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Network based evidence of the financial impact of Covid-19 pandemic

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  • Ahelegbey, Daniel Felix
  • Cerchiello, Paola
  • Scaramozzino, Roberta
Abstract
How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analyzing the top 50 S&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.

Suggested Citation

  • Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:finana:v:81:y:2022:i:c:s1057521922000710
    DOI: 10.1016/j.irfa.2022.102101
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    1. Cameron Cornell & Lewis Mitchell & Matthew Roughan, 2023. "Vector Autoregression in Cryptocurrency Markets: Unraveling Complex Causal Networks," Papers 2308.15769, arXiv.org.
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    4. Celani, Alessandro & Cerchiello, Paola & Pagnottoni, Paolo, 2024. "The topological structure of panel variance decomposition networks," Journal of Financial Stability, Elsevier, vol. 71(C).
    5. Roberta Scaramozzino & Paola Cerchiello & Tomaso Aste, 2021. "Information theoretic causality detection between financial and sentiment data," DEM Working Papers Series 202, University of Pavia, Department of Economics and Management.
    6. Cameron Cornell & Lewis Mitchell & Matthew Roughan, 2024. "Enhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression," Papers 2408.12210, arXiv.org.
    7. Daniele Pala & Enea Parimbelli & Cristiana Larizza & Cindy Cheng & Manuel Ottaviano & Andrea Pogliaghi & Goran Đukić & Aleksandar Jovanović & Ognjen Milićević & Vladimir Urošević & Paola Cerchiello & , 2022. "A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas," IJERPH, MDPI, vol. 19(15), pages 1-16, July.
    8. Ahelegbey, Daniel Felix & Celani, Alessandro & Cerchiello, Paola, 2024. "Measuring the impact of the EU health emergency response authority on the economic sectors and the public sentiment," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).

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