Analyst Logical Inconsistency and Stock Price Crash Risk: Evidence from Large Language Models

Abstract

This paper examines whether and how analyst logical inconsistency affects the stock price crash risk in China’s A-share market. The sample includes 3784 listed non-financial companies. The final sample comprises 12,460 firm-year observations from 2016 to 2023. Specifically, we employ the open source Qwen1.5-14B-Chat model, an instruction-tuned generative large language model, to measure analyst logical inconsistency, classify the sentiment expressed in analyst reports, and determine the differences between earnings forecasts and textual tone. Using a panel fixed-effect model for basic regression, and applying two-stage least squares and propensity score matching to deal with endogenous problems, we find that analyst logical inconsistency significantly increases the stock price crash risk. The results remain robust to alternative variable definitions, additional control variables, alternative sample periods, and more stringent fixed-effects specifications. The mechanism test shows that the analyst logical inconsistency increases the stock price crash risk through three channels: increased financial risk, reduced investment efficiency, and degraded information disclosure quality. Heterogeneity analysis also shows that this positive impact is strongest in companies with high media coverage, good corporate reputation and low ESG performance. Our research results are helpful to the study of information intermediaries and stock price crash risk by introducing measures for the quality of analyst reports based on large language models, and provide useful suggestions for regulators and investors in emerging markets.