Alternative methods of predicting competitive events: An application in horserace betting markets

International Journal of Forecasting

Volumn 26, Issue 3, 2010, Pages 518-536

  Lessmann, Stefan ^a   Sung, Ming Chien ^b   Johnson, Johnnie E V ^b  

^a UNIVERSITY OF HAMBURG   (Germany)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Classification; Probability forecasting; Random forest; Sports forecasting

Indexed keywords

EID: 77952292198     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2009.12.013     Document Type: Article

References (42)

1. Benter W. Computer based horserace handicapping and wagering systems: A report. Efficiency of racetrack betting markets 1994, 183-198. Academic Press, New York. D.B. Hausch, V.S.Y. Lo, W.T. Ziemba (Eds.).
2. Bolton R.N., Chapman R.G. Searching for positive returns at the track: A multinomial logit model for handicapping horseraces. Management Science 1986, 32(8):1040-1060.
3. Breiman L. Optimal gambling systems for favourable games. Proceedings of the 4th Berkeley symposium on mathematical statistics and probability 1961, 63-68. University California Press, Berkeley. J. Neyman (Ed.).
4. Breiman L. Bagging predictors. Machine Learning 1996, 24(2):123-140.
5. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
6. Breiman L., Friedman J.H., Olshen R., Stone C. Classification and regression trees 1984, Wadsworth, Belmont.
7. Bruce A.C., Johnson J.E.V. Investigating the roots of the favourite-longshot bias: An analysis of supply and demand side agents in parallel betting markets. The Journal of Behavioral Decision Making 2000, 13(4):413-430.
8. Burez J., Van den Poel D. CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications 2007, 32(2):277-288.
9. Caruana R., Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. Proceedings of the 23rd international conference on machine learning 2006, 161-168. ACM, New York. W.W. Cohen, A. Moore (Eds.).
10. Chapman R.G. Still searching for positive returns at the track: Empirical results from 2000 Hong Kong races. Efficiency of racetrack betting markets 1994, 173-181. Academic Press, New York. D.B. Hausch, V.S.Y. Lo, W.T. Ziemba (Eds.).
11. Chapman R.G., Staelin R. Exploiting rank ordered choice set data within the stochastic utility model. Journal of Marketing Research 1982, 19(3):288-301.
12. Chen, C., Liaw, A., and Breiman, L. (2004). Using random forest to learn imbalanced data. Technical Report, 666, Statistics Department, University of California at Berkeley.
13. Coussement K., Van den Poel D. Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications 2008, 34(1):313-327.
14. Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 2006, 7:1-30.
15. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 2000, 40(2):139-157.
16. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2001, Wiley, New York. 2nd ed.
17. Edelman D. Adapting support vector machine methods for horserace odds prediction. Annals of Operations Research 2007, 151(1):325-336.
18. Efron B., Tibshirani R.J. An introduction to the bootstrap 1993, Chapman & Hall, New York.
19. Fama E.F. Efficient capital markets: A review of theory and empirical work. Journal of Finance 1970, 25(2):383-417.
20. Figlewski S. Subjective information and market efficiency in a betting market. Journal of Political Economy 1979, 87(1):75-89.
21. Freund Y., Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Science 1997, 55(1):119-139.
22. Granger C.W.J., Pesaran M.H. Economic and statistical measures of forecast accuracy. Journal of Forecasting 2000, 19(7):537-560.
23. Gu, M. G., Huang, C., and Benter, W. (2003). Multinomial probit models for competitive horse racing. Working paper. Chinese University of Hong Kong.
24. Guo L., Ma Y., Cukic B., Singh H. Robust prediction of fault-proneness by random forests. Proceedings of the 15th international symposium on software reliability engineering 2004, 417-428. IEEE Computer Society, Los Alamitos.
25. Hamza M., Larocque D. An empirical comparison of ensemble methods based on classification trees. Journal of Statistical Computation and Simulation 2005, 75(8):629-643.
26. Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20(8):832-844.
27. Järvelin K., Kekäläinen J. IR evaluation methods for retrieving highly relevant documents. Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval 2000, 41-48. ACM Press, New York. N.J. Belkin, P. Ingwersen, M.-K. Leong (Eds.).
28. Johnson J.E.V., Jones O., Tang L. Exploring decision makers' use of price information in a speculative market. Management Science 2006, 52(6):897-908.
29. Kelly J.L. A new interpretation of information rate. The Bell System Technical Journal 1956, 35:917-926.
30. Lariviere B., Van den Poel D. Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications 2005, 29(2):472-484.
31. Leitch G., Tanner J.E. Economic forecast evaluation: Profit versus the conventional error measures. American Economic Review 1991, 81(3):580-590.
32. Leon, F., Zaharia, M. H., and Gâlea, D. (2004). Performance analysis of categorization algorithms. In Proceedings of the 8th international symposium on automatic control and computer science.
33. Lessmann S., Mues C., Baesens B., Pietsch S. Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Transactions on Software Engineering 2008, 34(4):485-496.
34. Lessmann S., Sung M., Johnson J.E.V. Adapting least-square support vector regression models to forecast the outcome of horseraces. Journal of Prediction Markets 2007, 1(3):169-187.
35. Lessmann S., Sung M., Johnson J.E.V. Identifying winners of competitive events: A SVM-based classification model for horserace prediction. European Journal of Operational Research 2009, 196:569-577.
36. McFadden D. Conditional logit analysis of qualitative choice behavior. Frontiers in econometrics 1974, 105-142. Academic Press, New York. P. Zarembka (Ed.).
37. Sung M., Johnson J.E.V. Comparing the effectiveness of one- and two-step conditional logit models for predicting outcomes in a speculative market. Journal of Prediction Markets 2007, 1(1):43-59.
38. Sung M., Johnson J.E.V. Semi-strong form information efficiency in horserace betting markets. Handbook of sports and lottery markets 2008, North Holland: Elsevier. D. Hausch, W. Ziemba (Eds.).
39. Sung M., Johnson J.E.V., Bruce A.C. Searching for semi-strong form inefficiency in the UK racetrack betting market. Information efficiency in financial and betting markets 2005, 179-192. Cambridge University Press, Cambridge. L. Vaughan Williams (Ed.).
40. Van Gestel T., Suykens J.A.K., Baesens B., Viaene S., Vanthienen J., Dedene G., De Moor B., Vandewalle J. Benchmarking least squares support vector machine classifiers. Machine Learning 2004, 54(1):5-32.
41. Vapnik V.N. The nature of statistical learning theory 1995, Springer, New York.
42. Zar J.H. Biostatistical analysis 2007, Prentice Hall, Upper Saddle River.