l 1 -Regularization in Portfolio Selection with Machine Learning

Stefania Corsaro, Valentina De Simone, Zelda Marino and Salvatore Scognamiglio
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Stefania Corsaro: Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
Valentina De Simone: Department of Mathematics and Physics, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Zelda Marino: Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
Salvatore Scognamiglio: Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy

Mathematics, 2022, vol. 10, issue 4, 1-15

Abstract: In this work, we investigate the application of Deep Learning in Portfolio selection in a Markowitz mean-variance framework. We refer to a l 1 regularized multi-period model; the choice of the l 1 norm aims at producing sparse solutions. A crucial issue is the choice of the regularization parameter, which must realize a trade-off between fidelity to data and regularization. We propose an algorithm based on neural networks for the automatic selection of the regularization parameter. Once the neural network training is completed, an estimate of the regularization parameter can be computed via forward propagation. Numerical experiments and comparisons performed on real data validate the approach.

Keywords: deep learning; multi-period portfolio optimization; l 1 -norm; split Bregman (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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