IDEAS home Printed from https://ideas.repec.org/p/rdg/emxxdp/em-dp2019-10.html
   My bibliography  Save this paper

Information, prices and efficiency in an online betting market

Author

Listed:
  • Guy Elaad

    (Department of Economics and Business Management, Ariel University)

  • J. James Reade

    (Department of Economics, University of Reading)

  • Carl Singleton

    (Department of Economics, University of Reading)

Abstract

We contribute to the discussion on betting market efficiency by studying the odds (or prices) set by fifty-one online bookmakers, for the result outcomes in over 16,000 association football matches in England since 2010. Adapting a methodology typically used to evaluate forecast efficiency, we test the Efficient Market Hypothesis in this context. We find odds are generally not biased when compared against actual match outcomes, both in terms of favourite-longshot or outcome types. But individual bookmakers are not efficient. Their own odds do not appear to use fully the information contained in their competitors' odds.

Suggested Citation

  • Guy Elaad & J. James Reade & Carl Singleton, 2019. "Information, prices and efficiency in an online betting market," Economics Discussion Papers em-dp2019-10, Department of Economics, University of Reading.
  • Handle: RePEc:rdg:emxxdp:em-dp2019-10
    as

    Download full text from publisher

    File URL: http://www.reading.ac.uk/web/FILES/economics/emdp201910.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. Ioannis Asimakopoulos & John Goddard, 2004. "Forecasting football results and the efficiency of fixed-odds betting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(1), pages 51-66.
    3. James Reade, 2014. "Information And Predictability: Bookmakers, Prediction Markets And Tipsters As Forecasters," Journal of Prediction Markets, University of Buckingham Press, vol. 8(1), pages 43-76.
    4. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    5. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    6. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1.
    7. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    8. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    9. repec:bla:econom:v:56:y:1989:i:223:p:323-41 is not listed on IDEAS
    10. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    11. Steven D. Levitt, 2004. "Why are gambling markets organised so differently from financial markets?," Economic Journal, Royal Economic Society, vol. 114(495), pages 223-246, April.
    12. Thaler, Richard H & Ziemba, William T, 1988. "Parimutuel Betting Markets: Racetracks and Lotteries," Journal of Economic Perspectives, American Economic Association, vol. 2(2), pages 161-174, Spring.
    13. Tim Kuypers, 2000. "Information and efficiency: an empirical study of a fixed odds betting market," Applied Economics, Taylor & Francis Journals, vol. 32(11), pages 1353-1363.
    14. 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.
    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. J Reade & C Singleton & L Vaughan Williams, 2020. "Betting Markets for English Premier League Results and Scorelines: Evaluating a Simple Forecasting Model," Economic Issues Journal Articles, Economic Issues, vol. 25(1), pages 87-106, March.
    2. Luca De Angelis & J. James Reade, 2022. "Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball," Economics Discussion Papers em-dp2022-01, Department of Economics, University of Reading.
    3. Kai Fischer & Justus Haucap, 2020. "Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games during the Covid-19 Pandemic," CESifo Working Paper Series 8526, CESifo.
    4. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    5. Guy Elaad, 2020. "Home-field advantage and biased prediction markets in English soccer," Applied Economics Letters, Taylor & Francis Journals, vol. 27(14), pages 1170-1174, July.
    6. Kai Fischer & Justus Haucap, 2022. "Home advantage in professional soccer and betting market efficiency: The role of spectator crowds," Kyklos, Wiley Blackwell, vol. 75(2), pages 294-316, May.
    7. He, Xue-Zhong & Treich, Nicolas, 2017. "Prediction market prices under risk aversion and heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 70(C), pages 105-114.
    8. Pascal Flurin Meier & Raphael Flepp & Egon Franck, 2021. "Are sports betting markets semistrong efficient? Evidence from the COVID-19 pandemic," Working Papers 387, University of Zurich, Department of Business Administration (IBW).
    9. Goto, Shingo & Yamada, Toru, 2023. "What drives biased odds in sports betting markets: Bettors’ irrationality and the role of bookmakers," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 252-270.
    10. David Winkelmann & Marius Ötting & Christian Deutscher & Tomasz Makarewicz, 2024. "Are Betting Markets Inefficient? Evidence From Simulations and Real Data," Journal of Sports Economics, , vol. 25(1), pages 54-97, January.
    11. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    12. Ramirez, Philip & Reade, J. James & Singleton, Carl, 2023. "Betting on a buzz: Mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1413-1423.
    13. Ruud H. Koning & Renske Zijm, 2023. "Betting market efficiency and prediction in binary choice models," Annals of Operations Research, Springer, vol. 325(1), pages 135-148, June.
    14. Luca De Angelis & J. James Reade, 2023. "Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball," Annals of Operations Research, Springer, vol. 325(1), pages 391-418, June.

