Measuring Information Quality by Topic Attention Divergence: Evidence from Earnings Calls

Abstract

Leveraging computational linguistics and 20 million turns of dialogues from earnings conference calls from 2006-2022, we introduce a novel measure that quantifies the disparity in narrative focus between managers’ disclosures and analysts’ questions during these calls, denoted as Topic Attention Divergence (TAD). A higher level of TAD indicates a higher level of firm-investor asymmetry and lower information quality. Our results confirm that higher TAD in earnings conference calls inversely (positively) predict firms’ future stock liquidity (cost of equity capital). Further, the predictive power of TAD is more pronounced in firms with higher information processing costs characterized by smaller firm size, lower levels of analyst coverage, and lower institutional ownership. A long-short portfolio sorted by TAD earns an annual 5.99% risk-adjusted return and cannot be explained by existing factor models.

Keywords: Information Quality, Voluntary Disclosure, Conference Call, Topic Classification, Cost of Capital, NLP, Large Language Model

JEL Classification: C82, D8, G1