Sparse Portfolio Selection via

In this paper, we propose 'p-norm regularized models to seek near-optimal sparse portfolios. These sparse solutions reduce the complexity of portfolio implemen- tation and management. Theoretical results are established to guarantee the sparsity of the second-order KKT points of the 'p-norm regularized models. More interest- ingly, we present a theory that relates sparsity of the KKT points with Projected correlation and Projected Sharpe ratio. We also design an interior point algorithm to obtain an approximate second-order KKT solution of the 'p-norm models in polyno- mial time with a xed error tolerance, and then test our 'p-norm modes on SP A combined '2-'p model is able to produce extremely high performing portfolios that exceeded the 1/N strategy and all '1 and '2 regularized portfolios.

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