Asymmetry, and the Accrual Anomaly," The Review of Financial Studies, Society for Financial Studies, vol. 24(7), pages 2429-2461.
    11. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    12. Brian Boyer & Todd Mitton & Keith Vorkink, 2010. "Expected Idiosyncratic Skewness," The Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 169-202, January.
    13. Karen Schnatterly & Kenneth W. Shaw & William W. Jennings, 2008. "Information advantages of large institutional owners," Strategic Management Journal, Wiley Blackwell, vol. 29(2), pages 219-227, February.
    14. Aboody, David & Even-Tov, Omri & Lehavy, Reuven & Trueman, Brett, 2018. "Overnight Returns and Firm-Specific Investor Sentiment," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(2), pages 485-505, April.
    15. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    16. Conrad, Jennifer & Kapadia, Nishad & Xing, Yuhang, 2014. "Death and jackpot: Why do individual investors hold overpriced stocks?," Journal of Financial Economics, Elsevier, vol. 113(3), pages 455-475.
    17. Markus K. Brunnermeier & Jonathan A. Parker & Christian Gollier, 2007. "Optimal Beliefs, Asset Prices, and the Preference for Skewed Returns," American Economic Review, American Economic Association, vol. 97(2), pages 159-165, May.
    18. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    19. Kumar, Alok & Page, Jeremy K. & Spalt, Oliver G., 2016. "Gambling and Comovement," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(1), pages 85-111, February.
    20. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    21. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    22. Zeng, Yeqin, 2016. "Institutional investors: Arbitrageurs or rational trend chasers," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 240-262.
    23. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    24. Liu, Jianan & Stambaugh, Robert F. & Yuan, Yu, 2019. "Size and value in China," Journal of Financial Economics, Elsevier, vol. 134(1), pages 48-69.
    25. 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.
    26. Lee, Charles M. C., 1992. "Earnings news and small traders : An intraday analysis," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 265-302, August.
    27. T. Clifton Green & Byoung-Hyoun Hwang, 2012. "Initial Public Offerings as Lotteries: Skewness Preference and First-Day Returns," Management Science, INFORMS, vol. 58(2), pages 432-444, February.
    28. Alok Kumar, 2009. "Who Gambles in the Stock Market?," Journal of Finance, American Finance Association, vol. 64(4), pages 1889-1933, August.
    29. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    30. Xiaohui Gao & Tse-Chun Lin, 2015. "Do Individual Investors Treat Trading as a Fun and Exciting Gambling Activity? Evidence from Repeated Natural Experiments," The Review of Financial Studies, Society for Financial Studies, vol. 28(7), pages 2128-2166.
    31. Chan, Louis K C & Jegadeesh, Narasimhan & Lakonishok, Josef, 1996. "Momentum Strategies," Journal of Finance, American Finance Association, vol. 51(5), pages 1681-1713, December.
    32. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    33. Zhu, Hong-bing & Zhang, Bing & Yang, Li-hua, 2021. "The gambling preference and stock price: Evidence from China's stock market," Emerging Markets Review, Elsevier, vol. 49(C).
    34. Ng, Lilian & Wu, Fei, 2007. "The trading behavior of institutions and individuals in Chinese equity markets," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2695-2710, September.
    35. Li, Xindan & Geng, Ziyang & Subrahmanyam, Avanidhar & Yu, Honghai, 2017. "Do wealthy investors have an informational advantage? Evidence based on account classifications of individual investors," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 1-18.
    36. Han, Bing & Kumar, Alok, 2013. "Speculative Retail Trading and Asset Prices," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(2), pages 377-404, April.
    37. Jang, Jeewon & Kang, Jangkoo, 2019. "Probability of price crashes, rational speculative bubbles, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 222-247.
    38. Cai, Fang & Han, Song & Li, Dan & Li, Yi, 2019. "Institutional herding and its price impact: Evidence from the corporate bond market," Journal of Financial Economics, Elsevier, vol. 131(1), pages 139-167.
    39. 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.
    40. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    41. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    42. Ke, Bin & Ramalingegowda, Santhosh, 2005. "Do institutional investors exploit the post-earnings announcement drift?," Journal of Accounting and Economics, Elsevier, vol. 39(1), pages 25-53, February.
    43. Hendershott, Terrence & Livdan, Dmitry & Rösch, Dominik, 2020. "Asset pricing: A tale of night and day," Journal of Financial Economics, Elsevier, vol. 138(3), pages 635-662.
