Skip to main content
SpringerLink
Log in

Forecasting IBEX-35 moves using support vector machines

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This research aims at examining the application of support vector machines (SVMs) to the task of forecasting the weekly change in the Madrid IBEX-35 stock index. The data cover the period between 10/18/1990 and 10/29/2010. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) decision rules. The SVMs with given values of the RSI and MACD indicators are used in order to determine the best situations to buy or sell the market. The two outputs of the SVM are both the direction of the market and the probability attached to each forecast market move. The best result that it has been achieved is a hit ratio of 100% using the SVM classifier under some chosen risk-aversion parameters. However, these results are obtained analyzing recent periods rather than using all the dataset information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. The software used is MATLAB 7.8.0 (R2009a).

  2. The software tool that we have used is “EasyNN-plus v8.0i” (© 2002–2007 Neural Planner Software).

References

  1. Allen HL, Taylor MP (1990) Charts, noise and fundamentals in the London foreign exchange market. Econ J 100:49–59

    Article  Google Scholar 

  2. Allen HL, Taylor MP (1992) Chartist analysis. In: Newman P, Milgate M, Eatwell J (eds) The new palgrave dictionary of money and finance. Macmillan, London, pp 339–42

  3. Brock W, Lakonishok J, LeBaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Finance 47:1731–1764

    Article  Google Scholar 

  4. Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167

    Article  Google Scholar 

  5. Cao L, Tay F (2001) Financial forecasting using support vector machines. Neural Comput Appl 10:184–192

    Article  MATH  Google Scholar 

  6. Cao L, Tay F (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transac Neural Netw 14:1506–1518

    Article  Google Scholar 

  7. Chong TerenceTai-Leung, Ng Wing-Kam (2008) Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30. Appl Econ Lett 15:1111–1114

    Article  Google Scholar 

  8. Cristianini N, Taylor JS (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New York

    Book  Google Scholar 

  9. Curcio R, Goodhart C, Dominique G, Payne R (1997) Do technical trading rules generate profits? Conclusions from the intra-day foreign exchange market. Int J Finance Econ 2:267–280

    Article  Google Scholar 

  10. De Grauwe P, Decupere D (1992) Psychological Barriers in the Foreign Exchange Market. J Int Comp Econ 1:87–101

    Google Scholar 

  11. Evgeniou T, Pontil M, Poggio T (2000) Regularization networks and support vector machines. Adv Comput Math 13:1–50

    Article  MathSciNet  MATH  Google Scholar 

  12. Fama EF (1965) The behaviour of stock-market prices. J Bus 38:34–105

    Article  Google Scholar 

  13. Fama EF (1970) Efficient capital markets: review of theory and empirical work. J Finance 25:383–423

    Article  Google Scholar 

  14. Huang S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17:721–728

    Article  Google Scholar 

  15. Huang W, Nakamori Y, Wang SY (2005) Forecasting stock market movement direction with support vector machine. Comput Oper Res 32:2513–2522

    Article  MATH  Google Scholar 

  16. Hudson R, Dempsey M, Keasey K (1996) A note on the weak form efficiency of capital markets: the application of simple technical trading rules to UK stock prices—1935 to 1994. J Bank Finance 20:1121–1132

    Article  Google Scholar 

  17. Ince H, Trafalis T (2008) Short term forecasting with support vector machines and application to stock price prediction. Int J General Syst 37:677–687

    Article  MathSciNet  MATH  Google Scholar 

  18. Kim K (2003) Financial time series forecasting using support vector machines. Neurocomputing 55:307–319

    Article  Google Scholar 

  19. Kwon KY, Kish RJ (2002) Technical trading strategies and return predictability: NYSE. Appl Financ Econ 12:639–653

    Article  Google Scholar 

  20. Lindemann A, Dunis CL, Lisboa P (2005) Level estimation, classification and probability distribution architectures for trading the EUR/USD exchange rate. Neural Comput Appl 14:256–271

    Article  Google Scholar 

  21. Menkhoff L, Taylor MP (2007) The obstinate passion of foreign exchange professionals: technical analysis. J Econ Lit 45:936–972

