Corporate Earnings Calls and Analyst Beliefs

TL;DR

This paper demonstrates that narratives in earnings calls significantly influence analyst expectations and reveals biases in their reactions to sentiment and risk-related narratives, using a novel text-morphing methodology.

Contribution

Introduces a new text-morphing approach with large language models to systematically vary narratives in earnings calls, revealing how analysts over- and under-react to specific narrative dimensions.

Findings

Narratives improve prediction of earnings and expectations.

Analysts over-react to sentiment and under-react to risk narratives.

The methodology enables precise measurement of narrative effects.

Abstract

Economic behavior is shaped not only by quantitative information but also by the narratives through which such information is communicated and interpreted (Shiller, 2017). I show that narratives extracted from earnings calls significantly improve the prediction of both realized earnings and analyst expectations. To uncover the underlying mechanisms, I introduce a novel text-morphing methodology in which large language models generate counterfactual transcripts that systematically vary topical emphasis (the prevailing narrative) while holding quantitative content fixed. This framework allows me to precisely measure how analysts under- and over-react to specific narrative dimensions. The results reveal systematic biases: analysts over-react to sentiment (optimism) and under-react to narratives of risk and uncertainty. Overall, the analysis offers a granular perspective on the mechanisms of expectation formation through the competing narratives embedded in corporate communication.