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Comparing the Effectiveness of One- and Two-step Conditional Logit Models for Predicting Outcomes in a Speculative Market

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  • Ming-Chien Sung

    (Centre for Risk Research, School of Management, University of Southampton)

  • Johnnie E.V. Johnson

    (Centre for Risk Research, School of Management, University of Southampton)

Abstract

This paper compares two approaches to predicting outcomes in a speculative market, the horserace betting market. In particular, the nature of one- and two-step conditional logit procedures involving a process for exploding the choice set are outlined, their strengths and weaknesses are compared and their relative effectiveness is evaluated by predicting winning probabilities for horse races at a UK racetrack. The models incorporate variables which are widely recognised as having predictive power and which should therefore be effectively discounted in market odds. Despite this handicap, both approaches produce probability estimates which can be used to earn positive returns, but the two-step approach yields substantially higher profits.

Suggested Citation

  • Ming-Chien Sung & Johnnie E.V. Johnson, 2007. "Comparing the Effectiveness of One- and Two-step Conditional Logit Models for Predicting Outcomes in a Speculative Market," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 43-59, February.
    • Handle: RePEc:buc:jpredm:v:1:y:2007:i:1:p:43-59
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    Citations

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    Cited by:

    1. 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.
    2. Green, Lawrence & Sung, Ming-Chien & Ma, Tiejun & Johnson, Johnnie E. V., 2019. "To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market," European Journal of Operational Research, Elsevier, vol. 278(1), pages 226-239.
    3. 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.
    4. 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.
    5. 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.
    6. Sperb, Luis Felipe Costa & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2019. "Keeping a weather eye on prediction markets: The influence of environmental conditions on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 35(1), pages 321-335.

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