Forecasting financial markets with semantic network analysis in the COVID-19 crisis
This work addresses forecasting financial markets for investors and policymakers, but it is incremental as it applies a novel method to a specific dataset during a crisis.
The paper tackled predicting Italian stock and bond market returns and volatilities during the COVID-19 crisis using a new textual data index based on economic keywords in news articles, resulting in strong predictability for bond market data and stock market volatility.
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.