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Information-theoretic approach to lead-lag effect on financial markets

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  • Paweł Fiedor

Abstract

Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both types of analysis are concentrated mostly on Pearson’s correlation coefficient and consequently intraday lead-lag relationships (where one of the variables in a pair is time-lagged) are also associated with them. Under the Efficient-Market Hypothesis such relationships are not possible as all information is embedded in the prices, but in real markets we find such dependencies. In this paper we analyse lead-lag relationships of financial instruments and extend known methodology by using mutual information instead of Pearson’s correlation coefficient. Mutual information is not only a more general measure, sensitive to non-linear dependencies, but also can lead to a simpler procedure of statistical validation of links between financial instruments. We analyse lagged relationships using New York Stock Exchange 100 data not only on an intraday level, but also for daily stock returns, which have usually been ignored. Copyright The Author(s) 2014

Suggested Citation

  • Paweł Fiedor, 2014. "Information-theoretic approach to lead-lag effect on financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(8), pages 1-9, August.
    • Handle: RePEc:spr:eurphb:v:87:y:2014:i:8:p:1-9:10.1140/epjb/e2014-50108-3
    DOI: 10.1140/epjb/e2014-50108-3
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    1. Pawe{l} Fiedor, 2013. "Frequency Effects on Predictability of Stock Returns," Papers 1310.5540, arXiv.org, revised Nov 2013.
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    Cited by:

    1. Stanislaus Maier-Paape & Andreas Platen, 2016. "Lead–Lag Relationship Using a Stop-and-Reverse-MinMax Process," Risks, MDPI, vol. 4(3), pages 1-20, July.
    2. James, Nick & Menzies, Max & Gottwald, Georg A., 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Paweł Fiedor & Artur Hołda, 2015. "The Effects of Bankruptcy on the Structural Complexity of the Price Changes on WSE," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 41.
    4. Jacopo Rocchi & Enoch Yan Lok Tsui & David Saad, 2016. "Emerging interdependence between stock values during financial crashes," Papers 1611.02549, arXiv.org.
    5. Yutong Lu & Gesine Reinert & Mihai Cucuringu, 2023. "Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets," Papers 2302.09382, arXiv.org.
    6. Jacopo Rocchi & Enoch Yan Lok Tsui & David Saad, 2017. "Emerging interdependence between stock values during financial crashes," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    7. Stanislaus Maier-Paape & Andreas Platen, 2015. "Lead-Lag Relationship using a Stop-and-Reverse-MinMax Process," Papers 1504.06235, arXiv.org.
    8. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    9. Nick James & Max Menzies & Georg A. Gottwald, 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Papers 2202.10623, arXiv.org, revised Jun 2022.
    10. Paweł Fiedor, 2015. "Multiscale Analysis of the Predictability of Stock Returns," Risks, MDPI, vol. 3(2), pages 1-15, June.
    11. Fiedor, Paweł, 2014. "Sector strength and efficiency on developed and emerging financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 180-188.
    12. Tristan Millington & Mahesan Niranjan, 2020. "Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation," Papers 2005.03963, arXiv.org, revised Nov 2020.
    13. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    14. Arnav Hiray & Pratvi Shah & Vishwa Shah & Agam Shah & Sudheer Chava & Mukesh Tiwari, 2023. "Shifting Cryptocurrency Influence: A High-Resolution Network Analysis of Market Leaders," Papers 2307.16874, arXiv.org, revised Jan 2024.
    15. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    16. Stefanos Bennett & Mihai Cucuringu & Gesine Reinert, 2022. "Lead-lag detection and network clustering for multivariate time series with an application to the US equity market," Papers 2201.08283, arXiv.org.

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    Keywords

    Statistical and Nonlinear Physics;

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