2022/01/01 by Andrea Fronzetti Colladon, Stefano Grassi, Francesco Ravazzolo +1 · 1 voice
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Market Dynamics and Volatility #Stock Market Forecasting Methods
paper · pdf · doi:10.1002/for.2936
openalex publication_date 2022/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.