Forecasting financial markets with semantic network analysis in the
COVID-19 crisis
- URL: http://arxiv.org/abs/2009.04975v4
- Date: Sat, 8 Jul 2023 06:33:48 GMT
- Title: Forecasting financial markets with semantic network analysis in the
COVID-19 crisis
- Authors: A. Fronzetti Colladon, S. Grassi, F. Ravazzolo, F. Violante
- Abstract summary: We apply the index to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities.
Results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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