AI风险是否已在美国股市中被定价？来自金融新闻因素的证据
Are AI Risks Priced in the U.S. Stock Market? Evidence from Financial News Factors
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中文总结 AI 辅助
本文利用LDA和MIT AI风险库分类法，基于《华尔街日报》新闻构建四个AI风险因素，发现仅虚假信息因素（D3）在美国股市中被稳健定价，表明AI风险定价具有领域特异性。
中文摘要 AI 辅助
本文探讨了企业面临不同类型AI风险新闻的暴露程度是否已在美国股票回报中被定价。通过使用AI和风险关键词，我识别出2016年1月至2025年12月间《华尔街日报》的7,787篇文章。我将潜在狄利克雷分配（LDA）与MIT AI风险库中的领域分类法相结合，构建了四个基于新闻的系统性风险因素。我估计了因素创新的贝塔值，并通过单变量投资组合分析和Fama-MacBeth回归检验定价情况。仅分类法映射的虚假信息因素（D3）被稳健地定价。其高减低贝塔投资组合每月产生0.49%-0.57%的阿尔法收益，且估计的D3风险价格在贝塔估计窗口、常规因素和行业控制、替代创新模型以及ChatGPT之前的子样本中均为正且统计显著。其他因素未被可靠定价，表明AI风险定价具有领域特异性。
英文摘要
This paper asks whether firms' exposures to news about different types of AI risk are priced in U.S. stock returns. Using AI and risk keywords, I identify 7,787 Wall Street Journal articles from January 2016 to December 2025. I combine latent Dirichlet allocation (LDA) with the Domain Taxonomy in the MIT AI Risk Repository to construct four news-based systematic risk factors. I estimate betas to factor innovations and test pricing with univariate portfolio analysis and Fama-MacBeth regressions. Only the taxonomy-mapped Misinformation factor (D3) is robustly priced. Its high-minus-low beta portfolio earns monthly alphas of 0.49%-0.57%, and the estimated D3 price of risk is positive and statistically significant across beta-estimation windows, conventional factor and industry controls, alternative innovation models, and the pre-ChatGPT subsample. The other factors are not reliably priced, indicating that AI-risk pricing is domain-specific.
