Impact of the COVID-19 pandemic on the stock market and investor online word of mouth

Abstract

Investor sentiment based on social media Word-of-Mouth (WoM) has been shown to be predictive of stock returns under normal market conditions. However, the COVID-19 pandemic has brought unprecedented impact and uncertainty to the stock market. In this paper, we examine the predictability of social media-based WoM on day-ahead US stock market returns across three phases of the pandemic: pre-COVID, peak-COVID, and recovery. We collect over 24 million stock-related tweets from Twitter across these three periods and employ text-mining methods for feature engineering. We focus on two channels of WoM information, aggregate sentiments, and disaggregated topics, representing investors' overall opinions and discussion patterns about the stock market. We find that, in contrast to normal market conditions, the investor sentiment indices are not significant in predicting market returns during the peak-COVID and recovery periods, while for the pre-COVID period, our findings are consistent with prior studies. To explore the effect of disaggregated latent topics, we propose an ensemble feature selection approach that identifies relevant topics for each of the three periods. We find that adding topic information significantly improves model prediction during the peak-COVID period. Our findings imply that during a global socio-economic disruption and the subsequent recovery, the landscape of financial markets, examined through the lens of social media-based WoM, is different than during normal market conditions. Our findings also suggest that investors who primarily trade on sentiment may wish to leverage machine learning techniques to identify latent topic information, subsequently incorporating such information into their trading decision-making.