Qualitative expressions in MD&A and management forecast accuracy

Abstract

In this study, we explore the Management Discussion and Analysis (MD&A) section of Japanese Annual Securities Reports, a mandatory disclosure known for providing crucial qualitative information about management perspectives. Our research primarily utilizes the ChatGPT to extract qualitative expressions within the MD&A texts. Firstly, we compare the proportion of qualitative expressions presented in their MD&A across different companies, quantifying the extent of qualitative information. Our hypothesis that a higher prevalence of qualitative information may indicate a deeper understanding by management of their company’s business model, market environment, and strategy, potentially leading to more accurate performance forecasts. We then analyze the impact of the proportion of qualitative expressions in the MD&A on the accuracy of management earnings guidance in financial results summary. We aim to understand how the nature of information in MD&A–whether more qualitative–correlates with the precision of managerial predictions on company performance.