Detection of multiscale properties of financial market dynamics based on an entropic segmentation method

Abstract

An entropic segmentation algorithm is introduced to combine with recently developed multiscale techniques, which can probe the multiscale properties containing in the financial time series. We propose the three multiscale methods based on the segmentation method and study the multiscale characteristics and structures in depth by comparing the results, original series and the shuffled series which are obtained by the multiscale research techniques: multiscale entropy, multiscale time irreversibility and multiscale detrended fluctuation analysis. The key finding is that three recently developed multiscale techniques are able to extract information from financial time series not contained in traditional time and frequency domain techniques based on the entropic segmentation algorithm. We reveal some interesting knowledge on the aspects of complexity, time irreversibility and correlation properties and get a better understanding about the dynamics of stock markets.