Much of the extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

We study optimal securitization in the presence of an initial moral hazard. A financial intermediary creates and then sells to outside investors defaultable assets, whose default risk is determined by the unobservable costly effort exerted by the intermediary. We calculate the optimal contract for any given effort level and show the natural emergence of extreme punishment for defaults, under which investors stop paying the intermediary after the first default. With securitization contracts optimally designed, we find securitization improves the intermediary's screening incentives. Furthermore, the equilibrium effort level and the surplus converge to their first best levels with sufficiently many assets.

We develop a continuous-time general equilibrium model with intermediaries at the heart of international financial markets. Global intermediaries bargain with households and extract rents from providing access to foreign claims. By tilting state prices, intermediaries’ market power breaks monetary neutrality and makes international risk-sharing inefficient. Despite having zero net positions, markups charged by intermediaries significantly distort international asset prices, affecting exchange rate dynamics and their response to shocks. Our model can reproduce patterns consistent with several well-known exchange rate puzzles, such as deviations from uncovered and covered interest parity. All equilibrium quantities are derived in closed form, allowing us to pin down the underlying economic mechanisms explicitly.

We develop a dynamic model of investment, financing, and cash management decisions in which investment is lumpy and firms face capital supply uncertainty. We characterize optimal policies explicitly, demonstrate that smooth-pasting conditions may not guarantee optimality, and show that firms may not follow standard single barrier policies. In the model, firms with high investment costs differ in their behaviors from firms with low investment costs, financing policy does not follow a strict pecking order, and the optimal payout policy may feature several regions with both incremental and lumpy dividend payments.

We solve for the equilibrium dynamics of Information sharing in a large population Each agent is endowed with signals regarding the likely outcome of a random variable of common concern Individuals choose the effort with which they search for others from whom they can gather additional information. When two agents meet, they share their information The Information gathered is further shared at subsequent meetings, and so on. Equilibria exist in which agents search maximally until they acquire sufficient information precision and then search minimally A tax whose proceeds are used to Subsidize the costs of search improves information sharing and can, in some cases, increase welfare on the other hand, endowing agents with public signals reduces Information sharing and can, in some cases, decrease welfare

This paper presents an equilibrium model in a pure exchange economy when investors have three possible sources of heterogeneity. Investors may differ in their beliefs, in their level of risk aversion, and in their time preference rate. The authors study the impact of investors’ heterogeneity on equilibrium properties and, in particular, on the consumption shares, the market price of risk, the risk-free rate, the bond prices at different maturities, the stock price and volatility as well as on the stock's cumulative returns, and optimal portfolio strategies. The authors relate the heterogeneous economy with the family of associated homogeneous economies with only one class of investors. Cross-sectional as well as long-run properties are analyzed.

We propose a new asset pricing framework in which all securities' signals predict each individual return. While the literature focuses on securities' own‐signal predictability, assuming equal strength across securities, our framework includes cross‐predictability—leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a “prediction matrix,” which we call “principal portfolios.” Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out‐of‐sample alphas to standard factors in several data sets.