    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. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    2. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    3. Kai Fischer & Justus Haucap, 2022. "Home advantage in professional soccer and betting market efficiency: The role of spectator crowds," Kyklos, Wiley Blackwell, vol. 75(2), pages 294-316, May.
    4. David Winkelmann & Marius Ötting & Christian Deutscher & Tomasz Makarewicz, 2024. "Are Betting Markets Inefficient? Evidence From Simulations and Real Data," Journal of Sports Economics, , vol. 25(1), pages 54-97, January.
    5. Goto, Shingo & Yamada, Toru, 2023. "What drives biased odds in sports betting markets: Bettors’ irrationality and the role of bookmakers," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 252-270.
    6. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    7. Gross, Johannes & Rebeggiani, Luca, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," MPRA Paper 87230, University Library of Munich, Germany.
    8. Kai Fischer & Justus Haucap, 2020. "Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games during the Covid-19 Pandemic," CESifo Working Paper Series 8526, CESifo.
    9. Pascal Flurin Meier & Raphael Flepp & Egon Franck, 2021. "Are sports betting markets semistrong efficient? Evidence from the COVID-19 pandemic," Working Papers 387, University of Zurich, Department of Business Administration (IBW).
    10. Luca De Angelis & J. James Reade, 2023. "Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball," Annals of Operations Research, Springer, vol. 325(1), pages 391-418, June.
    11. Luca De Angelis & J. James Reade, 2022. "Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball," Economics Discussion Papers em-dp2022-01, Department of Economics, University of Reading.
    12. Guy Elaad, 2020. "Home-field advantage and biased prediction markets in English soccer," Applied Economics Letters, Taylor & Francis Journals, vol. 27(14), pages 1170-1174, July.
    13. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    14. Oliver Merz & Raphael Flepp & Egon Franck, 2019. "Does sentiment harm market efficiency? An empirical analysis using a betting exchange setting," Working Papers 381, University of Zurich, Department of Business Administration (IBW).
    15. Ruud H. Koning & Renske Zijm, 2023. "Betting market efficiency and prediction in binary choice models," Annals of Operations Research, Springer, vol. 325(1), pages 135-148, June.
    16. Babatunde Buraimo & David Peel & Rob Simmons, 2013. "Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions," IJFS, MDPI, vol. 1(4), pages 1-15, December.
    17. Alexis Direr, 2013. "Are betting markets efficient? Evidence from European Football Championships," Applied Economics, Taylor & Francis Journals, vol. 45(3), pages 343-356, January.
    18. Nicos Zafiris, 2016. "Is There Such A Thing As A Safe Bet ?," Journal of Gambling Business and Economics, University of Buckingham Press, vol. 10(1), pages 40-65.
    19. Montone, Maurizio, 2021. "Optimal pricing in the online betting market," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 344-363.
    20. J Reade & C Singleton & L Vaughan Williams, 2020. "Betting Markets for English Premier League Results and Scorelines: Evaluating a Simple Forecasting Model," Economic Issues Journal Articles, Economic Issues, vol. 25(1), pages 87-106, March.

    More about this item

    Keywords

    prediction markets; Efficient Market Hypothesis; favourite-longshot bias; forecast efficiency;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Z29 - Other Special Topics - - Sports Economics - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:rdg:emxxdp:em-dp2019-10. 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: Alexander Mihailov (email available below). General contact details of provider: https://edirc.repec.org/data/derdguk.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.