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Forecasting financial markets with semantic network analysis in the COVID-19 crisis

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  • A. Fronzetti Colladon
  • S. Grassi
  • F. Ravazzolo
  • F. Violante

Abstract

This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic-related keywords appearing in the text. The index assesses the importance of the economic-related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures the different phases of financial time series well. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

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  • A. Fronzetti Colladon & S. Grassi & F. Ravazzolo & F. Violante, 2020. "Forecasting financial markets with semantic network analysis in the COVID-19 crisis," Papers 2009.04975, arXiv.org, revised Jul 2023.
    • Handle: RePEc:arx:papers:2009.04975
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    Cited by:

    1. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
    2. A. Fronzetti Colladon & F. Grippa & B. Guardabascio & G. Costante & F. Ravazzolo, 2021. "Forecasting consumer confidence through semantic network analysis of online news," Papers 2105.04900, arXiv.org, revised Jul 2023.
    3. 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).

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