论文标题
在Covid-19危机中，通过语义网络分析预测金融市场
Forecasting financial markets with semantic network analysis in the COVID-19 crisis
论文作者
论文摘要
本文使用新的文本数据指数来预测股票市场数据。该指数应用于大量新闻，以评估文本中出现的一个或多个与经济相关的关键字的重要性。该指数根据其使用频率和语义网络位置评估了与经济相关的关键字的重要性。我们将其应用于意大利媒体和构造指数，以预测最近样本期间的意大利股票和债券市场收益和波动，包括COVID-19危机。证据表明该指数很好地捕获了财务时间序列的不同阶段。此外，结果表明债券市场数据的可预测性，回报和波动性，短期和长期的到期以及股票市场波动。
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.
