Tracking Turbulence Through Financial News During COVID-19
This work addresses financial market analysis during crises for investors and researchers, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of analyzing financial sentiment in news during the COVID-19 pandemic, achieving a maximum weighted F1 score of 0.746 with a CNN-based model and finding strong correlations between predicted sentiment and stock market indicators like the S&P 500 index.
Grave human toll notwithstanding, the COVID-19 pandemic created uniquely unstable conditions in financial markets. In this work we uncover and discuss relationships involving sentiment in financial publications during the 2020 pandemic-motivated U.S. financial crash. First, we introduce a set of expert annotations of financial sentiment for articles from major American financial news publishers. After an exploratory data analysis, we then describe a CNN-based architecture to address the task of predicting financial sentiment in this anomalous, tumultuous setting. Our best performing model achieves a maximum weighted F1 score of 0.746, establishing a strong performance benchmark. Using predictions from our top performing model, we close by conducting a statistical correlation study with real stock market data, finding interesting and strong relationships between financial news and the S\&P 500 index, trading volume, market volatility, and different single-factor ETFs.