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

A. Fronzetti Colladon, S. Grassi, Francesco Ravazzolo and Francesco Violante ()

Papers from arXiv.org

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.

Date: 2020-09, Revised 2023-07
New Economics Papers: this item is included in nep-big, nep-fmk, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Published in Journal of Forecasting 42(5), 1187-1204 (2023)

Downloads: (external link)
http://arxiv.org/pdf/2009.04975 Latest version (application/pdf)

Related works:
Working Paper: Forecasting financial markets with semantic network analysis in the COVID—19 crisis (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2009.04975

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().