Thought Leadership

This study investigates how firms engage in information-seeking lobbying to address trade policy uncertainty. I argue that lobbying enables firms to gain early insights into forthcoming tariff actions, allowing them to strategically stockpile products likely to be targeted. Using shipping records of US firms during the 2018 US–China trade war, I find that lobbying firms increased imports of soon-to-be-tariffed products before tariff lists were publicly released, compared to non-lobbying firms. This selective stockpiling pattern disappeared after tariff announcements. Further analysis shows that lobbying firms were less likely to request tariff exemptions for products they had preemptively stockpiled, suggesting that information-seeking lobbying during policy formulation provides an additional benefit by reducing the need for costly government engagement during the implementation phase.

We study whether access to local pollution information causes investors to make greener portfolio allocations, exploiting the rollout of air quality monitoring stations in India. Using a triple-differences framework on the trading records of 19 million investors, we show that retail investors’ holdings in “brown” stocks become more negatively related to local pollution after a nearby station appears. This effect is more pronounced on “alert” dates when air quality is reported to be harmful. The effect is strongest among tech-savvy investors likely “treated” by real-time pollution data, and younger investors, who may be more sensitive to environmental concerns.

We develop a transparent Bayesian framework to measure uncertainty in asset pricing models. By assigning a modified class of -priors to the risk prices of asset pricing factors, our method quantifies the trade-off between mean–variance efficiency and parsimony for asset pricing models to achieve high posterior probabilities. Model uncertainty is defined as the entropy of these model probabilities. We prove the model selection consistency property of our procedure, which is missing from the classic -priors. Acknowledging the possibility of omitting true asset pricing factors in real applications, we also characterize the maximum degree of contamination that the omitted factors can introduce to our model uncertainty measure. Empirically, we find that model uncertainty escalates during major market events and carries a significantly negative risk premium of approximately half the magnitude of the market. Positive shocks to model uncertainty predict persistent outflows from US equity funds and inflows to Treasury funds.