# Modeling financial analysts' decision making via the pragmatics and semantics of earnings calls

## TL;DR

This paper investigates how the language used in earnings calls influences analysts' decision making, revealing that pragmatic and semantic features can moderately predict post-call changes in analysts' price targets.

## Contribution

It introduces a set of 20 pragmatic features of analysts' questions and analyzes their correlation with analysts' recommendations, enhancing understanding of language's role in financial decision making.

## Key findings

- Earnings call language features are moderately predictive of analysts' post-call decisions.
- Pragmatic and semantic features complement market data in predicting analyst behavior.
- Model performance varies across different market sectors.

## Abstract

Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity research reports to help investors make decisions. In this paper, we examine analysts' decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts' questions which we correlate with analysts' pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts' post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts' decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.