No abstract

No abstract

No abstract

We consider the°ow intensity of economic and¯nancial news taken from a nine-month period of 2015. This data is found to be well approximated by a persistent self-a±ne walk. It is characterized by a Hurst exponent of H > 0:6 over three orders of magnitude in time ranging from minutes to several days. In this paper, we use the Detrended Fluctuation Analysis (DFA) of order 1, Rescaled Range Analysis (R/S) and Fourier Transform Method (FTM) to examine long-range auto-correlation and self-similarity of time series of news°ow intensity. DFA method allowed us to reveal a strong scaling behavior as well as to detect a distinct crossover e®ect. On the other hand, it turns out that for the classic R/S analysis and Fourier transform techniques, the scaling regimes and/or positions of cross-overs are hard to de¯ne.

This paper studies the properties of the Russian stock market by employing the data-driven science and network approaches. The theory of complex networks allows us to build and examine topological network structures of the market with the further identification of relationships between stocks and the analysis of hidden information and market dynamics. In this paper we will present an analysis of structural and topological properties of the Russian stock market using market graph, hierarchical tree, minimum spanning tree approaches. We compare topological properties of the networks constructed for the US and China stock markets with the properties of corresponding networks constructed for the Russian stock market using a dataset spanning over eight years.