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Impact of Brexit on STOXX Europe 600 Constituents: A Complex Network Analysis

Author

Listed:
  • Anna Maria D’Arcangelis

    (Department of Economics and Business, University of Tuscia, Via del Paradiso 47, 01100 Viterbo, Italy
    These authors contributed equally to this work.)

  • Arianna Pierdomenico

    (BancoBPM, Piazza Filippo Meda 4, 20121 Milan, Italy
    These authors contributed equally to this work.)

  • Giulia Rotundo

    (Department of Statistical Sciences, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy
    These authors contributed equally to this work.)

Abstract

Political events play a significant role in exerting their influence on financial markets globally. This paper aims to investigate the long term effect of Brexit on European stock markets using Complex Network methods as a starting point. The media has heavily emphasized the connection between this major political event and its economic and financial impact. To analyse this, we created two samples of companies based on the geographical allocation of their revenues to the UK. The first sample consists of companies that are either British or financially linked to the United Kingdom. The second sample serves as a control group and includes other European companies that are conveniently matched in terms of economic sector and firm size to those in the first sample. Each analysis is repeated over three non-overlapping periods: before the 2016 Referendum, between the Referendum and the 2019 General Elections, and after the 2019 General Elections. After an event study aimed at verifying the short-term response of idiosyncratic daily returns to the referendum result, we analysed the topological evolution of the networks through the MST (Minimum Spanning Trees) of the various samples. Finally, after the computation of the centrality measures pertaining to each network, our attention was directed towards the examination of the persistence of the levels of degree and eigenvector centralities over time. Our target was the investigation on whether the events that determined the evolution of the MST had also brought about structural modifications to the centrality of the most connected companies within the network. The findings demonstrate the unexpected impact of the referendum outcome, which is more noticeable on European equities compared to those of the UK, and the lack of influence from the elections that marked the beginning of the hard Brexit phase in 2019. The modifications in the MST indicate a restructuring of the network of British companies, particularly evident in the third period with a repositioning of the UK nodes. The dynamics of the MSTs around the referendum date is associated with the persistence in the relative rank of the centrality measures (relative to the median). Conversely, the arrival of hard Brexit does alter the relative ranking of the nodes in accord to the the degree centrality. The ranking in accord to the eigenvector centrality keeps the persistence. However, such movements are not statistically significant. An analysis of this kind points out relevant insights for investors, as it equips them to have a comprehensive view of political events, while also assisting policymakers in their endeavour to uphold stability by closely monitoring the ever-changing influence and interconnectedness of global stock markets during similar political events.

Suggested Citation

  • Anna Maria D’Arcangelis & Arianna Pierdomenico & Giulia Rotundo, 2024. "Impact of Brexit on STOXX Europe 600 Constituents: A Complex Network Analysis," Stats, MDPI, vol. 7(3), pages 1-20, June.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:3:p:38-646:d:1423706
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    References listed on IDEAS

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    1. Onnela, J.-P. & Chakraborti, A. & Kaski, K. & Kertész, J., 2003. "Dynamic asset trees and Black Monday," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 247-252.
    2. Sunil Kumar & Nivedita Deo, 2012. "Correlation, Network and Multifractal Analysis of Global Financial Indices," Papers 1202.0409, arXiv.org.
    3. Guedes, E.F. & Ferreira, Paulo & Dionísio, Andreia & Zebende, G.F., 2019. "An econophysics approach to study the effect of BREXIT referendum on European Union stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1175-1182.
    4. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    5. D'Arcangelis, Anna Maria & Rotundo, Giulia, 2021. "Herding in mutual funds: A complex network approach," Journal of Business Research, Elsevier, vol. 129(C), pages 679-686.
    6. Campbell, John Y. & Lo, Andrew W. & MacKinlay, A. Craig & Whitelaw, Robert F., 1998. "The Econometrics Of Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(4), pages 559-562, December.
    7. Brown, Stephen J. & Warner, Jerold B., 1985. "Using daily stock returns : The case of event studies," Journal of Financial Economics, Elsevier, vol. 14(1), pages 3-31, March.
    8. Chakrabarti, Rajesh & Roll, Richard, 2002. "East Asia and Europe during the 1997 Asian collapse: a clinical study of a financial crisis," Journal of Financial Markets, Elsevier, vol. 5(1), pages 1-30, January.
    9. Arcagni, Alberto & Grassi, Rosanna & Stefani, Silvana & Torriero, Anna, 2017. "Higher order assortativity in complex networks," European Journal of Operational Research, Elsevier, vol. 262(2), pages 708-719.
    10. Chakrabarti, Rajesh & Roll, Richard, 2002. "East Asia and Europe During the 1997 Asian Collapse: A Clinical Study of a Financial Crisis," University of California at Los Angeles, Anderson Graduate School of Management qt09f9j331, Anderson Graduate School of Management, UCLA.
    11. Kofman, Paul & Koedijk, Kees & Campbell, Rachel, 2002. "Increased Correlation in Bear markets: A Downside Risk Perspective," CEPR Discussion Papers 3172, C.E.P.R. Discussion Papers.
    12. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
    13. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.
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