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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)

Abstract

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.

Keywords

Shannon entropy informational efficiency financial market local Hurst exponent stock price 

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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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