Forecasting Stock Market Reactions Using Decomposed Topics and Sentiments in Earning Calls

A positive relationship between Earning Call Sentiment and Stock Market Reaction has already been identified. Still, this utilization for prediction has yet to gain much attention. This study explores the predictive potential of earnings calls by employing the BERT model to extract key topics and their sentiments. Various machine learning techniques are then employed to leverage these insights for predicting stock market reactions and associated risks, evaluating the extent to which earnings call topics and sentiments can enhance prediction accuracy. Analyzing all quarterly earnings calls from S&P 500 companies in 2022, the results indicate that the decomposition into key topics with their respective sentiment outperforms the usage of overall sentiment across multiple scenarios and models. The random forest model is found to make the best utilization of the decomposition.