We show that in recent years global factor models have been catching up significantly with their local counterparts in terms of explanatory power (R2) for international stock returns. This catch-up is driven by a rise in global factor betas, not a rise in factor volatilities, suggesting that the...

Average skewness, which is defined as the average of monthly skewness values across firms, performs well at predicting future market returns. This result still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. We also find that average...

We build a macroeconomic model for Switzerland, the Euro Area, and the USA that drives the dynamics of several asset classes and the liabilities of a representative Swiss (defined-contribution) pension fund. This encompassing approach allows us to generate correlations between returns on assets...

In this paper, we document evidence that downside betas tend to comove more than upside betas during a financial crisis, but upside betas tend to comove more than the downside betas during financial booms. We find that the asymmetry between Downside-Beta Comovement and Upside-Beta Comovement is...

We present a new theory of asset pricing and portfolio choices under asymmetric reasoning, contrast the predictions with those under asymmetric information, and present experimental evidence in favor of our theory. The Efficient Markets Hypothesis and its formal foundation, the Rational...

I study short-sale constraints in a market with asymmetric information. I offer a novel approach endogenizing short-sale constraints by including an asset-borrowing market in my model. Short-sellers have to borrow an asset and therefore reveal information to a lender. The lender trades on her...

I study the effects of short sale constraints in a rational framework with asymmetric information. I consider the cases of Bernoulli-distributed (à la Glosten and Milgrom) and continuously distributed (à la Kyle) liquidation values, and focus on the latter case.In this case my model is able to...

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...

I model a market in which a trader with superior information about an asset is subject to careful scrutiny by another agent who immediately observes the trading decisions of the informed agent with some noise and engages in (klepto)parasitic behavior by imicking the informed trader and trading...

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...