买入传闻，卖出新闻：新闻何时被定价？

中文摘要 AI 辅助

市场有两句古老谚语：新闻发布时已被定价，传闻被买入而新闻被卖出，二者均认为新闻相关价格变动发生在发布前后而非之后。这些说法是否成立、适用于何种新闻、影响程度如何，是市场吸收公开信息速度的基础问题。我们针对2023-2026年覆盖约3000只美国股票的457万篇金融新闻文章开展测试：通过主动学习将大型语言模型教师蒸馏为紧凑分类器，为每篇文章分配17个事件标签和5个属性；将文章聚类为故事，以区分首次报道与后续报道；围绕最终168万个股-日事件测量经beta调整的异常收益，其中364405个中性情绪事件作为安慰剂组。得到三项结果：其一，新闻相关价格变动集中于发布前及发布当日：所有带符号事件汇总后，发布日收盘时新闻方向的累计变动是20天后的2.8倍；标记为传闻的事件中，传闻日覆盖全部变动，后续确认无贡献。其二，与可比股票安慰剂相比，市场对数字类新闻反应不足，对故事类新闻反应过度：量化基本面新闻（财报、股息、业绩指引、分析师行动）会在数周内沿新闻方向持续漂移，而软故事驱动类新闻（产品发布、宏观评论、领导层变动）则会回吐变动。其三，新闻兼具广度与方向：发布前宣传会推高波动率，新闻发布后波动率下降，因发布消除了不确定性。本研究还生成了各事件标签的测量漂移表，可作为新闻条件预测模型的先验。

英文摘要

Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it. Whether the claims hold, for which kinds of news, and by how much are basic questions about how fast markets absorb public information. We test them on 4.57 million financial news articles covering roughly 3,000 US stocks (2023-2026). A large language model teacher, distilled into a compact classifier through active learning, assigns each article one of 17 event tags and five attributes; articles are clustered into stories to separate first reports from follow-up coverage; and beta-adjusted abnormal returns are measured around the resulting 1.68 million stock-day events, with 364,405 neutral-sentiment events as a placebo group. Three results follow. First, the price move associated with news concentrates before and at publication: pooled across all signed events, the cumulative move in the news direction by the close of publication day is 2.8 times its value 20 days later, and for rumor-flagged events the rumor day captures the entire move while the subsequent confirmation contributes nothing. Second, measured against the placebo of comparable stocks, markets underreact to numbers and overreact to stories: quantified fundamental news (earnings, dividends, guidance, analyst actions) keeps drifting in the direction of the news for weeks, while soft story-driven news (launches, macro commentary, leadership) gives back its move. Third, news carries width as well as direction: publicity raises volatility before the publication day, and volatility declines once the news is out, because publication resolves uncertainty. The study also produces a table of measured drift for each event tag, usable as a prior in news-conditioned forecasting models.

发表机构

• Tailstate Intelligence Ltd（Tailstate Intelligence有限公司）
• Zanista AI Ltd（Zanista AI有限公司）
• Increase Alpha, LLC（Increase Alpha有限责任公司）

机构由 AI 辅助整理，请以论文原文为准。