IDEAS home Printed from https://ideas.repec.org/a/eee/empfin/v64y2021icp272-294.html
   My bibliography  Save this article

Time-dependent lottery preference and the cross-section of stock returns

Author

Listed:
  • Lin, Chaonan
  • Chen, Hong-Yi
  • Ko, Kuan-Cheng
  • Yang, Nien-Tzu

Abstract

Highlighting the importance of benchmark to identify lottery-like payoffs of stocks, this study proposes that investors’ lottery preference is formed toward tracking stocks’ performance over time. Accordingly, we develop a strategy based on time-dependent maximum daily return (denoted as TMAX) by buying (short selling) stocks with the most recent maximum daily returns (MAX) ranked in the bottom (top) decile of the historical distribution. The TMAX strategy generates significant premium that subsumes the profitability of Bali, Cakici, and Whitelaw’s (2011) MAX strategy, but not vice versa. A major advantage of the TMAX strategy is its time-invariant profitability across different periods and sentiment states. Further analyses show that the TMAX premium can be explained by shorting flow and behavioral theories, supporting the time-dependent feature of lottery preference.

Suggested Citation

  • Lin, Chaonan & Chen, Hong-Yi & Ko, Kuan-Cheng & Yang, Nien-Tzu, 2021. "Time-dependent lottery preference and the cross-section of stock returns," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 272-294.
    • Handle: RePEc:eee:empfin:v:64:y:2021:i:c:p:272-294
    DOI: 10.1016/j.jempfin.2021.09.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927539821000815
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jempfin.2021.09.005?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. 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. Byun, Suk-Joon & Goh, Jihoon & Kim, Da-Hea, 2020. "The role of psychological barriers in lottery-related anomalies," Journal of Banking & Finance, Elsevier, vol. 114(C).
    3. Kent Daniel & David Hirshleifer & Lin Sun, 2020. "Short- and Long-Horizon Behavioral Factors," The Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1673-1736.
    4. Wang, Xue & Yan, Xuemin (Sterling) & Zheng, Lingling, 2020. "Shorting flows, public disclosure, and market efficiency," Journal of Financial Economics, Elsevier, vol. 135(1), pages 191-212.
    5. 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.
    6. 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.
    7. Eugene F. Fama & Kenneth R. French, 2008. "Dissecting Anomalies," Journal of Finance, American Finance Association, vol. 63(4), pages 1653-1678, August.
    8. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2012. "Salience Theory of Choice Under Risk," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1243-1285.
    9. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2013. "Salience and Asset Prices," American Economic Review, American Economic Association, vol. 103(3), pages 623-628, May.
    10. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    11. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    12. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    13. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    14. Michael J. Cooper & Huseyin Gulen & Michael J. Schill, 2008. "Asset Growth and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 63(4), pages 1609-1651, August.
    15. Grinblatt, Mark & Han, Bing, 2005. "Prospect theory, mental accounting, and momentum," Journal of Financial Economics, Elsevier, vol. 78(2), pages 311-339, November.
    16. Li, Jun & Yu, Jianfeng, 2012. "Investor attention, psychological anchors, and stock return predictability," Journal of Financial Economics, Elsevier, vol. 104(2), pages 401-419.
    17. Nicholas Barberis & Abhiroop Mukherjee & Baolian Wang, 2016. "Prospect Theory and Stock Returns: An Empirical Test," The Review of Financial Studies, Society for Financial Studies, vol. 29(11), pages 3068-3107.
    18. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    19. Kent Daniel & David Hirshleifer & Lin Sun, 2020. "Short- and Long-Horizon Behavioral Factors," Review of Finance, European Finance Association, vol. 33(4), pages 1673-1736.
    20. Fong, Wai Mun & Toh, Benjamin, 2014. "Investor sentiment and the MAX effect," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 190-201.
    21. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    22. Mohrschladt, Hannes, 2021. "The ordering of historical returns and the cross-section of subsequent returns," Journal of Banking & Finance, Elsevier, vol. 125(C).
    23. Thomas J. George & Chuan-Yang Hwang, 2004. "The 52-Week High and Momentum Investing," Journal of Finance, American Finance Association, vol. 59(5), pages 2145-2176, October.
