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Measuring long-range correlations in news flow intensity time series

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sergei Sidorov & Alexey Faizliev & Vladimir Balash, 2017. "Measuring long-range correlations in news flow intensity time series," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(08), pages 1-13, August.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:08:n:s0129183117501030
    DOI: 10.1142/S0129183117501030
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