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Towards a methodology for measuring the true degree of efficiency in a speculative market

Author

Listed:
  • S Lessmann

    (University of Hamburg)

  • M-C Sung

    (University of Southampton)

  • J E V Johnson

    (University of Southampton)

Abstract

Betting markets have drawn much attention in the economics, finance and operational research literature because they provide a valuable window on the manner in which individuals use information in wider financial markets. One question that has received particular attention is to what extent individuals discount information in market prices. The predominant approach to explore this issue involves predictive modeling to forecast market outcomes and examining empirically whether abnormal returns can be made by employing these forecasts. It is argued here that present practices to assess such forecasting models, including the use of point estimates and information, which would not be available in practice (at the forecasting stage) and failing to update forecasting models with information from the recent past, may give rise to misleading conclusions regarding a market's informational efficiency. Hypotheses are developed to conceptualize these views and are tested by means of extensive empirical experimentation using real-world data from the Hong Kong horserace betting market. Our study identifies several sources of bias and confirms that current practices may not be relied upon. A more appropriate modeling procedure for assessing the true degree of market efficiency is then proposed.

Suggested Citation

  • S Lessmann & M-C Sung & J E V Johnson, 2011. "Towards a methodology for measuring the true degree of efficiency in a speculative market," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2120-2132, December.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:12:d:10.1057_jors.2010.192
    DOI: 10.1057/jors.2010.192
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    References listed on IDEAS

    as
    1. Figlewski, Stephen, 1979. "Subjective Information and Market Efficiency in a Betting Market," Journal of Political Economy, University of Chicago Press, vol. 87(1), pages 75-88, February.
    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. Chris Hope, 2003. "When should you sack a football manager? Results from a simple model applied to the English Premiership," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(11), pages 1167-1176, November.
    4. repec:bla:econom:v:43:y:1976:i:17:p:139-50 is not listed on IDEAS
    5. Johnson, Johnnie E. V. & Bruce, Alistair C., 2001. "Calibration of Subjective Probability Judgments in a Naturalistic Setting," Organizational Behavior and Human Decision Processes, Elsevier, vol. 85(2), pages 265-290, July.
    6. Nikolaos Vlastakis & George Dotsis & Raphael Markellos, 2008. "Nonlinear modelling of European football scores using support vector machines," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 111-118.
    7. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2010. "Alternative methods of predicting competitive events: An application in horserace betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 518-536, July.
    8. Schnytzer, Adi & Shilony, Yuval, 1995. "Inside Information in a Betting Market," Economic Journal, Royal Economic Society, vol. 105(431), pages 963-971, July.
    9. Johnnie E. V. Johnson & Owen Jones & Leilei Tang, 2006. "Exploring Decision Makers' Use of Price Information in a Speculative Market," Management Science, INFORMS, vol. 52(6), pages 897-908, June.
    10. Raymond D. Sauer, 1998. "The Economics of Wagering Markets," Journal of Economic Literature, American Economic Association, vol. 36(4), pages 2021-2064, December.
    11. White, Edna M. & Dattero, Ronald & Flores, Benito, 1992. "Combining vector forecasts to predict thoroughbred horse race outcomes," International Journal of Forecasting, Elsevier, vol. 8(4), pages 595-611, December.
    12. S R Clarke & P Allsopp, 2001. "Fair measures of performance: the World Cup of cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(4), pages 471-479, April.
    13. Woodland, Linda M & Woodland, Bill M, 1994. "Market Efficiency and the Favorite-Longshot Bias: The Baseball Betting Market," Journal of Finance, American Finance Association, vol. 49(1), pages 269-279, March.
    14. David Forrest & Robert Simmons, 2008. "Sentiment in the betting market on Spanish football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 119-126.
    15. Hyun Song Shin, 2008. "Prices Of State Contingent Claims With Insider Traders, And The Favourite-Longshot Bias," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 34, pages 343-352, World Scientific Publishing Co. Pte. Ltd..
    16. Dixon, Mark J. & Pope, Peter F., 2004. "The value of statistical forecasts in the UK association football betting market," International Journal of Forecasting, Elsevier, vol. 20(4), pages 697-711.
    17. Williams, Leighton Vaughan & Paton, David, 1997. "Why Is There a Favourite-Longshot Bias in British Racetrack Betting Markets?," Economic Journal, Royal Economic Society, vol. 107(440), pages 150-158, January.
    18. M. Sung & J. E. V. Johnson, 2010. "Revealing Weak‐Form Inefficiency in a Market for State Contingent Claims: The Importance of Market Ecology, Modelling Procedures and Investment Strategies," Economica, London School of Economics and Political Science, vol. 77(305), pages 128-147, January.
    19. B Buraimo & D Forrest & R Simmons, 2009. "Insights for clubs from modelling match attendance in football," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 147-155, February.
    20. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    21. Ruth N. Bolton & Randall G. Chapman, 2008. "Searching For Positive Returns At The Track: A Multinomial Logit Model For Handicapping Horse Races," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 17, pages 151-171, World Scientific Publishing Co. Pte. Ltd..
    22. D F Percy, 2009. "A mathematical analysis of badminton scoring systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 63-71, January.
    23. 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.
    24. David Edelman, 2007. "Adapting support vector machine methods for horserace odds prediction," Annals of Operations Research, Springer, vol. 151(1), pages 325-336, April.
    25. repec:bla:econom:v:54:y:1987:i:215:p:289-98 is not listed on IDEAS
    26. Snyder, Wayne W, 1978. "Horse Racing: Testing the Efficient Markets Model," Journal of Finance, American Finance Association, vol. 33(4), pages 1109-1118, September.
    27. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2009. "Identifying winners of competitive events: A SVM-based classification model for horserace prediction," European Journal of Operational Research, Elsevier, vol. 196(2), pages 569-577, July.
    28. David Paton & Leighton Vaughan Williams, 2005. "Forecasting outcomes in spread betting markets: can bettors use 'quarbs' to beat the book?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 139-154.
    29. Rodney Paul & Andrew Weinbach, 2005. "Market efficiency and NCAA college basketball gambling," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 29(3), pages 403-408, September.
    30. 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.
    31. Greenhough, J & Birch, P.C & Chapman, S.C & Rowlands, G, 2002. "Football goal distributions and extremal statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 615-624.
    32. de Dios Tena, Juan & Forrest, David, 2007. "Within-season dismissal of football coaches: Statistical analysis of causes and consequences," European Journal of Operational Research, Elsevier, vol. 181(1), pages 362-373, August.
    33. 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.
    34. D Dyte & S R Clarke, 2000. "A ratings based Poisson model for World Cup soccer simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(8), pages 993-998, August.
    35. Bruno Deschamps & Olivier Gergaud, 2007. "Efficiency in Betting Markets: Evidence from English Football," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 61-73, February.
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