A long memory property of economic and financial news flows

Abstract

One of the tools for examining the processes and time series with self-similarity is the long-range correlation exponent (the Hurst exponent). Many methods have been developed for estimating the long-range correlation exponent using experimental time series over the last years. In this paper we estimate the Hurst exponent parameter obtained by different methods using news analytics time series. We exploit the most commonly used methods for estimating the Hurst exponents: fluctuation analysis, the detrended fluctuation analysis and the detrending moving average analysis. Following some previous studies, empirical results show the presence of long-range correlations for the time series of news intensity data. In particular, the paper shows that the behaviour of long range dependence for time series of news intensity in the recent period from 1 January 2015 to 22 September 2015 did not change in comparison to the period from 1 September 2010 to 29 October 2010. Moreover, the change of the news analytics provider and the consideration of more recent data did not significantly affect estimates of the Hurst exponent. The results show that the self-similarity property is a stable characteristic of the news flow of information which serves the financial industry and stock markets.