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l 1 -Regularization in Portfolio Selection with Machine Learning

Author

Listed:
  • Stefania Corsaro

    (Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
    These authors contributed equally to this work.)

  • Valentina De Simone

    (Department of Mathematics and Physics, University of Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
    These authors contributed equally to this work.)

  • Zelda Marino

    (Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
    These authors contributed equally to this work.)

  • Salvatore Scognamiglio

    (Department of Management and Quantitative Studies, Parthenope University of Naples, 80133 Naples, Italy
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2022. "l 1 -Regularization in Portfolio Selection with Machine Learning," Mathematics, MDPI, vol. 10(4), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:540-:d:745529
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    References listed on IDEAS

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    1. Cui, Xiangyu & Gao, Jianjun & Li, Xun & Li, Duan, 2014. "Optimal multi-period mean–variance policy under no-shorting constraint," European Journal of Operational Research, Elsevier, vol. 234(2), pages 459-468.
    2. Michael Ho & Zheng Sun & Jack Xin, 2015. "Weighted Elastic Net Penalized Mean-Variance Portfolio Design and Computation," Papers 1502.01658, arXiv.org, revised Oct 2015.
    3. Stefania Corsaro & Valentina De Simone & Zelda Marino & Francesca Perla, 2020. "$$l_1$$ l 1 -Regularization for multi-period portfolio selection," Annals of Operations Research, Springer, vol. 294(1), pages 75-86, November.
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    7. Chen, Zhi-ping & Li, Gang & Guo, Ju-e, 2013. "Optimal investment policy in the time consistent mean–variance formulation," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 145-156.
    8. Stefania Corsaro & Valentina Simone, 2019. "Adaptive $$l_1$$ l 1 -regularization for short-selling control in portfolio selection," Computational Optimization and Applications, Springer, vol. 72(2), pages 457-478, March.
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