We calculate learning rates when agents are informed through public and private observation of other agents' actions. We characterize the evolution of the distribution of posterior beliefs. If the private learning channel is present, convergence of the distribution of beliefs to the...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance---in terms of SDF Sharpe ratio and test asset pricing errors---is improving in model parameterization (or "complexity''). Our empirical findings verify the...

We introduce a new channel through which US money market funds (MMFs) affect the pricing of near-money assets and measured convenience yields. Our theoretical model reveals that MMFs' strategic interactions create frictions that are exacerbated by T-bill market illiquidity. Using instrumental...

Firms in emerging markets borrow more in foreign currency when the local currency actually provides a better hedge in downturns. Motivated by this fact, we develop an international corporate finance model in which firms facing adverse selection choose the foreign currency share of their debt. In...

We introduce artificial intelligence pricing theory (AIPT). In contrast with the APT's foundational assumption of a low dimensional factor structure in returns, the AIPT conjectures that returns are driven by a large number of factors. We first verify this conjecture empirically and show that...