The Promise and Peril of Generative AI: Evidence from GPT as Sell-Side Analysts

Abstract

Large language models (LLMs) promise to democratize financial analysis by reducing information-processing costs. Yet equal access does not ensure equal outcomes, as the locus of friction may shift from processing information to evaluating model outputs. We study GPT's earnings forecasts following corporate earnings releases and document two patterns. First, GPT's narrative attention is consistent and human-like but not always associated with higher forecast accuracy. Second, its quantitative reasoning varies substantially across contexts, challenging the view that LLMs are uniformly weak at numerical tasks. Building on these insights, we propose a diagnostic framework that links forecast accuracy to observable processing features (i.e., narrative focus, numerical reasoning, and self-assessed confidence). These indicators serve as proxies for this new form of information friction and alert investors when to exercise caution. Our study has implications for information frictions, regulatory oversight, and the economics of AI-mediated financial markets.