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arXiv · 2610.04094

Measuring What is Repeated and Novel in Earnings Disclosures Using Optimal Transport

Yuntao Wu·Lynn Tao·Charles Martineau·Vincent Grégoire·Andreas Veneris
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Abstract

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, and use these as predictors of stock returns around earnings announcements. Using over 105,000 document pairs from 2008 to 2023, we find that the aligned portions of both disclosures explain announcement-day returns with high statistical significance. The unaligned component of conference calls, which captures forward-looking content absent from press releases, provides substantial incremental explanatory power, shedding light on why conference calls are central to market reactions to earnings news. Extending to post-announcement returns, investors appear to overreact to shared press release content while underreacting to the unique information conveyed in conference calls.

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Yuntao Wu, Lynn Tao, Charles Martineau, Vincent Grégoire, Andreas Veneris. 2026-10-02. Measuring What is Repeated and Novel in Earnings Disclosures Using Optimal Transport. https://arxiv.org/abs/2610.04094

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