Systematic, rules-based investment strategies are where academia and practice are currently interacting strongly. My objective in this editorial is to offer some thoughts on research on systematic investing, including three articles in this issue, that can provide significant practical benefits...

We present a methodology that uses supervised learning techniques, in particular, 'random forests' and 'gradient boosting trees' to assess the probability of success on trade ideas. We use features related to the underlying stock's characteristics, the characteristics of the contributor of the...

This article addresses the problem of portfolio construction in the context of efficient hedge fund investments replication. We propose a modification to the standard à la Sharpe “style analysis” by introducing a constraint on the asset weights 1-norm and 2-norm. This constraint regularizes...

We develop a hybrid model that relies on the nonlinear classification Decision Tree (DT) approach but also on multivariate predictive regressions to aid implement a size rotation strategy in the U.S. equity markets. Our investment prediction is derived with a two-stage algorithm. In the first...