期刊
JOURNAL OF BANKING & FINANCE
卷 110, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jbankfin.2019.105687
关键词
Portfolio management; Markowitz model; Sorted l(1)-Norm regularization; Alternating direction method of multipliers
资金
- Polish National Center of Science, Poland [2016/23/B/ST1/00454]
- ICT COST Action from CRoNoS [IC1408]
- National Research Foundation of Korea (NRF) - Korea government (MSIP
- Ministry of Science, ICT & Future Planning) [2017R1C1B5018367]
- National Research Foundation of Korea [2017R1C1B5018367] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
We introduce a financial portfolio optimization framework that allows to automatically select the relevant assets and estimate their weights by relying on a sorted El-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. (C) 2019 Elsevier B.V. All rights reserved.
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