Identifying winners of competitive events: A SVM-based classification model for horserace prediction
AbstractThe aim of much horserace modelling is to appraise the informational efficiency of betting markets. The prevailing approach involves forecasting the runners' finish positions by means of discrete or continuous response regression models. However, theoretical considerations and empirical evidence suggest that the information contained within finish positions might be unreliable, especially among minor placings. To alleviate this problem, a classification-based modelling paradigm is proposed which relies only on data distinguishing winners and losers. To assess its effectiveness, an empirical experiment is conducted using data from a UK racetrack. The results demonstrate that the classification-based model compares favourably with state-of-the-art alternatives and confirm the reservations of relying on rank ordered finishing data. Simulations are conducted to further explore the origin of the model's success by evaluating the marginal contribution of its constituent parts.
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 196 (2009)
Issue (Month): 2 (July)
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Forecasting Decision analysis Finance Horseracing Support vector machines;
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- 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, 04.
- Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
- Schnytzer, Adi & Shilony, Yuval, 1995. "Inside Information in a Betting Market," Economic Journal, Royal Economic Society, vol. 105(431), pages 963-71, July.
- Vaughan Williams, Leighton, 1999. "Information Efficiency in Betting Markets: A Survey," Bulletin of Economic Research, Wiley Blackwell, vol. 51(1), pages 1-30, January.
- Ruth N. Bolton & Randall G. Chapman, 1986. "Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Races," Management Science, INFORMS, vol. 32(8), pages 1040-1060, August.
- 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.
- 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.
- Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
- Raymond D. Sauer, 1998. "The Economics of Wagering Markets," Journal of Economic Literature, American Economic Association, vol. 36(4), pages 2021-2064, December.
- 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.
- Watson, Peter L. & Westin, Richard B., 1975. "Transferability of disaggregate mode choice models," Regional Science and Urban Economics, Elsevier, vol. 5(2), pages 227-249, May.
- Law, David & Peel, David A, 2002. "Insider Trading, Herding Behaviour and Market Plungers in the British Horse-Race Betting Market," Economica, London School of Economics and Political Science, vol. 69(274), pages 327-38, May.
- 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.
- Ozer, Muammer, 2011. "Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions," European Journal of Operational Research, Elsevier, vol. 211(2), pages 359-369, June.
- 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.
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