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

TL;DR

This paper introduces a semantic network-based textual index to predict Italian stock and bond market returns and volatilities, demonstrating strong predictive power during the COVID-19 crisis.

Contribution

It presents a novel approach using semantic network analysis of news to forecast financial market movements, especially during crisis periods.

Findings

Index captures different phases of financial time series

Strong predictability for bond market returns and volatilities

Effective in forecasting stock market volatility during COVID-19

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.