Leveraging Text Mining to Extract Insights from Earnings Call Transcripts
Vol.21, No.1, 2023
by Andrew Chin and Yuyu Fan
We apply text-mining techniquesin earnings call transcriptsto extract meaningful features that capture management and investment community signals. Using a corpus of transcripts of earnings calls for global companies from 2010 to 2021, we create fundamentally driven features spanning document attributes, readability, and sentiment on different sections of the transcripts. We test the efficacy of these features in predicting the future stock returns of companies and find that there are opportunities for investors to use these signals in stock selection. Specifically, we find that readability and sentiment-based techniques can enhance an investor’s ability to differentiate amongst outperformers and underperformers and these results are robust across market capitalization as well as investment universes (US Large Cap, US Small Cap, World ex-US, and Emerging Markets). We also introduce methods to create more robust sentiment features for active and systematic investors. By analyzing the performance patterns of the various call participants, we find evidence that the analyst questions may contain more information than the executive sections. Finally, we observe that sentiment features derived from context-driven deep learning language models like BERT are promising and may have more efficacy than bag-of-words approaches.