We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading...

“Buy Now, Pay Later” (BNPL) and other forms of consumer credit create a wedge between consumption and payments. We introduce this wedge into a standard consumption-based asset pricing model (CCAPM). In equilibrium, the pricing kernel equals the marginal utility of consumption divided by 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...

The core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and...

We derive closed form expressions for equilibrium asset prices and liquidity in an economy populated by a finite number of large, strategic, risk averse investors. The model allows for arbitrary risk preferences, any number of assets, and an arbitrary distribution of asset payoffs. In...

We consider an economy populated by CARA investors who trade, accounting for their price impact, multiple risky assets with arbitrary distributed payoffs. We propose a constructive solution method: finding the equilibrium reduces to solving a linear ordinary differential equation. With market...

We develop an overlapping generations model in which investors differ in their investment horizons. In equilibrium, the intertemporal hedging demand of longer horizon investors leads to a two-factor capital asset pricing model (CAPM) in which risk premiums are determined by both the market...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...