    Article  Google Scholar 

  22. Mills TC (1997) Technical analysis and the London Stock Exchange: Testing trading rules using the FT30. Int J Finance Econ 2:319–331

    Article  Google Scholar 

  23. Murphy JJ (1999) Technical analysis of the financial markets. Institute of Finance, New York

    Google Scholar 

  24. Neely CJ, Weller PA (2003) Intraday technical trading in the foreign exchange market. J Int Money Finance 22:223–237

    Article  Google Scholar 

  25. Neftçi SN (1991) Naïve trading rules in financial markets and Wiener-Kolmogorov prediction theory: A study of ‘technical analysis. J Bus 64:549–571

    Article  Google Scholar 

  26. Okunev J, White D (2003) Do momentum-based strategies still work in foreign currency markets? J Financ Quant Anal 38:425–447

    Article  Google Scholar 

  27. Osler CL (2000) Support for resistance: technical analysis and intraday exchange rates. Fed Reverse Bank NY Econ Policy Rev 6:53–68

    Google Scholar 

  28. Osler CL (2003) Currency orders and exchange rate dynamics: an explanation for the predictive success of technical analysis. J Finance 58:1791–1819

    Article  Google Scholar 

  29. Osler CL (2005) Stop-loss orders and price cascades in currency markets. J Int Money Finance 24:219–241

    Article  Google Scholar 

  30. Rosillo R, De la Fuente D, Brugos JAL (2013) Technical analysis and the Spanish stock Exchange: testing the RSI, MACD, Momentum and Stochastic rules using Spanish market companies. Appl Econ 45:1541–1550

    Article  Google Scholar 

  31. Rumelhart DE, McClelland J (1986) Parallel distributed processing. MIT Press, Cambridge

    Google Scholar 

  32. Sarle WS (1995) Stopped training and other remedies for overfitting. In: Proceedings of the 27th symposium on the interface of computing science and statistics, pp 352–360

  33. Tay F, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29:309–317

    Article  Google Scholar 

  34. Tay F, Cao L (2001) A comparative study of saliency analysis and genetic algorithm for features selection in support vector machines. Intell Data Anal 5:191–209

    MATH  Google Scholar 

  35. Tay F, Cao L (2001) Improved financial time series forecasting by combining support vector machines with self-organizing feature map. Intell Data Anal 5:339–354

    MATH  Google Scholar 

  36. Tay F, Cao L (2002) Modifies support vector machines in financial time series forecasting. Neurocomputing 48:847–861

    Article  MATH  Google Scholar 

  37. Taylor MP, Allen HL (1992) The use of technical analysis in the foreign exchange market. J Int Money Finance 11:304–314

    Article  Google Scholar 

  38. Treynor JL, Ferguson R (1985) In defense of technical analysis. J Finance 3:757–773

    Article  Google Scholar 

  39. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  40. Vapnik VN (1999) An overview of statistical learning theory. IEEE Transac Neural Netw 10:988–999

    Article  Google Scholar 

  41. Welles Wilder J Jr (1978) New concepts in technical trading systems. Hunter Publishing Company, Greensboro

    Google Scholar 

  42. Xu X, Zhou C, Wang Z (2009) Credit scoring algorithm based on link analysis ranking with support vector machine. Expert Syst Appl 36:2625–2632

    Article  Google Scholar 

Download references

Acknowledgments

Financial support given by the Government of the Principality of Asturias is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Liverpool Business School, Liverpool, UK

    Christian L. Dunis

  2. CIBEF, Centre for International Banking, Economics and Finance, John Moores University, John Foster Building, 98 Mount Pleasant, Liverpool, L3 5UZ, UK

    Christian L. Dunis & Rafael Rosillo

  3. Business Management, University of Oviedo, Oviedo, Spain

    Rafael Rosillo, David de la Fuente & Raúl Pino

Authors
  1. Christian L. Dunis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael Rosillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David de la Fuente
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raúl Pino
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Rosillo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dunis, C.L., Rosillo, R., de la Fuente, D. et al. Forecasting IBEX-35 moves using support vector machines. Neural Comput & Applic 23, 229–236 (2013). https://doi.org/10.1007/s00521-012-0821-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-0821-9

Keywords

Search

Navigation