Does the Textual Tone of Analyst Reports Have Valuable Information? Korean Evidence*

Abstract We investigate whether the text of analyst reports can provide additional information beyond the recommendation and target price. Positive and negative word lexicons are generated through an automated Bayesian learning method applied to Korean analyst reports spanning from 2016 to 2018. Then, the textual tone of an analyst report is quantified as the difference between the frequencies of positive and negative words in the text. The announcement returns of portfolios sorted by textual tone exhibit significant differences ranging from 1.14% to 2.82% within the same recommendation or target price revision group. Regression analysis also reveals significant association between the textual tone of analyst reports and stock announcement returns, even when controlling for the recommendation and target price. Notably, the text proves to be more informative in negative tones and within firms with limited analyst coverage. Our results indicate that textual analysis can unveil nuanced analyst opinions not captured by numerical information.