We investigate a discrete-time mean-risk portfolio selection problem, where risk is measured by the conditional value-at-risk (CVaR). By embedding this time-inconsistent problem into a family of expected utility maximization problems with a piecewise linear utility function, we solve the problem...

We introduce the concept of forward rank-dependent performance criteria, extending the original notion to forward criteria that incorporate probability distortions. A fundamental challenge is how to reconcile the time-consistent nature of forward performance criteria with the time-inconsistency...

We study the implications of various models of reference point formation on optimal decision making in the context of portfolio optimization under loss aversion. If the reference point is exogenously given, then the predictions of any such model crucially depend on the choice of the reference...

Any robo-advisor needs to decide on a framework to model the preferences of its investors over uncertain outcomes. As of today, most robo-advisors model their investors as mean-variance optimizers. While the mean-variance framework is intuitive and optimal investment strategies have been derived...

We introduce a model for portfolio selection with an extendable investment universe where the agent faces a trade-off between exploiting existing and exploring for new investment opportunities. An agent with mean-variance preferences starts with an existing investment universe consisting of a...