Tracking Turbulence Through Financial News During COVID-19

TL;DR

This paper analyzes the relationship between financial news sentiment during COVID-19 and market behavior, introducing a CNN-based sentiment prediction model and revealing strong correlations with market indicators.

Contribution

It presents a novel CNN-based approach for predicting financial sentiment during turbulent times and provides insights into news-market relationships during COVID-19.

Findings

CNN model achieved F1 score of 0.746

Strong correlations found between news sentiment and market metrics

Introduced expert annotations for financial news sentiment

Abstract

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.