  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.
