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Exploring Decision Makers' Use of Price Information in a Speculative Market

Author

Listed:
  • Johnnie E. V. Johnson

    (Center for Risk Research, School of Management, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom)

  • Owen Jones

    (Department of Mathematics and Statistics, University of Melbourne, Parkville, Melbourne, Victoria 3010, Australia)

  • Leilei Tang

    (Center for Risk Research, School of Management, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom)

Abstract

We explore the extent to which the decisions of participants in a speculative market effectively account for information contained in prices and price movements. The horse race betting market is an ideal environment to explore these issues. A conditional logit model is constructed to determine winning probabilities based on bookmakers' closing prices and the time-indexed movement of prices to the market close. We incorporate a technique for extracting predictors from price (odds) curves using orthogonal polynomials. The results indicate that closing prices do not fully incorporate market price information, particularly information that is less readily discernable by market participants.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormnsc:v:52:y:2006:i:6:p:897-908
    DOI: 10.1287/mnsc.1060.0506
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    References listed on IDEAS

    as
    1. Asch, Peter & Malkiel, Burton G. & Quandt, Richard E., 1982. "Racetrack betting and informed behavior," Journal of Financial Economics, Elsevier, vol. 10(2), pages 187-194, July.
    2. Ron Bird & Michael Mccrae, 2008. "Tests Of The Efficiency Of Racetrack Betting Using Bookmaker Odds," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 59, pages 593-603, World Scientific Publishing Co. Pte. Ltd..
    3. repec:bla:econom:v:52:y:1985:i:27:p:295-304 is not listed on IDEAS
    4. Ali, Mukhtar M, 1977. "Probability and Utility Estimates for Racetrack Bettors," Journal of Political Economy, University of Chicago Press, vol. 85(4), pages 803-815, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ma, Tiejun & Tang, Leilei & McGroarty, Frank & Sung, Ming-Chien & Johnson, Johnnie E. V, 2016. "Time is money: Costing the impact of duration misperception in market prices," European Journal of Operational Research, Elsevier, vol. 255(2), pages 397-410.
    2. 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.
    3. Rose D. Baker & Ian G. McHale, 2013. "Optimal Betting Under Parameter Uncertainty: Improving the Kelly Criterion," Decision Analysis, INFORMS, vol. 10(3), pages 189-199, September.
    4. Tang, Leilei & Thomas, Lyn & Fletcher, Mary & Pan, Jiazhu & Marshall, Andrew, 2014. "Assessing the impact of derived behavior information on customer attrition in the financial service industry," European Journal of Operational Research, Elsevier, vol. 236(2), pages 624-633.
    5. 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.
    6. 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.
    7. Yanchao Du & Hengyu Zhou & Yongbo Yuan & Hong Xue, 2019. "Exploring the Moral Hazard Evolutionary Mechanism for BIM Implementation in an Integrated Project Team," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    8. Taufiq Choudhry & Frank McGroarty & Ke Peng & Shiyun Wang, 2012. "High‐Frequency Exchange‐Rate Prediction With An Artificial Neural Network," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(3), pages 170-178, July.
    9. Sung, Ming-Chien & Johnson, Johnnie E.V. & McDonald, David C.J., 2016. "Informed trading, market efficiency and volatility," Economics Letters, Elsevier, vol. 149(C), pages 56-59.
    10. Costa Sperb, L.F. & Sung, M.-C. & Ma, T. & Johnson, J.E.V., 2022. "Turning the heat on financial decisions: Examining the role temperature plays in the incidence of bias in a time-limited financial market," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1142-1157.
    11. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    12. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    13. Miller, Thomas W. & Rapach, David E., 2013. "An intra-week efficiency analysis of bookie-quoted NFL betting lines in NYC," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 10-23.

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