Leveraging Text Mining to Extract Insights From Earnings Call Transcripts

24 May 2023

2 min read

What You Need to Know

In research contributed to the Journal Investment Management, AB’s data science experts test the efficacy of text mining techniques and find that there are opportunities to use these signals in stock selection.

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Introduction

We apply text-mining techniques in earnings call transcripts to 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 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 among outperformers and underperformers, results that are robust across market capitalizations 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 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.

Past performance, historical and current analyses, and expectations do not guarantee future results.

The views expressed herein do not constitute research, investment advice or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams. Views are subject to revision over time.

About the Authors

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