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 well the different phases of financial time series. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.

中文翻译：

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在 COVID-19 危机中使用语义网络分析预测金融市场

本文使用一种新的文本数据索引来预测股市数据。该指数应用于大量新闻，以评估文本中出现的一个或多个一般经济相关关键词的重要性。该指数根据经济相关关键词的使用频率和语义网络位置来评估其重要性。我们将其应用于意大利媒体并构建指数，以预测最近一个样本期间（包括 COVID-19 危机）意大利股票和债券市场的回报和波动率。证据表明，该指数很好地捕捉了金融时间序列的不同阶段。此外，结果表明债券市场数据具有可预测性的有力证据，包括回报和波动性、短期和长期期限以及股票市场波动性。