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Co-movements in financial fluctuations are anchored to economic fundamentals: A mesoscopic mapping

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  • Kiran Sharma
  • Balagopal Gopalakrishnan
  • Anindya S. Chakrabarti
  • Anirban Chakraborti

Abstract

We demonstrate the existence of an empirical linkage between the nominal financial networks and the underlying economic fundamentals across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and figure the relative importance of the sectors in the nominal network through a measure of centrality and clustering algorithms. The eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with the metrics market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector-level nominal return dynamics is anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. Our results are reasonably robust across 27 countries of varying degrees of prosperity and across periods of market turbulence (2008-09) as well as relative calmness (2015-16).

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  • Kiran Sharma & Balagopal Gopalakrishnan & Anindya S. Chakrabarti & Anirban Chakraborti, 2016. "Co-movements in financial fluctuations are anchored to economic fundamentals: A mesoscopic mapping," Papers 1612.05952, arXiv.org, revised Jan 2017.
  • Handle: RePEc:arx:papers:1612.05952
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    References listed on IDEAS

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    Cited by:

    1. Dariusz Siudak, 2021. "Sectoral Analysis of the US Stock Market through Complex Networks," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 951-966.
    2. Frank Emmert-Streib & Aliyu Musa & Kestutis Baltakys & Juho Kanniainen & Shailesh Tripathi & Olli Yli-Harja & Herbert Jodlbauer & Matthias Dehmer, 2017. "Computational Analysis of the structural properties of Economic and Financial Networks," Papers 1710.04455, arXiv.org.
    3. Kumar, Ashish & Chakrabarti, Anindya S. & Chakraborti, Anirban & Nandi, Tushar, 2021. "Distress propagation on production networks: Coarse-graining and modularity of linkages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    4. Kumar, Sudarshan & Bansal, Avijit & Chakrabarti, Anindya S., 2019. "Ripples on financial networks," IIMA Working Papers WP 2019-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Kiran Sharma & Subhradeep Das & Anirban Chakraborti, 2017. "Global Income Inequality and Savings: A Data Science Perspective," Papers 1801.00253, arXiv.org, revised Aug 2018.
    6. Ashish Kumar & Anindya S. Chakrabarti & Anirban Chakraborti & Tushar Nandi, 2020. "Distress propagation on production networks: Coarse-graining and modularity of linkages," Papers 2004.14485, arXiv.org.
    7. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    8. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.

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