Measuring long-range correlations in news flow intensity time series

Sergei Sidorov, Alexey Faizliev () and Vladimir Balash ()
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Sergei Sidorov: Saratov State University, Astrakhanskaya, 83 Saratov 410012, Russian Federation
Alexey Faizliev: Saratov State University, Astrakhanskaya, 83 Saratov 410012, Russian Federation
Vladimir Balash: Saratov State University, Astrakhanskaya, 83 Saratov 410012, Russian Federation
Authors registered in the RePEc Author Service: Владимир Алексеевич Балаш

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 08, 1-13

Abstract: We consider the flow intensity of economic and financial news taken from a nine-month period of 2015. This data is found to be well approximated by a persistent self-affine 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 flow intensity. DFA method allowed us to reveal a strong scaling behavior as well as to detect a distinct crossover effect. 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 define.

Keywords: Long-range correlation; detranded fluctuation analysis; time series; auto-correlation (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117501030

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