Advertisement

Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading

  • 339 Accesses

  • 1 Citations

Abstract

In this paper, a Nonlinear AutoRegressive network with eXogenous inputs and a support vector machine are proposed for algorithmic trading by predicting the future value of financial time series. These architectures are capable of modeling and predicting vector autoregressive VAR(p) time series. In order to avoid overfitting, the input is pre-processed by independent component analysis to filter out the most noise like component. In this way, the accuracy of the prediction and the trading performance is increased. The proposed algorithms have a small number of free parameters which makes fast learning and trading possible. The method is not only tested on single asset price series, but also on predicting the value of mean reverting portfolios obtained by maximizing the predictability parameter of VAR(1) processes. The tests were first performed on artificially generated data and then on real data selected from exchange traded fund time series including bid–ask spread. In both cases the proposed method could achieve positive returns.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. Aalto University, D.o.C.S. (2015). The fastica package for matlab. http://research.ics.aalto.fi/ica/fastica/.

  2. Alpaydin, E. (2010). Introduction to machine learning (2nd ed.). Cambridge: The MIT Press.

  3. Back, A. D., & Weigend, A. S. (1997). A first application of independent component analysis to extracting structure from stock returns. International Journal of Neural Systems, 08(04), 473–484.

  4. Back, A. D., & Weigend, A. S. (1998). Decision technologies for computational finance. In Proceedings of the fifth international conference computational finance, chapter discovering structure in finance using independent component analysis (pp. 309–322). Boston, MA: Springer.

  5. Chan, E. (2013). Algorithmic trading: Winning strategies and their rationale (1st ed.). Hoboken, NJ: Wiley Publishing.

  6. Chang, C. C. & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:1–27:27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

  7. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. International Journal of Theoretical and Applied Finance, 08(01), 13–58.

  8. Cheung, Y., & Xu, L. (2001). Independent component ordering in ica time series analysis. Neurocomputing, 41, 145–152.

  9. d’Aspremont, A. (2007). Identifying small mean reverting portfolios. CoRR abs/0708.3048.

  10. Dreyfus, G. (2005). Neural networks—methodology and applications. Berlin: Springer. doi:10.1007/3-540-28847-3.

  11. Fogarasi, N., & Levendovszky, J. (2013). Sparse, mean reverting portfolio selection using simulated annealing. Algorithmic Finance, 2(3–4), 197–211.

  12. Gestel, T. V., Suykens, J. A. K., Baestaens, D. E., Lambrechts, A., Lanckriet, G., Vandaele, B., et al. (2001). Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12(4), 809–821.

  13. Hyvärinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Network, 13(4–5), 411–430.

  14. Itô, K. (1944). Stochastic integral. Proceedings of the Imperial Academy, 20(8), 519–524. doi:10.3792/pia/1195572786.

  15. Johnson, R. A., & Wichern, D. W. (Eds.). (1988). Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice-Hall Inc.

  16. Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–236. (Financial applications, part II).

  17. Kim, K. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1–2), 307–319. (Support vector machines).

  18. Kumar, P. N., Seshadri, G. R., Hariharan, A., Mohandas, V. P. & Balasubramanian, P. (2011). Technology systems and management. In First international conference, ICTSM 2011, Mumbai, India, February 25–27, 2011. Selected papers, chapter financial market prediction using feed forward neural network, (pp. 77–84). Berlin, Heidelberg: Springer.

  19. Lahmiri, S. (2011). A comparison of pnn and svm for stock market trend prediction using economic and technical information. International Journal of Computer Applications, 29(3), 24–30.

  20. Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125.

  21. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.

  22. Natarajan, B. K. (1995). Sparse approximate solutions to linear systems. SIAM Journal on Computing, 24(2), 227–234.

  23. Saad, E. W., Prokhorov, D. V., & Wunsch, D. C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks, 9(6), 1456–1470. doi:10.1109/72.728395.

  24. Sipos, I. R., & Levendovszky, J. (2013). Optimizing sparse mean reverting portfolios. Algorithmic Finance, 2(2), 127–139.

  25. Tsay, R. S. (2005). Analysis of financial time series. Wiley series in probability and statistics. Hoboken, NJ: Wiley-Interscience.

  26. Uhlenbeck, G. E., & Ornstein, L. S. (1930). On the theory of the brownian motion. Physical Review, 36, 823–841.

  27. Vapnik, V. N. (1995). The nature of statistical learning theory. New York, NY: Springer.

  28. Wen, Q., Yang, Z., Song, Y., & Jia, P. (2010). Automatic stock decision support system based on box theory and SVM algorithm. Expert Systems with Applications, 37(2), 1015–1022.

  29. Wilamowski, B. M., & Irwin, J. D. (2011). Intelligent systems (2nd ed.). Boca Raton, FL: CRC Press Inc.

  30. Yahoo (2015). Yahoo! finance. https://finance.yahoo.com.

Download references

Author information

Correspondence to Attila Ceffer.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ceffer, A., Levendovszky, J. & Fogarasi, N. Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading. Comput Econ 54, 281–303 (2019) doi:10.1007/s10614-017-9719-z

Download citation

Keywords

  • Algorithmic trading
  • Financial time series
  • Neural network
  • Support vector machine
  • Independent component analysis
  • Mean reverting portfolio