Manager sentiment, policy uncertainty, ESG disclosure and firm performance: a large language model in corporate landscape

Purpose - This study aims to examine the impact of manager sentiment on the firm performance (FP) of Indian-listed nonfinancial firms. Further, it endeavors to investigate the moderating role of economic policy uncertainty (EPU) and environment, social and governance (ESG) transparency in this relationship. Design/methodology/approach - A noble manager sentiment is introduced using FinBERT, a bidirectional encoder representation from a transformers (BERT)-type large language model. Using this deep learning-based natural language processing approach implemented through a Python-generated algorithm, this study constructs a manager sentiment for each firm and year based on the management discussions and analysis (MD&A) report. This research uses the system GMM to examine how manager sentiment affects FP. Findings - The empirical results suggest that managers’ optimistic outlook in MD&A corporate disclosure sections tends to present higher performance. This positive association remains consistent after several robustness checks – using propensity score matching and instrumental variable approach to address further endogeneity, using alternative proxies of manager sentiment and FP and conducting subsample analysis based on financial constraints. Furthermore, the authors observe that the relationship is more pronounced for ESG-disclosed firms and during the low EPU. Practical implications - The results demonstrate that the manager sentiment strongly predicts FP. Thus, this study may provide valuable insight for academics, practitioners, investors, corporates and policymakers. Originality/value - To the best of the authors’ knowledge, this is the first study to predict FP by using FinBERT-based managerial sentiment, particularly in an emerging market context.