5 papers
Linguistic Passiveness in Earnings Calls and Stock Returns
Lynn Tao, Yuntao Wu, Charles Martineau +2
We study linguistic passiveness as the fraction of passive-voice sentences in earnings conference call Q&A sessions and show it is informative for equity markets. Using 105,642 U.S…
Measuring What is Repeated and Novel in Earnings Disclosures Using Optimal Transport
Yuntao Wu, Lynn Tao, Charles Martineau +2
We introduce an optimal transport framework to decompose the textual content of earnings press releases and conference calls into aligned (shared) and unaligned (unique) components…
Aligning Multilingual News for Stock Return Prediction
Yuntao Wu, Lynn Tao, Ing-Haw Cheng +4
News spreads rapidly across languages and regions, but translations may lose subtle nuances. We propose a method to align sentences in multilingual news articles using optimal tran…
Extracting the Structure of Press Releases for Predicting Earnings Announcement Returns
Yuntao Wu, Ege Mert Akin, Charles Martineau +2
We examine how textual features in earnings press releases predict stock returns on earnings announcement days. Using over 138,000 press releases from 2005 to 2023, we compare trad…
Understanding the Excess Bond Premium
Kevin Benson, Ing-Haw Cheng, John Hull +5
We study the drivers of the Gilchrist and Zakrajšek (2012) excess bond premium (EBP) through the lens of the news. The monthly attention the news pays to 180 topics (Bybee et al.,…