  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.
