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...

This paper tests the efficiency of macroeconomic forecasts, contributing to the existing literature using a rolling-event approach. We construct a monthly economic surprises index, aggregating several macroeconomic news surprises for the nine largest economic areas (G9), which we further analyze...

In this paper, we forecast industry returns out-of-sample using the cross-section of book-to-market ratios and investigate whether investors can exploit this predictability in portfolio allocation. Cash-flow and return forecasting regressions show that cross-industry book-to-market ratios...

We find out-of-sample predictability of commodity futures excess returns using forecast combinations of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean-variance investor....

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...

This paper aims to implement a portfolio optimization strategy considering two fundamental aspects: the empirical regularities observed in the time series of stock returns, and the views of portfolio managers about these regularities. From an analytical point of view, all the results are...

For many multi-factor asset pricing models proposed in the recent literature, their implied tang-ency portfolios have substantially higher sample Sharpe ratios than that of the value-weighted market portfolio. In contrast, such high sample Sharpe ratio is rarely delivered by professional fund...

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...