研究发现新闻发布前股价已提前反应，且市场对故事类消息过激反应。
Buy the Rumor, Sell the News: When Is News Priced In?
- 用大模型自动标注新闻事件，按故事脉络分离首次报道与后续跟进。
- 新闻发布当日及之前股价已累积2.8倍后续涨幅，谣言日完成全部价格变动。
- 市场对数据类新闻反应不足，对故事类新闻反应过度，适合用于新闻预测建模。
我们基于2023至2026年457万条美股财经新闻（覆盖约3000只股票）检验了市场对新闻的定价规律。通过大语言模型教师指导，结合主动学习将新闻分类为17类事件与5个属性，按故事结构聚类出168万次股票-日期事件，并以36.44万条中性情绪事件作为对照组，测量贝塔调整后的异常收益。结果表明：第一，股价变动集中在新闻发布前及当天，所有已签事件在发布日收盘时的累计变动是20天后值的2.8倍；对谣言标记事件而言，谣言日即完成全部价格变动，后续确认无新增影响。第二，与可比股票相比，市场对量化基本面新闻（盈利、分红、指引、分析师行为）反应不足，持续漂移数周；而对软性故事类新闻（产品发布、宏观评论、人事变动）反应过度，随后回撤。第三，新闻传播本身带来波动率上升——发布前波动加剧，发布后因不确定性消除而下降。研究还生成每类事件的漂移量表格，可作为新闻驱动预测模型的先验信息。
原文摘要 · Abstract (English)
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.
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