We show that labor market frictions are first-order for understanding credit markets. Wage growth and labor share forecast aggregate credit spreads and debt growth as well as or better than alternative predictors. They also predict credit risk and debt growth in a cross section of international firms. Finally, high labor share firms choose lower financial leverage. A model with labor market frictions and risky long-term debt can explain these findings, and produce large credit spreads despite realistically low default probabilities. This is because precommitted payments to labor make other committed payments (i.e., interest) riskier. (JEL D33, E23, E24, E25, E44, F23, G32)

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading on long-run stock price informativeness. Overall, our findings indicate that a significant number of investors use ChatGPT in ways that influence their trading decisions and market outcomes. Future research can investigate the mechanisms underlying these GenAI effects and the potential risks of using GenAI for trading.

Previous studies have identified a negative relation between firms’ hiring rates and future stock returns in the cross-section. We document that this relation is significantly steeper in industries that rely relatively more on high-skill workers than low-skill workers. A long-short portfolio sorted on firm-level hiring rate earns an average annual return of 8.6% in high-skill industries, and only 0.9% in low-skill industries. Moreover, this pattern is not explained by the standard CAPM. These findings are consistent with a neoclassical model with labor force heterogeneity and labor market frictions if it is more costly to replace high-skill than low-skill workers. Received August 14, 2015; editorial decision December 31, 2016 by Editor Leonid Kogan.