Neural Computing and Applications

, Volume 25, Issue 2, pp 321–332 | Cite as

The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500

  • Rafael Rosillo
  • Javier Giner
  • David de la Fuente
Original Article

Abstract

The aim of this research is to analyse the effectiveness of the Chicago Board Options Exchange Market Volatility Index (VIX) when used with Support Vector Machines (SVMs) in order to forecast the weekly change in the S&P 500 index. The data provided cover the period between 3 January 2000 and 30 December 2011. 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 Relative Strength Index, Moving Average Convergence Divergence, VIX and the daily return of the S&P 500. The SVM identifies the best situations in which to buy or sell in the market. The two outputs of the SVM are the movement of the market and the degree of set membership. The obtained results show that SVM using VIX produce better results than the Buy and Hold strategy or SVM without VIX. The influence of VIX in the trading system is particularly significant when bearish periods appear. Moreover, the SVM allows the reduction in the Maximum Drawdown and the annualised standard deviation.

Keywords

Support Vector Machines Quantitative trading strategies VIX RSI MACD Machine learning 

References

  1. 1.
    Allen HL, Taylor MP (1990) Charts, noise and fundamentals in the London foreign exchange market. Econ J 100:49–59CrossRefGoogle Scholar
  2. 2.
    Andersen TG, Bollerslev T (1998) Answering the skeptics: yes standard volatility models do provide accurate forecasts. Int Econ Rev 39:885–905CrossRefGoogle Scholar
  3. 3.
    Blair BJ, Poon SH, Taylor SJ (2001) Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns. J Econom 105:5–26CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econom 31:307–327CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Brock W, Lakonishok J, LeBaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Finance 47:1731–1764CrossRefGoogle Scholar
  6. 6.
    Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167CrossRefGoogle Scholar
  7. 7.
    Canu S, Grandvalet Y, Guigue V, Rakotomamonjy A (2005) SVM and Kernel methods Matlab toolbox, perception systèmes et information. INSA de Rouen, RouenGoogle Scholar
  8. 8.
    Cao L, Tay F (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Networks 14:1506–1518CrossRefGoogle Scholar
  9. 9.
    Chapelle O, Haner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Networks 10(5):1055–1064CrossRefGoogle Scholar
  10. 10.
    Chong TT-L, Ng W-K (2008) Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30. Appl Econ Lett 15:1111–1114CrossRefGoogle Scholar
  11. 11.
    Cristianini N, Taylor JS (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New YorkCrossRefGoogle Scholar
  12. 12.
    Dunis CL, Rosillo R, De la Fuente D, Pino R (2013) Forecasting IBEX-35 moves using support vector machines. Neural Comput Appl 23(1):229–236. doi:10.1007/s00521-012-0821-9 CrossRefGoogle Scholar
  13. 13.
    Dunis C, Likothanassis S, Karathanasopoulos A, Sermpinis G, Theofilatos K (2013b) A hybrid genetic algorithm-support vector machine approach in the task of forecasting and trading the ASE 20. J Asset Manag 1–20. doi:10.1057/jam.2013.2
  14. 14.
    Evgeniou T, Pontil M, Poggio T (2000) Regularization networks and support vector machines. Adv Comput Math 13:1–50CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Hajizadeh E, Seifi A, Zarandi MNF, Turksen IB (2012) A hybrid modeling approach for forecasting the volatility of S&P 500 index return. Expert Syst Appl 39(1):431–436. doi:10.1016/j.eswa.2011.07.033 CrossRefGoogle Scholar
  16. 16.
    Huang S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17:721–728CrossRefGoogle Scholar
  17. 17.
    Huang W, Nakamori Y, Wang SY (2005) Forecasting stock market movement direction with support vector machine. Comput Oper Res 32:2513–2522CrossRefMATHGoogle Scholar
  18. 18.
    Kim K (2003) Financial time series forecasting using support vector machines. Neurocomputing 55:307–319CrossRefGoogle Scholar
  19. 19.
    Kwon KY, Kish RJ (2002) Technical trading strategies and return predictability: NYSE. Appl Financ Econ 12:639–653CrossRefGoogle Scholar
  20. 20.
    Lee M-C (2009) Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst Appl 36(8):10896–10904CrossRefGoogle Scholar
  21. 21.
    Menkhoff L, Taylor MP (2007) The obstinate passion of foreign exchange professionals: technical analysis. J Econ Lit 45:936–972CrossRefGoogle Scholar
  22. 22.
    Mills TC (1997) Technical analysis and the London stock exchange: testing trading rules using the FT30. Int J Finance Econ 2:319–331CrossRefGoogle Scholar
  23. 23.
    Murphy JJ (1999) Technical analysis of the financial markets. Institute of Finance, New YorkGoogle Scholar
  24. 24.
    Perez-Cruz F, Alfonso-Rodiguez JA, Giner J (2003) Estimating GARCH models using support vector machines. Quant Finance 3(3):163–172CrossRefMathSciNetGoogle Scholar
  25. 25.
    Rodriguez-Gonzalez A, Garcia-Crespo A, Colomo-Palacios R et al (2011) CAST: using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Expert Syst Appl 38(9):11489–11500CrossRefGoogle Scholar
  26. 26.
    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 45:1541–1550CrossRefGoogle Scholar
  27. 27.
    Szado E (2009) VIX futures and options: a case study of portfolio diversification during the 2008 financial crisis. J Altern Invest 12(2):68–85, 18pGoogle Scholar
  28. 28.
    Taylor MP, Allen HL (1992) The use of technical analysis in the foreign exchange market. J Int Money Finance 11:304–314CrossRefGoogle Scholar
  29. 29.
    Vapnik VN (1998) Statistical learning theory. Wiley, New YorkMATHGoogle Scholar
  30. 30.
    Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999CrossRefGoogle Scholar
  31. 31.
    Welles Wilder J Jr (1978) New concepts in technical trading systems. Hunter Publishing Company, Greensboro, NCGoogle Scholar
  32. 32.
    Whaley R (2009) Understanding the VIX. J Portf Manag 35:98–105CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Rafael Rosillo
    • 1
  • Javier Giner
    • 2
  • David de la Fuente
    • 1
  1. 1.Business Management DepartmentUniversity of OviedoOviedoSpain
  2. 2.Finances and Economics DepartmentUniversity of La LagunaLa LagunaSpain

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