Skip to main content

Predicting Stock Market Time Series Using Evolutionary Artificial Neural Networks with Hurst Exponent Input Windows

  • Conference paper
AI 2006: Advances in Artificial Intelligence (AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4304))

Included in the following conference series:

Abstract

Predicting stock market time series is a challenging problem due to their random nature, non-stationarity and noise. In this study, we introduce an enhanced evolutionary artificial neural network (EANN) model to meet this challenge. Here, fractal analyses based on Hurst exponent calculations are used to characterize the time series and to identify appropriate input windows for the EANN. We investigate the efficacy of the model using closing price time series for a suite of stocks listed on the SPI index on the Australian Stock Exchange. The results show that Hurst exponent configured models out-perform basic EANN models in terms of average trading profit found using a simple trading strategy.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abraham, A., Philip, N.S., Saratchandran, P.: Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms. International Journal of Neural, Parallel & Scientific Computations 11(1-2), 143–160 (2003)

    MATH  Google Scholar 

  2. Cajuerio, D.O., Tabak, B.M.: The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient. Physica A 336, 521 (2004)

    Article  MathSciNet  Google Scholar 

  3. Chen, A.S., Leung, M.T., Daouk, H.: Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index. Computers and Operations Research 30, 901–923 (2003)

    Article  MATH  Google Scholar 

  4. Kim, D., et al.: Forecasting time series with genetic fuzzy predictor ensembles. IEEE Trans. Fuzzy Syst. 5, 523–535 (1997)

    Article  Google Scholar 

  5. Dorffner, G.: Neural Networks for Time Series Processing. Neural Network World 6(4), 447–468 (1996)

    Google Scholar 

  6. Hurst, H.E.: Long-term storage of reservoirs: an experimental study. Transactions of the American society of civil engineers 116, 770–799 (1951)

    Google Scholar 

  7. Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural network. In: Proceedings of the International Joint Conference on Neural Networks (1990)

    Google Scholar 

  8. Kong, Y.K., Moon, B.R.: Evolutionary ensemble for Stock Prediction. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 1102–1113. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Peters, E.E.: Fractal market analysis: applying chaos theory to investment and economics. Wiley, New York (1994)

    Google Scholar 

  10. Qian, B., Rasheed, K.: Hurst Exponent and Financial Market Predictability. FEA - Financial Engineering and Applications, 437–443 (2004)

    Google Scholar 

  11. Resta, M.: R/S Approach to Trends Breaks Detection. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3681, Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  12. Ruan, J., Pang, S.L., Luo, W.Q.: The MultiFractal Structure Analysis in the China Stock Market. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 18–21 (2005)

    Google Scholar 

  13. Tekbas, O.H.: Modelling of chaotic time series using a variable length windowing approach. Chaos, Solitons & Fractals 29(2), 277–281 (2006)

    Article  MATH  Google Scholar 

  14. Tino, P., Schittenkopf, C., Dorffner, G.: Financial volatility trading using recurrent neural networks. IEEE-Neural Networks 12, 865–874 (2001)

    Article  Google Scholar 

  15. Yakuwa, F., Dote, Y., Yoneyama, M., Uzurabashi, S.: Novel Time Series Analysis & Prediction of Stock Trading using Fractal Theory and Time Delayed Neural Network. In: IEEE International Conference on Systems, Man and Cybernetics, October 5-8, vol. 1, pp. 134–141 (2003)

    Google Scholar 

  16. Yao, J.T., Tan, C.L., Poh, H.L.: Neural Networks for Technical Analysis: A Study on KLCI. International Journal of Theoretical and Applied Finance 2(2), 221–241 (1999)

    Article  MATH  Google Scholar 

  17. Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE Neural Networks 8(9), 1423–1447 (1999)

    Google Scholar 

  18. Yao, X., Lim, Y.: New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks 8(3), 694–713 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Selvaratnam, S., Kirley, M. (2006). Predicting Stock Market Time Series Using Evolutionary Artificial Neural Networks with Hurst Exponent Input Windows. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_66

Download citation

  • DOI: https://doi.org/10.1007/11941439_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49787-5

  • Online ISBN: 978-3-540-49788-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics