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