Entropy-Based Indicator for Predicting Stock Price Trend Reversal

摘要

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.