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Impact of Hurst Exponent on Indicator Based Trading Strategies

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 289)

Abstract

Appearance of chaotic behavior that covers stock market trading, creates a lot of doubts of its analysis and predictions. However the chaos theory is applicable in a lot of studies of this kind. Stochastic and nonlinear systems can be viewed as deterministic through the prism of chaos theory. This article describes the experiment of analysis of stock market titles by Hurst exponent to find out the randomness or long range memory in the generating of prices. The other question is to figure out the impact of application of the Hurst exponent in two simple indicators like RSI and CCI. Since the usage of the evolution algorithm in stock market prediction and for optimization input parameters is very common it is used in this article too.

Keywords

chaos Hurst market RSI CCI evolution 

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References

  1. 1.
    Zelinka, I., Chadli, M., Davendra, D., Senkerik, R., Pluhacek, M., Lampinen, J.: Hidden Periodicity - Chaos Dependance on Numerical Precision. In: Zelinka, I., Chen, G., Rössler, O.E., Snasel, V., Abraham, A. (eds.) Nostradamus 2013: Prediction, Model. & Analysis. AISC, vol. 210, pp. 47–59. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Dostál, P.: Pokročilé metódy rozhodování v podnikatelství a veřejné správě. Akademické nakladatelství (2012)Google Scholar
  3. 3.
    He, L.-Y., Qian, W.-B.: A Monte Carlo simulation to the performance of the R/S and V/S methods-Statistical revisit and real world application, Beijing 100083, China (2012)Google Scholar
  4. 4.
    Lo, A.W.: Long term memory in stock market prices. Econometrica 59, 1279–1313 (1991)CrossRefMATHGoogle Scholar
  5. 5.
    Lo, A.W., Mackinlay, A.C.: A Non-Random Walk Down Wall Street. Princeton University Press (1996)Google Scholar
  6. 6.
    Giraitis, L., Kokoszka, P., Leipus, R., Teyssiere, G.: Rescaled variance and related tests for long memory in volatility and levels. Journal of Econometrics 112, 265–294 (2003)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Pluchino, A., Rapisarda, A., Tsallis, C.: Noise, synchrony, and correlations at the edge of chaos. Physical Review E 87(2) (2013), doi:10.1103/PhysRevE.87.022910Google Scholar
  8. 8.
    He, L.-Y., Fan, Y., Wei, Y.-M.: The empirical analysis for fractal features and long-run memory mechanism in petroleum pricing systems. International Journal of Global Energy Issues 27(4), 492–502 (2007)CrossRefGoogle Scholar
  9. 9.
    Ke, J., Chen, Y.: Modeling and Simulation of the Artificial Stock Market Trading System. Natural Sciences Publishing Cor. (2013)Google Scholar
  10. 10.
    Bodas-Sagi, D.J., Fernández-Blanco, P., Hidalgo, J.I., Soltero-Domingo, F.J.: A parallel evolutionary algorithm for technical market indicators optimization. Springer Science+Business Media Dordrecht (2012)Google Scholar
  11. 11.
    Potvina, J.-Y., Sorianoa, P., Vall, M.: Generating trading rules on the stock markets with genetic programming. Computers & Operations Research 31, 1033–1047 (2004)CrossRefGoogle Scholar
  12. 12.
    Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics SMC-16(1) (January/February 1986)Google Scholar
  13. 13.
    Davies, D.W.: Defining The Commodity Channel IndexGoogle Scholar
  14. 14.
    Deng, S., Sakurai, A.: Foreign Exchange Trading Rules using a Single Technical Indicator from Multiple Timeframes. In: 2013 27th International Conference on Advanced Information Networking and Applications Workshops (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.VSB-Technical University of OstravaOstrava-PorubaCzech Republic

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