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

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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.

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Notes

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

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Acknowledgments

Financial support given by the Government of the Principality of Asturias is gratefully acknowledged. The authors would like to thank the reviewers for their comments, which have greatly contributed to improving our paper. The contribution from the native speaker is also greatly appreciated. Any remaining shortcomings are, of course, our exclusive responsibility.

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Authors and Affiliations

  1. Business Management Department, University of Oviedo, Oviedo, Spain

    Rafael Rosillo & David de la Fuente

  2. Finances and Economics Department, University of La Laguna, La Laguna, Spain

    Javier Giner

Authors
  1. Rafael Rosillo
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  2. Javier Giner
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  3. David de la Fuente
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Corresponding author

Correspondence to Rafael Rosillo.

Appendix

Appendix

Annualised return:

$$R^{\text{A}} = 250*\frac{1}{n}\sum\limits_{i = 1}^{n} {r_{i} }$$
(12)

Standard deviation:

$$\sigma^{\text{A}} = \sqrt {250} \sqrt {\frac{1}{n - 1}\sum\limits_{i = 1}^{n} {\left( {r_{i} - \overline{r} } \right)^{2} } }$$
(13)

Sharpe ratio:

$${\text{SR}} = \frac{{R^{\text{A}} }}{{\sigma^{\text{A}} }}$$
(14)

Maximum Drawdown calculated in S&P 500 points:

$${\text{MDD}} = \mathop {\hbox{min} }\limits_{t = 1, \ldots ,n} \left( {F_{t} - \mathop {\hbox{max} }\limits_{\tau = 1, \ldots ,t} (F_{\tau } )} \right)$$
(15)

where F t is the accumulated fund with each different strategy.

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Rosillo, R., Giner, J. & de la Fuente, D. The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500. Neural Comput & Applic 25, 321–332 (2014). https://doi.org/10.1007/s00521-013-1487-7

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