IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v40y2008i1p111-118.html
   My bibliography  Save this article

Nonlinear modelling of European football scores using support vector machines

Author

Listed:
  • Nikolaos Vlastakis
  • George Dotsis
  • Raphael Markellos

Abstract

This article explores the linear and nonlinear forecastability of European football match scores using IX2 and Asian Handicap odds data from the English Premier league. To this end, we compare the performance of a Poisson count regression to that of a nonparametric Support Vector Machine (SVM) model. Our descriptive analysis of the odds and match outcomes indicates that these variables are strongly interrelated in a nonlinear fashion. An interesting finding is that the size of the Asian Handicap appears to be a significant predictor of both home and away team scores. The modelling results show that while the SVM is only marginally superior on the basis of statistical criteria, it manages to produce out-of-sample forecasts with much higher economic significance.

Suggested Citation

  • Nikolaos Vlastakis & George Dotsis & Raphael Markellos, 2008. "Nonlinear modelling of European football scores using support vector machines," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 111-118.
    • Handle: RePEc:taf:applec:v:40:y:2008:i:1:p:111-118
    DOI: 10.1080/00036840701731546
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840701731546
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036840701731546?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091.
    2. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
    3. Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521883818.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    2. Chia-Hao Chang, 2021. "Construction of a Predictive Model for MLB Matches," Forecasting, MDPI, vol. 3(1), pages 1-11, February.
    3. Xianning Wang & Zhengang Ma & Jiusheng Chen & Jingrong Dong, 2023. "Can Regional Eco-Efficiency Forecast the Changes in Local Public Health: Evidence Based on Statistical Learning in China," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    4. 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.
    5. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos & Verousis, Thanos, 2020. "A conditional fuzzy inference approach in forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 196-216.
    6. Xianning Wang & Zhengang Ma & Jingrong Dong, 2021. "Quantitative Impact Analysis of Climate Change on Residents’ Health Conditions with Improving Eco-Efficiency in China: A Machine Learning Perspective," IJERPH, MDPI, vol. 18(23), pages 1-23, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luca Bagnato & Valerio Potì & Maria Zoia, 2015. "The role of orthogonal polynomials in adjusting hyperpolic secant and logistic distributions to analyse financial asset returns," Statistical Papers, Springer, vol. 56(4), pages 1205-1234, November.
    2. Alberto Humala & Gabriel Rodriguez, 2013. "Some stylized facts of return in the foreign exchange and stock markets in Peru," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 30(2), pages 139-158, May.
    3. Stefanescu, Razvan & Dumitriu, Ramona, 2015. "Conţinutul analizei seriilor de timp financiare [The Essentials of the Analysis of Financial Time Series]," MPRA Paper 67175, University Library of Munich, Germany.
    4. Joe Hirschberg & Jenny Lye, 2021. "Estimating risk premiums for regulated firms when accounting for reference-day variation and high-order moments of return volatility," Environment Systems and Decisions, Springer, vol. 41(3), pages 455-467, September.
    5. De Santis, Paola & Drago, Carlo, 2014. "Asimmetria del rischio sistematico dei titoli immobiliari americani: nuove evidenze econometriche [Systematic Risk Asymmetry of the American Real Estate Securities: Some New Econometric Evidence]," MPRA Paper 59381, University Library of Munich, Germany.
    6. Mircea ASANDULUI, 2012. "On forecasting stock options volatility: evidence from London international financial futures and options exchange," Anale. Seria Stiinte Economice. Timisoara, Faculty of Economics, Tibiscus University in Timisoara, vol. 0, pages 505-511, May.
    7. Kundu, Srikanta & Paul, Amartya, 2022. "Effect of economic policy uncertainty on stock market return and volatility under heterogeneous market characteristics," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 597-612.
    8. Wugan Cai & Jiafeng Pan, 2017. "Stochastic Differential Equation Models for the Price of European CO 2 Emissions Allowances," Sustainability, MDPI, vol. 9(2), pages 1-12, February.
    9. Slim Mseddi & Noureddine Benlagha, 2017. "An Analysis of Spillovers Between Islamic and Conventional Stock Bank Returns: Evidence from the GCC Countries," Multinational Finance Journal, Multinational Finance Journal, vol. 21(2), pages 91-132, June.
    10. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    11. Rodrigo Alfaro & Natán Goldberger, 2012. "Cubrir o no Cubrir: ¿Ese es el Dilema?," Working Papers Central Bank of Chile 662, Central Bank of Chile.
    12. Vilém Novák & Soheyla Mirshahi, 2021. "On the Similarity and Dependence of Time Series," Mathematics, MDPI, vol. 9(5), pages 1-14, March.
    13. Zoia, Maria Grazia & Biffi, Paola & Nicolussi, Federica, 2018. "Value at risk and expected shortfall based on Gram-Charlier-like expansions," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 92-104.
    14. Mircea ASANDULUI, 2012. "A Multi-Horizon Comparison Of Volatility Forecasts: An Application To Stock Options Traded At Euronext Exchange Amsterdam," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 10, pages 179-190, December.
    15. Herwartz, Helmut, 2015. "Are GARCH innovations independent - a long term assessment for the S&P 500," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113109, Verein für Socialpolitik / German Economic Association.
    16. Markellos, Raphael N. & Psychoyios, Dimitris, 2018. "Interest rate volatility and risk management: Evidence from CBOE Treasury options," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 190-202.
    17. Torres-Rojo, Juan Manuel & Bahena-González, Roberto, 2018. "Scale invariant behavior of cropping area losses," Agricultural Systems, Elsevier, vol. 165(C), pages 33-43.
    18. GRITLI, Mohamed Ilyes, 2018. "Quel avenir du dinar tunisien face à l'euro ? Prévision avec le modèle ARIMA [What future of the Tunisian dinar against the euro? Prediction with the ARIMA model]," MPRA Paper 83937, University Library of Munich, Germany.
    19. Jiang, Shi-jie & Nieh, Chien-Chung, 2012. "Dynamics of underwriting profits: Evidence from the U.S. insurance market," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 1-15.
    20. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "Crude oil price behaviour before and after military conflicts and geopolitical events," Energy, Elsevier, vol. 120(C), pages 79-91.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:40:y:2008:i:1:p:111-118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.