Forecasting Earnings Surprises from Conference Call Transcripts (2023)

Abstract: There is a multitude of textual data relevant to the financial markets, spanning genres such as financial news, earnings conference calls, and social media posts. Earnings conference calls are one of the most important to information flow as they reflect a direct communication between company executives, financial analysts, and large shareholders. Since these calls contain content that is forward-looking in nature, they can be used to forecast the future performance of the company relative to market expectations. However, they typically contain over 5,000 words of text and large amounts of industry jargon. This length and domain-specific language present problems for many generic pretrained language models. In this work, we introduce a novel task of predicting earnings surprises from earnings call transcripts and contribute a new long document dataset that tests financial understanding with complex signals. We explore a variety of approaches for this long document classification task and establish some strong baselines. Furthermore, we demonstrate that it is possible to predict companies’ future earnings surprises from solely the text of their conference calls with reasonable accuracy. Finally, we probe the models through different interpretability methods and reveal some intuitive explanations of the linguistic features captured that go beyond traditional sentiment analysis.

## introduction there is a multitude of textual data relevant to the financial markets, spanning genres such as financial news, earnings conference calls, analyst recommendation reports, social media posts, and regulatory filings. earnings conference calls are one of the most important datasets relevant to the information flow in equity markets because they reflect a direct communication between company executives and financial analysts @xcite . ## model interpretability and analysis since hierarchical finbert was the best performing model, we probe its predictions through a variety of interpretability methods to better understand the linguistic features important to this task. ## conclusion in conclusion, we propose a novel task that uses transcripts from earnings conference calls to predict future earnings surprises. we formulate the problem as a long document classification task and explore a variety of different approaches to address it. while the length and language of the calls presents challenges for generic pretrained language models, we establish several strong baselines. we demonstrate that it is possible to predict companies' future earnings surprises with reasonable accuracy from the solely the text of their earnings conference calls. further, we probe the model through multiple interpretability methods to uncover intuitive linguistic features that go beyond traditional sentiment analysis.