The supervised portfolios approach is an effective asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. Yet, supervised learning algorithms are often seen as opaque, which undermines trust in those models, thereby limiting their...

We propose an asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. In contrast to the traditional approaches, the machine is able to learn risk measures, preferences and constraints beyond simple expected returns, within a flexible,...

We propose a linearization of rule-based algorithms that reveals the most important interactions between characteristics and macroeconomic variables when explaining future stock returns. Our results suggest that the two types of predictors are intertwined, which implies that the relationships...

We propose a linearization of rule-based algorithms that reveals the most important interactions between characteristics and macroeconomic variables when explaining future stock returns. Our results suggest that the two types of predictors are intertwined, which implies that the relationships...