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

arXiv: https://arxiv.org/abs/2009.04975 · HTML: https://mlsanity.com/paper/2009.04975/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Forecasting consumer confidence through semantic network analysis of online news](https://mlsanity.com/paper/2105.04900/) (2021)
- [Smart Agriculture : A Novel Multilevel Approach for Agricultural Risk Assessment over Unstructured Data](https://mlsanity.com/paper/2211.12515/) (2022)
- [Brand Network Booster: A new system for improving brand connectivity](https://mlsanity.com/paper/2309.16228/) (2023)
- [Analyzing Trendy Twitter Hashtags in the 2022 French Election](https://mlsanity.com/paper/2310.07576/) (2023)
- [Free Access to World News: Reconstructing Full-Text Articles from GDELT](https://mlsanity.com/paper/2504.16063/) (2025)

## Abstract (arXiv)

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.