IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.08242.html
   My bibliography  Save this paper

Analyzing Communicability and Connectivity in the Indian Stock Market During Crises

Author

Listed:
  • Pawanesh Pawanesh
  • Charu Sharma
  • Niteesh Sahni

Abstract

In financial networks, information does not always follow the shortest path between two nodes but may also take alternate routes. Communicability, a network measure, resolves this complexity and, in diffusion-like processes, provides a reliable measure of the ease with which information flows between nodes. As a result, communicability appears to be an important measure for detecting disturbances in connectivity within financial systems, similar to instability caused by periods of high volatility. This study investigates the evolution of communicability measures in the stock networks during periods of crises, showing how systemic shocks strengthen the pairwise interdependence between stocks in the financial market. In this study, the permutation test reveals that approximately 83.5 per cent of stock pairs were found to be statistically significant at the significance level of 0.001 and have an increase in the shortest communicability path length during the crisis than the normal days, indicating enhanced interdependence and heightened information flow in the market. Furthermore, we show that when employed as features in the classification model, the network shortest path-based measures, along with communicability measures, are able to accurately classify between the times periods of market stability and volatility. Additionally, our results show that the geometric measures perform better in terms of classification accuracy than topological measures. These findings provide important insights into market behaviour during times of increased volatility and advance our understanding of the financial market crisis.

Suggested Citation

  • Pawanesh Pawanesh & Charu Sharma & Niteesh Sahni, 2025. "Analyzing Communicability and Connectivity in the Indian Stock Market During Crises," Papers 2502.08242, arXiv.org.
    • Handle: RePEc:arx:papers:2502.08242
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.08242
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chun-Xiao Nie, 2021. "Studying the correlation structure based on market geometry," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(2), pages 411-441, April.
    2. Sunil Kumar & Nivedita Deo, 2012. "Correlation, Network and Multifractal Analysis of Global Financial Indices," Papers 1202.0409, arXiv.org.
    3. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    4. Fragkiskos Papadopoulos & Maksim Kitsak & M. Ángeles Serrano & Marián Boguñá & Dmitri Krioukov, 2012. "Popularity versus similarity in growing networks," Nature, Nature, vol. 489(7417), pages 537-540, September.
    5. Alessandro Muscoloni & Josephine Maria Thomas & Sara Ciucci & Ginestra Bianconi & Carlo Vittorio Cannistraci, 2017. "Machine learning meets complex networks via coalescent embedding in the hyperbolic space," Nature Communications, Nature, vol. 8(1), pages 1-19, December.
    6. 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.
    7. R. Vilela Mendes & R. Lima & T. Araújo, 2002. "A Process-Reconstruction Analysis Of Market Fluctuations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 5(08), pages 797-821.
    8. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    9. X. F. Jiang & T. T. Chen & B. Zheng, 2014. "Structure of local interactions in complex financial dynamics," Papers 1406.0070, arXiv.org.
    10. Charu Sharma & Amber Habib, 2019. "Mutual information based stock networks and portfolio selection for intraday traders using high frequency data: An Indian market case study," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    11. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2020. "Community structure in the World Trade Network based on communicability distances," Papers 2001.06356, arXiv.org, revised Jul 2020.
    12. Xue Guo & Hu Zhang & Tianhai Tian, 2018. "Development of stock correlation networks using mutual information and financial big data," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pawanesh Pawanesh & Charu Sharma & Niteesh Sahni, 2024. "Exploiting the geometry of heterogeneous networks: A case study of the Indian stock market," Papers 2404.04710, arXiv.org, revised Jan 2025.
    2. Weibo Li & Wei Liu & Lei Wu & Xue Guo, 2021. "Risk spillover networks in financial system based on information theory," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    3. Martin Keller-Ressel & Stephanie Nargang, 2020. "The hyperbolic geometry of financial networks," Papers 2005.00399, arXiv.org, revised May 2020.
    4. Maksim Kitsak & Alexander Ganin & Ahmed Elmokashfi & Hongzhu Cui & Daniel A. Eisenberg & David L. Alderson & Dmitry Korkin & Igor Linkov, 2023. "Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Zhenpeng Li & Luo Li, 2023. "The Generation Mechanism of Degree Distribution with Power Exponent >2 and the Growth of Edges in Temporal Social Networks," Mathematics, MDPI, vol. 11(13), pages 1-11, June.
    6. Chun-Xiao Nie & Fu-Tie Song, 2021. "Entropy of Graphs in Financial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1149-1166, April.
    7. Guo, Xue & Li, Weibo & Zhang, Hu & Tian, Tianhai, 2022. "Multi-likelihood methods for developing relationship networks using stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    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. Choi, Insu & Kim, Woo Chang, 2024. "Practical forecasting of risk boundaries for industrial metals and critical minerals via statistical machine learning techniques," International Review of Financial Analysis, Elsevier, vol. 94(C).
    10. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    11. Cho, Younghwan & Song, Jae Wook, 2023. "Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees," Finance Research Letters, Elsevier, vol. 53(C).
    12. Nicholas D Larusso & Brian E Ruttenberg & Ambuj Singh, 2013. "A Latent Parameter Node-Centric Model for Spatial Networks," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-15, September.
    13. Junya Wang & Yi-Jiao Zhang & Cong Xu & Jiaze Li & Jiachen Sun & Jiarong Xie & Ling Feng & Tianshou Zhou & Yanqing Hu, 2024. "Reconstructing the evolution history of networked complex systems," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    14. Lei Tan & Jun-Jie Chen & Bo Zheng & Fang-Yan Ouyang, 2016. "Exploring Market State and Stock Interactions on the Minute Timescale," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    15. Assaf, Ata & Charif, Husni & Demir, Ender, 2022. "Information sharing among cryptocurrencies: Evidence from mutual information and approximate entropy during COVID-19," Finance Research Letters, Elsevier, vol. 47(PA).
    16. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    17. Vinayak, & Raghuvanshi, Adarsh & kshitij, Avinash, 2023. "Signatures of capacity development through research collaborations in artificial intelligence and machine learning," Journal of Informetrics, Elsevier, vol. 17(1).
    18. Jascha-Alexander Koch & Michael Siering, 2019. "The recipe of successful crowdfunding campaigns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(4), pages 661-679, December.
    19. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Oct 2024.
    20. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2502.08242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.