This study investigates how firms’ recognition of tariff-related risk in (pre-shock) 10-K disclosures affects stock-market reactions to the April 2, 2025, “Liberation Day” tariff shock. We use a machine-learning approach to identify semantically meaningful tariff-risk recognition across the entire 10-K filing, with separate analyses for Risk Factors (“Item 1A”) and the remaining sections of the filing, excluding Item 1A (“Other”). Our results show that firms that explicitly acknowledge tariff risk experience significantly lower abnormal returns around the announcement. Interestingly, this effect is not driven by disclosures in the Item 1A section, but instead arises from tariff-risk recognition in the Other sections of the 10-K and is concentrated among firms facing high litigation risk. The findings highlight the informational role of narrative risk recognition and the importance of where such disclosures appear within corporate filings. • Investigate how pre-shock 10-K tariff-risk disclosures shape Liberation Day returns. • Utilizes ML-based sentence classification to measure tariff-risk recognition in 10-Ks. • Finds that stronger tariff-risk recognition predicts more negative abnormal returns. • Effects are driven by tariff-risk language outside Item 1A (Risk Factors). • Negative impact is concentrated among firms with higher litigation risk exposure.
Andreou et al. (Sun,) studied this question.