War Risk: Time Series and Cross-sectional Evidence from the Stock and Bond Markets
140 Pages Posted: 6 Sep 2022 Last revised: 25 Oct 2022
Date Written: August 16, 2022
We employ a semi-supervised topic model to extract the rare disaster risks and economic narratives from 7,000,000 NYT articles over 160 years. Our approach addresses the look-ahead bias and changes in semantics. War positively predicts market return in- and out-of-sample, while the economic narratives only predict in-sample. The predictability of War increases over time and is robust when extracted from WSJ. War as a solo factor prices characteristics-sorted portfolios with a negative risk premium and outperform some multifactor benchmarks when pricing machine learning-based nonlinear portfolios with an R2 of 54%. Our study lends support to the time-varying disaster risk model.
Keywords: War, disaster risk, topic modeling, narratives, War factors
JEL Classification: G00, G1
Suggested Citation: Suggested Citation