发表机构
University of Toronto; HEC Montréal（多伦多大学; 蒙特利尔高等商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出最优传输框架分解盈余披露文本为共享与独特成分，基于超10.5万对文档，发现共享内容解释公告日回报，电话会议独特前瞻信息提供增量解释力，且投资者对新闻稿过度反应、对电话会议信息反应不足。
AI 中文摘要
我们引入了一个最优传输框架，将盈余新闻稿和电话会议的文字内容分解为对齐（共享）和未对齐（独特）的组成部分，并将这些作为盈余公告前后股票回报的预测因子。利用2008年至2023年间超过105,000对文档，我们发现两种披露的对齐部分对公告日回报具有高度统计显著的解释力。电话会议中未对齐的部分，捕捉了新闻稿中缺失的前瞻性内容，提供了显著的增量解释力，揭示了为何电话会议对市场对盈余新闻的反应至关重要。扩展到公告后回报，投资者似乎对共享的新闻稿内容过度反应，而对电话会议中传达的独特信息反应不足。
英文摘要
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.
Comments9 pages, 4 tables, 4 figures
