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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.
    2. 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.
    3. Zhu, Pengfei & Lu, Tuantuan & Chen, Shenglan, 2022. "How do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak? A wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    4. 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.

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