War Discourse and the Cross Section of Expected Stock Returns

• 成果类型: Article; Early Access
• 署名作者: Hirshleifer, David; Mai, Dat; Pukthuanthong, Kuntara
• 署名单位: University of Southern California; University of Missouri System; University of Missouri Columbia
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13482
• 发表日期: 2025
• 关键词: rare disasters; asset; RISK; MARKET; volatility; arbitrage; skewness; WORST

摘要

A war-related factor model derived from textual analysis of media news reports explains the cross section of expected stock returns. Using a semisupervised topic model to extract discourse topics from 7,000,000 New York Times stories spanning 160 years, the war factor predicts the cross section of returns across test assets derived from both traditional and machine learning construction techniques, and spanning 138 anomalies. Our findings are consistent with assets that are good hedges for war risk receiving lower risk premia, or with assets that are more positively sensitive to war prospects being more overvalued. The return premium on the war factor is incremental to standard effects.