Sparse portfolio selection via the sorted ℓ1-Norm

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Sparse portfolio selection via the sorted ℓ1-Norm. / Kremer, Philipp J.; Lee, Sangkyun; Bogdan, Małgorzata; Paterlini, Sandra.

I: Journal of Banking and Finance, Vol. 110, 105687, 2020.

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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Kremer, Philipp J. ; Lee, Sangkyun ; Bogdan, Małgorzata ; Paterlini, Sandra. / Sparse portfolio selection via the sorted ℓ1-Norm. I: Journal of Banking and Finance. 2020 ; Vol. 110.

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TY - JOUR

T1 - Sparse portfolio selection via the sorted ℓ1-Norm

AU - Kremer, Philipp J.

AU - Lee, Sangkyun

AU - Bogdan, Małgorzata

AU - Paterlini, Sandra

PY - 2020

Y1 - 2020

N2 - We introduce a financial portfolio optimization framework that allows to automatically select the relevant assets and estimate their weights by relying on a sorted ℓ1-Norm penalization, henceforth SLOPE. To solve the optimization problem, we develop a new efficient algorithm, based on the Alternating Direction Method of Multipliers. SLOPE is able to group constituents with similar correlation properties, and with the same underlying risk factor exposures. Depending on the choice of the penalty sequence, our approach can span the entire set of optimal portfolios on the risk-diversification frontier, from minimum variance to the equally weighted. Our empirical analysis shows that SLOPE yields optimal portfolios with good out-of-sample risk and return performance properties, by reducing the overall turnover, through more stable asset weight estimates. Moreover, using the automatic grouping property of SLOPE, new portfolio strategies, such as sparse equally weighted portfolios, can be developed to exploit the data-driven detected similarities across assets.

AB - We introduce a financial portfolio optimization framework that allows to automatically select the relevant assets and estimate their weights by relying on a sorted ℓ1-Norm penalization, henceforth SLOPE. To solve the optimization problem, we develop a new efficient algorithm, based on the Alternating Direction Method of Multipliers. SLOPE is able to group constituents with similar correlation properties, and with the same underlying risk factor exposures. Depending on the choice of the penalty sequence, our approach can span the entire set of optimal portfolios on the risk-diversification frontier, from minimum variance to the equally weighted. Our empirical analysis shows that SLOPE yields optimal portfolios with good out-of-sample risk and return performance properties, by reducing the overall turnover, through more stable asset weight estimates. Moreover, using the automatic grouping property of SLOPE, new portfolio strategies, such as sparse equally weighted portfolios, can be developed to exploit the data-driven detected similarities across assets.

KW - Alternating direction method of multipliers

KW - Markowitz model

KW - Portfolio management

KW - Sorted ℓ-Norm regularization

U2 - 10.1016/j.jbankfin.2019.105687

DO - 10.1016/j.jbankfin.2019.105687

M3 - Article

VL - 110

JO - Journal of Banking and Finance

JF - Journal of Banking and Finance

SN - 1872-6372

M1 - 105687

ER -