Corporate Earnings Calls and Analyst Beliefs

Economic behavior is shaped not only by quantitative information but also by the narratives through which such information is communicated and in- terpreted (Shiller, 2017). I show that narratives extracted from earnings calls significantly improve the prediction of both realized earnings and analyst ex- pectations. To uncover the underlying mechanisms, I introduce a novel text- morphing methodology in which large language models generate counterfac- tual transcripts that systematically vary topical emphasis (the prevailing narra- tive) while holding quantitative content fixed. This framework allows me to precisely measure how analysts under- and over-react to specific narrative di- mensions. 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 forma- tion through the competing narratives embedded in corporate communication.