$$l_1$$ l 1 -Regularization for multi-period portfolio selection

Stefania Corsaro (), Valentina De Simone (), Zelda Marino () and Francesca Perla ()
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Stefania Corsaro: University of Naples “Parthenope”
Valentina De Simone: University of Campania “Luigi Vanvitelli”
Zelda Marino: University of Naples “Parthenope”
Francesca Perla: University of Naples “Parthenope”

Annals of Operations Research, 2020, vol. 294, issue 1, No 5, 75-86

Abstract: Abstract In this work we present a model for the solution of the multi-period portfolio selection problem. The model is based on a time consistent dynamic risk measure. We apply $$l_1$$ l 1 -regularization to stabilize the solution process and to obtain sparse solutions, which allow one to reduce holding costs. The core problem is a nonsmooth optimization one, with equality constraints. We present an iterative procedure based on a modified Bregman iteration, that adaptively sets the value of the regularization parameter in order to produce solutions with desired financial properties. We validate the approach showing results of tests performed on real data.

Keywords: Portfolio optimization; Time consistency; $$l_1$$ l 1 norm; Constrained optimization; 91G10; 90C30; 65K05 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10479-019-03308-w

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