According to no-arbitrage, risk-adjusted returns should be unpredictable. Using several prominent factor models and a large cross-section of anomalies, we find that past pricing errors predict future risk-adjusted anomaly returns. We show that past pricing errors can be interpreted as deviations...

We explore the cross-section of factor returns using a sample of 150+ equity factors. Most factors exhibit a positive premium and a negative market beta in the long run. Factor themes with a clear positive beta, in particular low leverage and size, have no alpha after controlling for this beta...

This paper decomposes firm-specific monthly-varying Amihud (2002) illiquidity measure into two components: (i) systematic illiquidity; (ii) idiosyncratic illiquidity. While there is a positive and significant relationship between systematic illiquidity and one-month-ahead stock returns, the...

We examine the pricing of tail risk in international stock markets. We find that the tail risk of different countries is highly integrated. Introducing a new World Fear index, we find that local and global aggregate market returns are mainly driven by global tail risk rather than local tail...

We merge the literature on downside return risk and liquidity risk and introduce the concept of extreme downside liquidity (EDL) risks. The cross-section of stock returns reflects a premium if a stock's return (liquidity) is lowest at the same time when the market liquidity (return) is lowest....

We study the problem of detecting structural instability of factor strength in asset pricing models for financial returns. We allow for strong and weaker factors, in which the sum of squared betas grows at a rate equal to and slower than the number of test assets, respectively: this growth rate...

Many asset pricing theories treat the cross-section of returns volatility and correlations as two intimately related quantities driven by common factors, which hinders achieving a neat definition of a correlation premium. We formulate a model without factors, but with a continuum of securities...

I propose to forecast the market returns through its constituents. In contrast to the voluminous literature that concentrates on the predictive power of aggregate cross-sectional or macroeconomic predictors, I analyze the return predictability of sub-portfolios that compose the market portfolio....

We investigate the relation between downside beta and stock returns in a global context using more than 170 million daily return observations. Contrary to the findings in the U.S. equity market, we show that downside beta does not explain the cross-sectional differences in future and...

Abstract We predict cumulative stock returns over horizons from 1 month to 10 years using a tree-based machine learning approach. Cumulative stock returns are significantly predictable in the cross-section over all horizons. A hedge portfolio generates 250 bp/month at a 1 year horizon and 110...