IDEAS home Printed from https://ideas.repec.org/a/buc/jpredm/v2y2008i2p13-28.html
   My bibliography  Save this article

The Relative Importance of Strength and Weight in Processing New Information in the College Football Betting Market

Author

Listed:
  • Greg Durham
  • Mukunthan Santhanakrishnan

Abstract

Griffin and Tversky (1992) suggest that individuals, when formulating posterior probabilities based on the available evidence, tend to overreact to a new piece of evidence's strength while underreacting to the relative importance of its weight. We test this prediction using the college football betting market, a market that is commonly employed in tests for efficiency and rationality. Using average points in excess of the spread and streak against the spread as measures for strength and weight, respectively, we find that bettors overreact to strength and underreact to weight. These results are consistent with the predictions of Griffin and Tversky, as well as with the findings of Sorescu and Subrahmanyam (2006) and Barberis, Shleifer, and Vishny (1998) in financial market settings. Our work also provides insight into how behavioral biases might affect price-formation processes in other markets.

Suggested Citation

  • Greg Durham & Mukunthan Santhanakrishnan, 2008. "The Relative Importance of Strength and Weight in Processing New Information in the College Football Betting Market," Journal of Prediction Markets, University of Buckingham Press, vol. 2(2), pages 13-28, September.
  • Handle: RePEc:buc:jpredm:v:2:y:2008:i:2:p:13-28
    as

    Download full text from publisher

    File URL: http://www.ingentaconnect.com/content/ubpl/jpm/2008/00000002/00000002/art00002
    Download Restriction: no
    ---><---

    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. Sorescu, Sorin & Subrahmanyam, Avanidhar, 2006. "The Cross Section of Analyst Recommendations," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 41(1), pages 139-168, March.
    3. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    4. Dare, William H. & MacDonald, S. Scott, 1996. "A generalized model for testing the home and favorite team advantage in point spread markets," Journal of Financial Economics, Elsevier, vol. 40(2), pages 295-318, February.
    5. Durvasula, Srinivas, et al, 1993. "Assessing the Cross-National Applicability of Consumer Behavior Models: A Model of Attitude toward Advertising in General," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(4), pages 626-636, March.
    6. Diane K. Denis & John J. McConnell & Alexei V. Ovtchinnikov & Yun Yu, 2003. "S&P 500 Index Additions and Earnings Expectations," Journal of Finance, American Finance Association, vol. 58(5), pages 1821-1840, October.
    7. Avery, Christopher & Chevalier, Judith, 1999. "Identifying Investor Sentiment from Price Paths: The Case of Football Betting," The Journal of Business, University of Chicago Press, vol. 72(4), pages 493-521, October.
    8. Gregory R. Durham & Michael G. Hertzel & J. Spencer Martin, 2005. "The Market Impact of Trends and Sequences in Performance: New Evidence," Journal of Finance, American Finance Association, vol. 60(5), pages 2551-2569, October.
    9. 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.
    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. Choi, Darwin & Hui, Sam K., 2014. "The role of surprise: Understanding overreaction and underreaction to unanticipated events using in-play soccer betting market," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 614-629.

    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. Tobias J. Moskowitz, 2021. "Asset Pricing and Sports Betting," Journal of Finance, American Finance Association, vol. 76(6), pages 3153-3209, December.
    2. David Hirshleifer, 2001. "Investor Psychology and Asset Pricing," Journal of Finance, American Finance Association, vol. 56(4), pages 1533-1597, August.
    3. Michael Sinkey & Trevon Logan, 2014. "Does the Hot Hand Drive the Market? Evidence from College Football Betting Markets," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 40(4), pages 583-603, September.
    4. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
    5. Choi, Darwin & Hui, Sam K., 2014. "The role of surprise: Understanding overreaction and underreaction to unanticipated events using in-play soccer betting market," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 614-629.
    6. Ashour, Samar & Hao, Grace Qing & Harper, Adam, 2023. "Investor sentiment, style investing, and momentum," Journal of Financial Markets, Elsevier, vol. 62(C).
    7. Gregory R. Durham & Michael G. Hertzel & J. Spencer Martin, 2005. "The Market Impact of Trends and Sequences in Performance: New Evidence," Journal of Finance, American Finance Association, vol. 60(5), pages 2551-2569, October.
    8. Harrison Hong & Terence Lim & Jeremy C. Stein, 2000. "Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies," Journal of Finance, American Finance Association, vol. 55(1), pages 265-295, February.
    9. Tobias J. Moskowitz & Mark Grinblatt, 2002. "What Do We Really Know About the Cross-Sectional Relation Between Past and Expected Returns?," Yale School of Management Working Papers ysm259, Yale School of Management.
    10. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    11. Kobana Abukari & Isaac Otchere, 2020. "Dominance of hybrid contratum strategies over momentum and contrarian strategies: half a century of evidence," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(4), pages 471-505, December.
    12. Stefano DellaVigna & Joshua M. Pollet, 2005. "Attention, Demographics, and the Stock Market," NBER Working Papers 11211, National Bureau of Economic Research, Inc.
    13. Cakici, Nusret & Tang, Yi & Yan, An, 2016. "Do the size, value, and momentum factors drive stock returns in emerging markets?," Journal of International Money and Finance, Elsevier, vol. 69(C), pages 179-204.
    14. Wu, Chen-Hui & Wu, Chin-Shun & Liu, Victor W., 2009. "The conservatism bias in an emerging stock market: Evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 17(4), pages 494-505, September.
    15. Blackburn, Douglas W. & Cakici, Nusret, 2017. "Overreaction and the cross-section of returns: International evidence," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 1-14.
    16. Hofmann, Daniel & Keiber, Karl Ludwig & Luczak, Adalbert, 2022. "Up and down together? On the linkage of momentum and reversal," Global Finance Journal, Elsevier, vol. 54(C).
    17. Ruanmin Cao & Lajos Horváth & Zhenya Liu & Yuqian Zhao, 2020. "A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis," Review of Quantitative Finance and Accounting, Springer, vol. 54(1), pages 335-358, January.
    18. Lin, Chaonan & Ko, Kuan-Cheng & Chen, Yu-Lin & Chu, Hsiang-Hui, 2016. "Information discreteness, price limits and earnings momentum," Pacific-Basin Finance Journal, Elsevier, vol. 37(C), pages 1-22.
    19. Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Monetary Momentum," CESifo Working Paper Series 6648, CESifo.
    20. Dimitri Vayanos & Paul Woolley, 2013. "An Institutional Theory of Momentum and Reversal," The Review of Financial Studies, Society for Financial Studies, vol. 26(5), pages 1087-1145.

    More about this item

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    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:buc:jpredm:v:2:y:2008:i:2:p:13-28. 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: Dominic Cortis, University of Malta (email available below). General contact details of provider: http://www.ubpl.co.uk/ .

    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.