We study pricing dynamics and risk-sharing in a market with rational investors and a Q-learning trader. The Q-learner's trading generates a feedback loop in prices: their demand for the risky security depends on their perceived benefit from trading, which in...

Generative AI has the potential to transform corporate innovation, but intellectual property (IP) created without sufficient human input is ineligible for protection by IP systems. We model a firm’s choice of AI versus human-capital use when investing in innovation, with...

We study markets in which traders acquire private information before submitting their schedules. We characterize conditions under which traders over-invest (respectively, under-invest) in information and trade excessively (respectively, insufficiently) on their private signals. These inefficiencies arise from a novel interaction...

Trading relationships in over-the-counter (OTC) markets are persistent and often exclusive, despite technological advances that have expanded clients' access to dealers. We rationalize this pattern in a model where trading relationships allow dealers to learn their clients' trading motives from...

We develop a theoretical framework that formalizes the conflicts of interest arising in continuation vehicles (CVs), in which general partners (GPs) transfer portfolio companies from an existing fund to a new vehicle they continue to manage. While CVs can enhance...

We develop a Diamond-Dybvig-style model in which a non-bank firm issues tokens backed by its future services. Consumers face uncertain liquidity demand and costly ex-post borrowing. Tokens are partially liquid–they provide liquidity for the firm’s service but not other consumption
goods,...