The Effect of Annual Report Readability on Analysts’ Earnings Forecast Accuracy

[Purpose] This study empirically examines how the readability of annual reports, as one of their linguistic characteristics, affects the accuracy of analysts’ earnings forecasts. Readable annual reports can enhance users’ understanding and improve information processing efficiency, which may meaningfully influence analysts’ judgments. The primary objective of this study is to explore the relationship between the corporate information environment and analysts’ decision-making behavior. [Methodology] Using annual reports of firms listed on the KOSPI and KOSDAQ markets from 2010 to 2020, this study measures readability through text analysis, specifically utilizing report length and file size as proxies. A regression analysis is conducted to investigate the relationship between annual report readability and analysts’ forecast errors. Additionally, the study analyzes differences between optimistic and pessimistic forecast samples. [Findings] The results indicate that firms with more readable annual reports tend to exhibit significantly higher accuracy in analysts’ earnings forecasts. In the optimistic forecast group, higher readability is associated with smaller forecast errors. Conversely, in the pessimistic group, higher readability correlates with larger forecast errors, suggesting a tendency toward more conservative forecasting. [Implications] This study provides empirical evidence that the non-financial attribute of readability in annual reports has a tangible impact on information users’ decision-making processes. These findings imply that improvements in disclosure practices-particularly through clearer reporting standards or readability-enhancing guidelines-are necessary to strengthen the effectiveness of corporate disclosures.