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We discuss sparse portfolio optimization in continuous time.
Optimization objective is to maximize an expected utility as in the
classical Merton problem but with regularizing sparsity constraints.
Such constraints aim for asset allocations that contain only few assets or
that deviate only in few coordinates from a reference benchmark allocation.
With a focus on growth optimization, we show empirical results for various
portfolio selection strategies with and without sparsity constraints,
investigating different portfolios of stock indicies, several performance
measures and adaptive methods to select the regularization parameter.
Sparse optimal portfolios are less sensitive to estimation
errors and performance is superior to portfolios without sparsity
constraints in reality, where estimation risk and model uncertainty must
not be ignored.