Entropy-Based Indicator for Predicting Stock Price Trend Reversal
作者： Virgilijus Sakalauskas , Dalia Kriksciuniene
DOI: 10.1007/978-3-642-25370-6_9
关键词:
摘要: Predicting changes of stock price long term trend is an important problem for validating strategies investment to the financial instruments. In this article we applied approach analysis information efficiency and correlation memory in order distinguish short trend, which can be evaluated as informational ‘nervousness’, from reversal point time series. By integrating two econometrical measures - Shannon’s entropy (SH) local Hurst exponent (HE) designed aggregated entropy-based (EB) indicator explored its ability forecast turning series calibrate market trading strategy.
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