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...

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...

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...

This study investigates the existence of a cross-sectional relationship between several index characteristics and future returns on country-industry indexes in six regions. The results from portfolio sorts and index-level cross-sectional regressions across regions collectively show that the...

The relation between idiosyncratic risk and stock returns is currently a topic of debate in the academic literature. So far the evidence regarding the relation is mixed. This study aims to investigate the cross-sectional relation between idiosyncratic risk and stock returns in the Indian stock...

Given the process {X(t), t in T}, the definition of self-affinity is reformulated in terms of diameter of the space of the rescaled pdf's of X(t). Two necessary conditions are deduced which contribute to discriminate uniscaling processes. Furthermore, by properly choosing the distance, the...

We investigate the cross-sectional relationship between stock returns and a number of measures of option-implied beta. Using portfolio analysis, we show that the method proposed by Buss and Vilkov (2012) leads to a stronger relationship between implied beta and stock returns than other...

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...