Corporate Earnings Calls and Analyst Beliefs

I study how linguistic dimensions of corporate disclosure shape earnings expectations. I develop a methodology that uses large language models to generate counterfactual versions of earnings call transcripts that vary in language while holding underlying quantitative content fixed, creating a form of counterfactual variation that is impossible to obtain experimentally. I first show that textual features from earnings calls improve out-of-sample predictions of analyst forecasts and realized earnings above and beyond a rich set of quantitative fundamentals. I then vary each call along six linguistic dimensions---confidence, sentiment, uncertainty, forward guidance, technical jargon, and macroeconomic focus---and trace how these changes affect predicted analyst revisions. The results show that analysts respond systematically to linguistic variation: they place excessive weight on optimistic and macro-focused language and do not fully incorporate information embedded in language emphasizing risks and uncertainty. Linguistic variation also changes which fundamentals drive predictions: optimistic language amplifies the role of recent earnings surprises, while risk-laden language diminishes it, evidence that presentation shapes not just the level of analyst beliefs but the information they attend to.