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We investigate an expected utility maximization problem under modeluncertainty in a one-period financial market. We capture model uncertainty byreplacing the baseline model $\mathbb{P}$ with an adverse choice from aWasserstein ball of radius $k$ around $\mathbb{P}$ in the space of probabilitymeasures and consider the corresponding Wasserstein distributionally robustoptimization problem. We show that optimal solutions converge to a strategywith minimal norm when uncertainty is increasingly large, i.e. when the radius$k$ tends to infinity.

Strategies with minimal norm are optimal for expected utility maximization under high model ambiguity