We develop a rational model of trading behavior in which the agents gradually learn about their ability to trade, and exit after poor trading performance. We demonstrate that it is optimal for experienced traders to "procrastinate" and postpone exit even after bad results. We embed this "optimal...

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...

A random variable dominates another random variable with respect to the covariance order if the covariance of any two monotone increasing functions of this variable is smaller. We characterize completely the covariance order, give strong sufficient conditions for it, present a number of examples...

We study survival, price impact and portfolio impact in heterogeneous economies. We show that, under the equilibrium risk-neutral measure, long-run price impact is in fact equivalent to survival, whereas longrun portfolio impact is equivalent to survival under an agent-specific, wealth-forward...

Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and...

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...

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...