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A Dantzig-Type Large Portfolio Optimization Model and Its Efficient Fitting Algorithm.

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Τίτλος: A Dantzig-Type Large Portfolio Optimization Model and Its Efficient Fitting Algorithm.
Συγγραφείς: Gou, Zikang1 (AUTHOR), Hu, Haonan2 (AUTHOR) huhn1014@mail.bnu.edu.cn, Yang, Hanming1 (AUTHOR), Yang, Songshan1 (AUTHOR)
Πηγή: Journal of Business & Economic Statistics. Mar2026, p1-60. 60p. 1 Illustration.
Θεματικοί όροι: *Portfolio management (Investments), *Parallel programming, *Simulation methods & models, *Financial databases, Regularization parameter
Περίληψη: Given the cyclical nature of market volatility and the increasing complexity of global financial systems, developing effective strategies for large-scale portfolio optimization is of critical importance. In this work, we propose a novel Dantzig-type portfolio optimization (DPO) model designed to help investors navigate these challenges and optimize their portfolios effectively. The model separately incorporates ℓ1 and folded concave penalties, enabling the direct estimation of optimal portfolio weights while enforcing the sum constraint and accommodating both long and short positions. We establish the desired theoretical properties under mild regularity conditions, and introduce efficient parallel computing algorithms based on asset-splitting. Through extensive simulation studies, we investigate the superior effectiveness and efficiency of the DPO model and proposed algorithms. Furthermore, we illustrate the usefulness of the model by applying it to U.S. stock market datasets, including the constituent stocks of both S&P 500 and Russell 2000 indices. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Business & Economic Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Business Source Index
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  Data: A Dantzig-Type Large Portfolio Optimization Model and Its Efficient Fitting Algorithm.
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– Name: Abstract
  Label: Abstract
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  Data: Given the cyclical nature of market volatility and the increasing complexity of global financial systems, developing effective strategies for large-scale portfolio optimization is of critical importance. In this work, we propose a novel Dantzig-type portfolio optimization (DPO) model designed to help investors navigate these challenges and optimize their portfolios effectively. The model separately incorporates ℓ1 and folded concave penalties, enabling the direct estimation of optimal portfolio weights while enforcing the sum constraint and accommodating both long and short positions. We establish the desired theoretical properties under mild regularity conditions, and introduce efficient parallel computing algorithms based on asset-splitting. Through extensive simulation studies, we investigate the superior effectiveness and efficiency of the DPO model and proposed algorithms. Furthermore, we illustrate the usefulness of the model by applying it to U.S. stock market datasets, including the constituent stocks of both S&P 500 and Russell 2000 indices. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Business & Economic Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/07350015.2026.2639146
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 60
        StartPage: 1
    Subjects:
      – SubjectFull: Portfolio management (Investments)
        Type: general
      – SubjectFull: Parallel programming
        Type: general
      – SubjectFull: Simulation methods & models
        Type: general
      – SubjectFull: Financial databases
        Type: general
      – SubjectFull: Regularization parameter
        Type: general
    Titles:
      – TitleFull: A Dantzig-Type Large Portfolio Optimization Model and Its Efficient Fitting Algorithm.
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            NameFull: Gou, Zikang
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            NameFull: Hu, Haonan
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            NameFull: Yang, Hanming
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            NameFull: Yang, Songshan
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          Dates:
            – D: 05
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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