    44. 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.
    45. Florian Weißofner & Ulrich Wessels, 2020. "Overnight Returns: An International Sentiment Measure," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(2), pages 205-217, April.
    46. Liu, Bibo & Wang, Huijun & Yu, Jianfeng & Zhao, Shen, 2020. "Time-varying demand for lottery: Speculation ahead of earnings announcements," Journal of Financial Economics, Elsevier, vol. 138(3), pages 789-817.
    47. 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.
    48. Byun, Suk-Joon & Kim, Da-Hea, 2016. "Gambling preference and individual equity option returns," Journal of Financial Economics, Elsevier, vol. 122(1), pages 155-174.
    49. 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.
    50. Anne Jones Dorn & Daniel Dorn & Paul Sengmueller, 2015. "Trading as Gambling," Management Science, INFORMS, vol. 61(10), pages 2376-2393, October.
    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. Atilgan, Yigit & Bali, Turan G. & Demirtas, K. Ozgur & Gunaydin, A. Doruk, 2020. "Left-tail momentum: Underreaction to bad news, costly arbitrage and equity returns," Journal of Financial Economics, Elsevier, vol. 135(3), pages 725-753.
    2. Yao, Shouyu & Wang, Chunfeng & Cui, Xin & Fang, Zhenming, 2019. "Idiosyncratic skewness, gambling preference, and cross-section of stock returns: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 464-483.
    3. Liu, Bibo & Wang, Huijun & Yu, Jianfeng & Zhao, Shen, 2020. "Time-varying demand for lottery: Speculation ahead of earnings announcements," Journal of Financial Economics, Elsevier, vol. 138(3), pages 789-817.
    4. Yang, Baochen & Ye, Tao & Ma, Yao, 2022. "Financing anomaly, mispricing and cross-sectional return predictability," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 579-598.
    5. 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.
    6. Sun, Kaisi & Wang, Hui & Zhu, Yifeng, 2023. "Salience theory in price and trading volume: Evidence from China," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 38-61.
    7. Byun, Suk-Joon & Kim, Da-Hea, 2016. "Gambling preference and individual equity option returns," Journal of Financial Economics, Elsevier, vol. 122(1), pages 155-174.
    8. Kelley Bergsma & Jitendra Tayal, 2019. "Short Interest and Lottery Stocks," Financial Management, Financial Management Association International, vol. 48(1), pages 187-227, March.
    9. Xu, Zhongxiang & Chevapatrakul, Thanaset & Li, Xiafei, 2019. "Return asymmetry and the cross section of stock returns," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 93-110.
    10. Andreas Oehler & Julian Schneider, 2022. "Gambling with lottery stocks?," Journal of Asset Management, Palgrave Macmillan, vol. 23(6), pages 477-503, October.
    11. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, September.
    12. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2019. "Extreme daily returns and the cross-section of expected returns: Evidence from Brazil," Journal of Business Research, Elsevier, vol. 102(C), pages 201-211.
    13. Nguyen, Hung T. & Truong, Cameron, 2018. "When are extreme daily returns not lottery? At earnings announcements!," Journal of Financial Markets, Elsevier, vol. 41(C), pages 92-116.
    14. Lin, Mei-Chen & Lin, Yu-Ling, 2021. "Idiosyncratic skewness and cross-section of stock returns: Evidence from Taiwan," International Review of Financial Analysis, Elsevier, vol. 77(C).
    15. Yao, Shouyu & Wang, Chunfeng & Fang, Zhenming & Chiao, Chaoshin, 2021. "MAX is not the max under the interference of daily price limits: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 348-369.
    16. Melisa Ozdamar & Levent Akdeniz & Ahmet Sensoy, 2021. "Lottery-like preferences and the MAX effect in the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    17. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    18. Zhu, Hong-bing & Zhang, Bing & Yang, Li-hua, 2021. "The gambling preference and stock price: Evidence from China's stock market," Emerging Markets Review, Elsevier, vol. 49(C).
    19. Zhu, Zhaobo & Harrison, DavidM. & Seiler, MichaelJ., 2020. "Preference for lottery features in real estate investment trusts," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 599-613.
    20. Zhong, Angel & Gray, Philip, 2016. "The MAX effect: An exploration of risk and mispricing explanations," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 76-90.

    More about this item

    Keywords

    Investor gambling preference; Gambling intensity; Return predictability; Information diffusion;
    All these keywords.

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    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:eee:finana:v:86:y:2023:i:c:s1057521922004185. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.