Identifying winners of competitive events: A SVM-based classification model for horserace prediction

被引：24

作者：

Lessmann, Stefan ^{[1
]}

Sung, Ming-Chien ^{[2
]}

Johnson, Johnnie E. V. ^{[2
]}

机构：

[1] Univ Hamburg, Inst Informat Syst, D-20146 Hamburg, Germany

[2] Univ Southampton, Ctr Risk Res, Sch Management, Southampton SO17 1BJ, Hants, England

来源：

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2009年 / 196卷 / 02期

关键词：

Forecasting; Decision analysis; Finance; Horseracing; Support vector machines; SUPPORT VECTOR MACHINES; INFORMATION; CHOICE; TUTORIAL;

D O I：

10.1016/j.ejor.2008.03.018

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

The 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. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：569 / 577

页数：9

共 50 条

[1] An SVM-Based Classification Model for Migration Prediction of Beijing
Zhang, Lan
Luo, Lina
Hu, Lei
Sun, Maohua
ENGINEERING LETTERS, 2020, 28 (04) : 1023 - 1030
[2] Classification of acoustic events using SVM-based clustering schemes
Temko, A
Nadeu, C
PATTERN RECOGNITION, 2006, 39 (04) : 682 - 694
[3] Multiple model classification using SVM-based approach
Ma, YQ
Cherkassky, V
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 1581 - 1586
[4] CTKPred: an SVM-based method for the prediction and classification of the cytokine superfamily
Huang, N
Chen, H
Sun, ZR
PROTEIN ENGINEERING DESIGN & SELECTION, 2005, 18 (08): : 365 - 368
[5] Sentiment Mining Using SVM-Based Hybrid Classification Model
Vinodhini, G.
Chandrasekaran, R. M.
COMPUTATIONAL INTELLIGENCE, CYBER SECURITY AND COMPUTATIONAL MODELS, 2014, 246 : 155 - 162
[6] SVM-Based Classification of Moving Objects
Li, Zhanchuang
Jiang, Jianmin
Xiao, Guoqiang
MULTIMEDIA, COMPUTER GRAPHICS AND BROADCASTING, PROCEEDINGS, 2009, 60 : 37 - 45
[7] SVM-based audio scene classification
Jiang, HC
Bai, JM
Zhang, SW
Xu, B
PROCEEDINGS OF THE 2005 IEEE INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING (IEEE NLP-KE'05), 2005, : 131 - 136
[8] Research of SVM-based document classification
Zhang, ZhenNan
Xu, Qian
Cui, Junbo
Pu, Duan
Information, Management and Algorithms, Vol II, 2007, : 260 - 263
[9] A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection
Nguyen, Bach Hoai
Xue, Bing
Zhang, Mengjie
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2024, 28 (01) : 2 - 16
[10] An optimal svm-based text classification algorithm
Wang, Zi-Qiang
Sun, Xia
Zhang, De-Xian
Li, Xin
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 1378 - +

← 1 2 3 4 5 →