We develop a novel methodology for extracting information from option implied volatility (IV) surfaces for the cross-section of stock returns, using image recognition techniques from machine learning (ML). The predictive information we identify is essentially uncorrelated with most of 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 average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

I develop a dynamic general equilibrium model of exchange traded funds (ETFs) that accounts for the two-tier ETF market structure with both a centralized exchange (secondary market) and a creation/redemption mechanism (primary market) operating through market-making firms known as Authorized...

We calculate equilibria of dynamic double-auction markets in which agents are distinguished by their preferences and information. Over time, agents are privately informed by bids and offers. Investors are segmented into groups that differ with respect to characteristics determining information...

We study the market price of risk, the stock volatility and the hedging behavior in equilibrium of heterogeneous agents with arbitrary utility functions, consuming only at the end of the time horizon, and with the state variable following an arbitrary homogeneous diffusion process. We introduce...

We document that corporates in emerging markets borrow more in foreign currency when the local currency provides a better hedge in downturns. We develop an international corporate finance model in which firms facing adverse selection choose the foreign currency share of their debt. In the unique...