The added value of smart beta indices is known to be explained by exposures to established factor premiums, but does that make these indices suitable for implementing a factor investing strategy? This paper finds that the amount of factor exposure provided by popular smart beta strategies...

The size premium has failed to materialize since its discovery almost forty years ago, but is seemingly revived when controlling for quality-versus-junk exposures. This paper aims to resolve whether there exists a distinct size premium that can be captured in reality. For the US we confirm that...

Conventional short-term reversal strategies exhibit dynamic exposures to the Fama and French (1993) factors. We develop a novel reversal strategy based on residual stock returns that does not exhibit these exposures and consequently earns risk-adjusted returns that are twice as large as those of...

Conventional short-term reversal strategies exhibit dynamic exposures to the Fama and French (1993) factors. We develop a novel reversal strategy based on residual stock returns that does not exhibit these exposures and consequently earns risk-adjusted returns that are twice as large as those of...

The equity risk premium is generally considered to be a reward that investors earn on top of the prevailing risk-free return, implying that, all else equal, total expected stock returns should increase with the level of the risk-free return. We examine whether this notion is true using long-term...

We examine how divesting from fossil fuel stocks, as announced by several large institutional investors, affects the systematic risk exposures of an equity portfolio. We find that fossil fuel stocks exhibit a highly significant positive exposure towards changes in the oil price. Consistent with...

We examine the effects of incorporating a potential tax on carbon emissions into a value investment strategy. We show that in a portfolio optimization problem, a carbon tax at the stock level is mathematically equivalent to a carbon constraint at the portfolio level. Using this insight we derive...

The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this paper, we discuss the promises and...