Distributional Robust Portfolio Construction based on Investor Aversion

TL;DR

This paper develops a distributionally robust portfolio optimization model that accounts for investor aversion and ambiguity in asset return distributions, using Wasserstein balls to improve asset allocation under uncertainty.

Contribution

It introduces a novel robust portfolio model considering investor aversion and ambiguity, solved via a hybrid algorithm for large-scale problems.

Findings

The model outperforms traditional strategies in empirical tests.

The hybrid algorithm improves computational efficiency.

Incorporating investor aversion enhances portfolio robustness.

Abstract

In behavioral finance, aversion affects investors' judgment of future uncertainty when profit and loss occur. Considering investors' aversion to loss and risk, and the ambiguous uncertainty characterizing asset returns, we construct a distributional robust portfolio model (DRP) under the condition that the distribution of risky asset returns is unknown. Specifically, our objective is to find an optimal portfolio of assets that maximizes the worst-case utility level on the Wasserstein ball, which is centered on the empirical distribution of sample returns and the radius of the ball quantifies the investor's ambiguity level. The model is also formulated as a mixed-integer quadratic programming problem with cardinality constraints. In addition, we propose a hybrid algorithm to improve the efficiency of the solution and make it more suitable for large-scale problems. The distributional robust portfolio model considering aversion is empirically tested for superior performance in asset allocation, and we also compare common asset allocation strategies to further enhance the credibility of the portfolio.