A new behavioral model for portfolio selection using the Half-Full/Half-Empty approach

We focus on a behavioral model that has been recently proposed in the literature, whose rationale can be traced back to the Half-Full/Half-Empty glass metaphor. More precisely, we generalize the Half-Full/Half-Empty approach to the context of positive and negative lotteries and give financial and behavioral interpretations of the Half-Full/Half-Empty parameters. We develop a portfolio selection model based on the Half-Full/Half-Empty strategy, resulting in a nonconvex optimization problem, which, nonetheless, is proven to be equivalent to an alternative Mixed-Integer Linear Programming formulation. Based on three real-world datasets, we obtain empirical validation of the theoretical properties of the Half-Full/Half-Empty model, and the computational results highlight the versatility of our approach when varying its defining parameter values. • Generalization of the Half-Full/Half-Empty approach for mixed lotteries. • Financial and behavioral interpretations of the Half-Full/Half-Empty parameters. • Portfolio selection model based on the Half-Full/Half-Empty strategy. • MILP formulation of the HF/HE portfolio selection model. • Promising out-of-sample performance on real-world data of the proposed approach.