Gill Segal, University of North Carolina-Chapel Hill
Chao Ying, Chinese University of Hong Kong
Abstract: Using minute-level wearable-device data on 51,191 adults from the National Institutes of Health (NIH), covering over 19 million person-nights, we show that sleep quality is a high-frequency biomarker of macroeconomic and financial uncertainty. Positive shocks to uncertainty reduce deep sleep and sleep efficiency for several nights-a "wake-and-see" effect that complements the classic "wait-and-see" channel. Heterogeneity is economically meaningful: effects are larger in higher-income areas, yet comparable across race, gender, and education. Finally, nights of unusually poor sleep-net of environmental or time-zone effects-predict lower next-day market liquidity and weaker opening-hour equity returns. Conversely, when worse-than-expected macro news is released before the market opens, better sleep is associated with more negative earlysession returns-consistent with improved market efficiency. These results connect uncertainty to a measurable human capital cost and reveal a two-way link between nightly physiology and market conditions.
Discussant: Ryan Israelsen, Michigan State University
Abstract: We study the asset pricing implications of geopolitical tensions using nearly 100 years of data. Leveraging widely adopted news-based geopolitical risk indices, we find that geopolitical threats (GPT) and acts (GPA) have markedly different effects. GPT aligns closely with geopolitical risk perceptions and decisions of investors and firms. Consequently, GPT is priced across individual US stocks, equity anomalies, international equity and bond indices, and it forecasts country-level equity premia. In contrast, GPA exhibits weaker and less stable links to the beliefs and decisions of investors and firms as well as to variation in risk premia across assets and over time. Importantly, our results are incremental to existing news-based indices of macro-financial uncertainty, including those capturing war-related discourse and economic or trade policy risk. Overall, our findings underscore the importance of forward-looking measures like GPT for understanding how news-based uncertainty affects investment decisions and asset prices.
Discussant: Jiatao Liu, Xi'an Jiaotong-Liverpool University
Abstract: We explore how the opioid crisis exposure affects firm downside tail risks implied from equity options. Using a large sample of public firms from 1999 to 2020, we find that firms headquartered in regions with higher opioid death rates face higher downside tail risks, i.e., the cost of protection against left tail risks is higher. The effects are reversed following exogenous anti-opioid legislation, supporting a causal interpretation. Further analysis shows that the opioid crisis heightens firm risk by lowering labor productivity. We document greater impact among firms with greater reliance on labor, limited local labor supply, and lower workplace safety.
Discussant: Giang Nguyen, Pennsylvania State University
Abstract: This paper examines how unauthorized immigration affects the fiscal health of local governments. We isolate immigration to the U.S. driven by social, economic, and political conditions in countries of origin. We predict destination county immigration using a shift-share instrument based on pre-existing population distributions. In areas with structurally tight labor markets, unauthorized immigration explains lower municipal bond yields. Areas with typical labor market conditions experience higher yields, as do areas with “sanctuary” status. These effects accompany increased unemployment rates and expenditures on public amenities, including welfare assistance, construction, education, and law enforcement. These expenditures are not offset by higher tax revenues.