    24. Kewei Hou & Chen Xue & Lu Zhang, 2015. "Editor's Choice Digesting Anomalies: An Investment Approach," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 650-705.
    25. 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.
    26. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    27. 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.
    28. 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.
    29. 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.
    30. Smith, Daniel R., 2007. "Conditional coskewness and asset pricing," Journal of Empirical Finance, Elsevier, vol. 14(1), pages 91-119, January.
    31. Wai Mun Fong, 2014. "The MAX Effect," Palgrave Macmillan Books, in: The Lottery Mindset: Investors, Gambling and the Stock Market, chapter 7, pages 138-155, Palgrave Macmillan.
    32. Alok Kumar, 2009. "Who Gambles in the Stock Market?," Journal of Finance, American Finance Association, vol. 64(4), pages 1889-1933, August.
    33. 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.
    34. Thaler, Richard, 1980. "Toward a positive theory of consumer choice," Journal of Economic Behavior & Organization, Elsevier, vol. 1(1), pages 39-60, March.
    35. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, June.
    36. Kraus, Alan & Litzenberger, Robert H, 1976. "Skewness Preference and the Valuation of Risk Assets," Journal of Finance, American Finance Association, vol. 31(4), pages 1085-1100, September.
    37. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    38. Li An & Huijun Wang & Jian Wang & Jianfeng Yu, 2020. "Lottery-Related Anomalies: The Role of Reference-Dependent Preferences," Management Science, INFORMS, vol. 66(1), pages 473-501, January.
    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. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, October.
    3. Cakici, Nusret & Zaremba, Adam, 2023. "Recency bias and the cross-section of international stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    4. Baars, Maren & Mohrschladt, Hannes, 2021. "An alternative behavioral explanation for the MAX effect," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 868-886.
    5. David Hirshleifer & Po-Hsuan Hsu & Dongmei Li, 2018. "Innovative Originality, Profitability, and Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2553-2605.
    6. 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.
    7. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
    8. Cosemans, Mathijs & Frehen, Rik, 2021. "Salience theory and stock prices: Empirical evidence," Journal of Financial Economics, Elsevier, vol. 140(2), pages 460-483.
    9. 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.
    10. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    11. Eom, Cheoljun & Eom, Yunsung & Park, Jong Won, 2023. "Left-tail momentum and tail properties of return distributions: A case of Korea," International Review of Financial Analysis, Elsevier, vol. 87(C).
    12. Xin Chen & Wei He & Libin Tao & Jianfeng Yu, 2023. "Attention and Underreaction-Related Anomalies," Management Science, INFORMS, vol. 69(1), pages 636-659, January.
    13. 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.
    14. 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.
    15. Sun, Kaisi & Wang, Hui & Zhu, Yifeng, 2022. "How is the change in left-tail risk priced in China?," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    16. Chabi-Yo, Fousseni & Ruenzi, Stefan & Weigert, Florian, 2018. "Crash Sensitivity and the Cross Section of Expected Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1059-1100, June.
    17. Cao, Ji & Rieger, Marc Oliver & Zhao, Lei, 2023. "Safety first, loss probability, and the cross section of expected stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 345-369.
    18. Lu, Jing & Ho, Keng-Yu & Ho, Po-Hsin & Ko, Kuan-Cheng, 2023. "CEO overconfidence, lottery preference and the cross-section of stock returns," Finance Research Letters, Elsevier, vol. 54(C).
    19. Hou, Kewei & Xue, Chen & Zhang, Lu, 2017. "Replicating Anomalies," Working Paper Series 2017-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    20. Ji Cao & Marc Oliver Rieger & Lei Zhao, 2019. "Safety First, Loss Probability, and the Cross Section of Expected Stock Returns," Working Paper Series 2019-02, University of Trier, Research Group Quantitative Finance and Risk Analysis.

    More about this item

    Keywords

    Lottery preference; Time dependence; Maximum daily returns; Stock returns;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • 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:empfin:v:64:y:2021:i:c:p:272-294. 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/jempfin .

    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.