IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v102y2019icp201-211.html
   My bibliography  Save this article

Extreme daily returns and the cross-section of expected returns: Evidence from Brazil

Author

Listed:
  • Berggrun, Luis
  • Cardona, Emilio
  • Lizarzaburu, Edmundo

Abstract

This paper examines whether extreme (positive) daily returns predict the cross-section of monthly stock returns in Brazil. We find a negative effect of the maximum (MAX) daily return on future performance which is in line with the findings from recent studies in the U.S. and Europe. High MAX stocks appear to cater to some investors who are looking for lottery-like stocks, as extreme positive return stocks offer the possibility of substantial gains with a low probability. Increased demand leads to overpricing of and ensuing lower returns to lottery-like stocks. Other proxies for extreme returns, such as idiosyncratic volatility and skewness, play a much weaker role (if any) as cross-sectional determinants of stock performance. We document that the MAX effect is significant only during economic contractions, thus suggesting that the gambling behavior in the stock market exacerbates during economic downturns.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jbrese:v:102:y:2019:i:c:p:201-211
    DOI: 10.1016/j.jbusres.2017.07.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296317302266
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2017.07.005?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. 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.
    2. 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.
    3. Ang, Andrew & Hodrick, Robert J. & Xing, Yuhang & Zhang, Xiaoyan, 2009. "High idiosyncratic volatility and low returns: International and further U.S. evidence," Journal of Financial Economics, Elsevier, vol. 91(1), pages 1-23, January.
    4. Jennifer Conrad & Robert F. Dittmar & Eric Ghysels, 2013. "Ex Ante Skewness and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 68(1), pages 85-124, February.
    5. 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.
    6. Merton, Robert C, 1987. "A Simple Model of Capital Market Equilibrium with Incomplete Information," Journal of Finance, American Finance Association, vol. 42(3), pages 483-510, July.
    7. 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.
    8. 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.
    9. Thomas R. Palfrey & Stephanie W. Wang, 2012. "Speculative Overpricing in Asset Markets With Information Flows," Econometrica, Econometric Society, vol. 80(5), pages 1937-1976, September.
    10. 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.
    11. Brian H. Boyer & Keith Vorkink, 2014. "Stock Options as Lotteries," Journal of Finance, American Finance Association, vol. 69(4), pages 1485-1527, August.
    12. 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.
    13. Pin-Huang Chou & Kuan-Cheng Ko & Szu-Tsen Kuo & Shinn-Juh Lin, 2012. "Firm characteristics, alternative factors, and asset-pricing anomalies: evidence from Japan," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 369-382, June.
    14. 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.
    15. Alok Kumar, 2009. "Who Gambles in the Stock Market?," Journal of Finance, American Finance Association, vol. 64(4), pages 1889-1933, August.
    16. 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.
    17. Annaert, Jan & De Ceuster, Marc & Verstegen, Kurt, 2013. "Are extreme returns priced in the stock market? European evidence," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3401-3411.
    18. Fu, Fangjian, 2009. "Idiosyncratic risk and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 91(1), pages 24-37, January.
    19. 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.
    20. Hsu, Junming & Yang, Tung-Hsiao & Sung, Po-Shen, 2016. "SEO firms' lottery-like characteristics, institutional ownership, and long-run performance," Journal of Business Research, Elsevier, vol. 69(6), pages 2160-2166.
    21. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-467.
    22. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    23. Fong, Wai Mun & Toh, Benjamin, 2014. "Investor sentiment and the MAX effect," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 190-201.
    24. 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.
    25. Stephanie Wang, 2012. "Speculative Overpricing in Asset Markets with Information Flows," Working Paper 489, Department of Economics, University of Pittsburgh, revised Jan 2012.
    26. 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.
    27. Todd Mitton & Keith Vorkink, 2007. "Equilibrium Underdiversification and the Preference for Skewness," The Review of Financial Studies, Society for Financial Studies, vol. 20(4), pages 1255-1288.
    28. Brennan, Michael J. & Chordia, Tarun & Subrahmanyam, Avanidhar, 1998. "Alternative factor specifications, security characteristics, and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 49(3), pages 345-373, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bradrania, Reza & Gao, Ya, 2024. "Lottery demand, weather and the cross-section of stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 42(C).
    2. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2024. "Evaluating asset pricing anomalies: Evidence from Latin America," Research in International Business and Finance, Elsevier, vol. 70(PB).
    3. Gao, Ya & Bradrania, Reza, 2024. "Property crime and lottery-related anomalies," Global Finance Journal, Elsevier, vol. 59(C).
    4. 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.

    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. 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.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
    3. 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.
    4. 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.
    5. Shuonan Yuan & Marc Oliver Rieger & Nilüfer Caliskan, 2020. "Maxing out: the puzzling influence of past maximum returns on future asset prices in a cross-country analysis," Management Review Quarterly, Springer, vol. 70(4), pages 567-589, November.
    6. Wan, Xiaoyuan, 2018. "Is the idiosyncratic volatility anomaly driven by the MAX or MIN effect? Evidence from the Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 53(C), pages 1-15.
    7. Annaert, Jan & De Ceuster, Marc & Verstegen, Kurt, 2013. "Are extreme returns priced in the stock market? European evidence," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3401-3411.
    8. 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.
    9. Ayadi, Mohamed A. & Cao, Xu & Lazrak, Skander & Wang, Yan, 2019. "Do idiosyncratic skewness and kurtosis really matter?," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    10. Andreas Oehler & Julian Schneider, 2022. "Gambling with lottery stocks?," Journal of Asset Management, Palgrave Macmillan, vol. 23(6), pages 477-503, October.
    11. Benjamin M Blau & Ryan J Whitby, 2017. "Range-based volatility, expected stock returns, and the low volatility anomaly," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
    12. 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.
    13. Zhao, Xiaojuan & Wang, Ye & Liu, Weiyi, 2024. "Someone like you: Lottery-like preference and the cross-section of expected returns in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    14. Yong-Ho Cheon & Kuan-Hui Lee, 2018. "Maxing Out Globally: Individualism, Investor Attention, and the Cross Section of Expected Stock Returns," Management Science, INFORMS, vol. 64(12), pages 5807-5831, December.
    15. 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).
    16. Gao, Ya & Bradrania, Reza, 2024. "Property crime and lottery-related anomalies," Global Finance Journal, Elsevier, vol. 59(C).
    17. Gao, Ya & Han, Xing & Xiong, Xiong, 2021. "Loss from the chasing of MAX stocks: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    18. Aboulamer, Anas & Kryzanowski, Lawrence, 2016. "Are idiosyncratic volatility and MAX priced in the Canadian market?," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 20-36.
    19. Nilesh Gupta & Joshy Jacob, 2021. "The Interplay Between Sentiment and MAX: Evidence from an Emerging Market," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 20(2), pages 192-217, August.
    20. Zhu, Hongbing & Yang, Lihua & Xu, Changxin, 2023. "Tracking investor gambling intensity," International Review of Financial Analysis, Elsevier, vol. 86(C).

    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:jbrese:v:102:y:2019:i:c:p:201-211. 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/jbusres .

    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.