Entropy-Based Indicator for Predicting Stock Price Trend Reversal

  • Virgilijus Sakalauskas
  • Dalia Kriksciuniene
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 97)


Predicting changes of stock price long term trend is an important problem for validating strategies of investment to the financial instruments. In this article we applied the approach of analysis of information efficiency and long term correlation memory in order to distinguish short term changes in trend, which can be evaluated as informational ‘nervousness’, from the reversal point of long term trend of the financial time series. By integrating two econometrical measures of information efficiency - Shannon’s entropy (SH) and local Hurst exponent (HE) - we designed aggregated entropy-based (EB) indicator and explored its ability to forecast the turning point of trend of the financial time series and to calibrate the stock market trading strategy.


Shannon entropy informational efficiency financial market local Hurst exponent stock price 


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  1. 1.
    Qian, B., Rasheed, K.: Hurst exponent and financial market predictability. In: IASTED Conference on Financial Engineering and Applications, pp. 203–209 (2004)Google Scholar
  2. 2.
    Bassler, K., Gunaratne, G., McCauley, J.: Markov processes, Hurst exponents, and nonlinear diffusion equations: With application to finance. Physica A 369(2), 343–353 (2006)CrossRefGoogle Scholar
  3. 3.
    McCauley, J., Gunaratne, G., Bassler, K.: Hurst Exponents, Markov Processes, and Fractional Brownian Motion. Physica A 379(1), 1–9 (2007)CrossRefGoogle Scholar
  4. 4.
    Cajueiro, D., Tabak, B.: Ranking Efficiency for Emerging Markets. Chaos, Solutions and Fractals 22, 349–352 (2004)CrossRefGoogle Scholar
  5. 5.
    Cajueiro, D., Tabak, B.: Ranking Efficiency for Emerging Markets II. Chaos, Solutions and Fractals 23, 671–675 (2005)CrossRefGoogle Scholar
  6. 6.
    Barunik, J., Kristoufek, L.: On Hurst exponent estimation under heavy-tailed distributions. Physica A 389(18), 3844–3855 (2010)CrossRefGoogle Scholar
  7. 7.
    Danilenko, S.: Hurst Analysis of Baltic Sector Indices. In: Applied Stochastic Models and Data Analysis (ASMDA 2009), Vilnius, pp. 329–333 (2009)Google Scholar
  8. 8.
    Grech, D., Mazur, Z.: Can one make any crash prediction in finance using the local Hurst exponent idea? Physica A: Statistical Mechanics and its Applications 336, 133–145 (2004)CrossRefGoogle Scholar
  9. 9.
    Shannon, C.: A Mathematical Theory of Communication. Bell System Technical Journal 27, 79–423, 623–656 (1948)CrossRefGoogle Scholar
  10. 10.
    Cover, T., Thomas, J.: Elements of Information Theory. Wiley ed. (1991)Google Scholar
  11. 11.
    Sakalauskas, V., Kriksciuniene, D.: Evolution in information efficiency in emerging markets. In: Advances in Intelligent and Soft Computing: Soft Computing Models in Industrial and Environmental Applications: 6th IC SOCO 2011, pp. 367–377. Springer, Heidelberg (2011)Google Scholar
  12. 12.
    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
  13. 13.
    Peng, C., Buldyrev, S., Havlin, S., Simons, M., Stanley, H., Goldberger, A.: Mosaic organization of dna nucleotides. Physical Review E 49(2) (1994)Google Scholar
  14. 14.
  15. 15.
    Risso, W.A.: The Informational Efficiency and the Financial Crashes. Research in International Business and Finance 22, 396–408 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Virgilijus Sakalauskas
    • 1
  • Dalia Kriksciuniene
    • 1
  1. 1.Department of InformaticsVilnius UniversityKaunasLithuania

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