IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v56y2013i4p1409-1423.html
   My bibliography  Save this article

Convex relaxations and MIQCQP reformulations for a class of cardinality-constrained portfolio selection problems

Author

Listed:
  • X. Cui
  • X. Zheng
  • S. Zhu
  • X. Sun

Abstract

In this paper we investigate a class of cardinality-constrained portfolio selection problems. We construct convex relaxations for this class of optimization problems via a new Lagrangian decomposition scheme. We show that the dual problem can be reduced to a second-order cone program problem which is tighter than the continuous relaxation of the standard mixed integer quadratically constrained quadratic program (MIQCQP) reformulation. We then propose a new MIQCQP reformulation which is more efficient than the standard MIQCQP reformulation in terms of the tightness of the continuous relaxations. Computational results are reported to demonstrate the tightness of the SOCP relaxation and the effectiveness of the new MIQCQP reformulation. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • X. Cui & X. Zheng & S. Zhu & X. Sun, 2013. "Convex relaxations and MIQCQP reformulations for a class of cardinality-constrained portfolio selection problems," Journal of Global Optimization, Springer, vol. 56(4), pages 1409-1423, August.
  • Handle: RePEc:spr:jglopt:v:56:y:2013:i:4:p:1409-1423
    DOI: 10.1007/s10898-012-9842-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10898-012-9842-2
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10898-012-9842-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gordon J. Alexander & Alexandre M. Baptista, 2004. "A Comparison of VaR and CVaR Constraints on Portfolio Selection with the Mean-Variance Model," Management Science, INFORMS, vol. 50(9), pages 1261-1273, September.
    2. Dimitris Bertsimas & Romy Shioda, 2009. "Algorithm for cardinality-constrained quadratic optimization," Computational Optimization and Applications, Springer, vol. 43(1), pages 1-22, May.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. P. Bonami & M. A. Lejeune, 2009. "An Exact Solution Approach for Portfolio Optimization Problems Under Stochastic and Integer Constraints," Operations Research, INFORMS, vol. 57(3), pages 650-670, June.
    5. Martin R. Young, 1998. "A Minimax Portfolio Selection Rule with Linear Programming Solution," Management Science, INFORMS, vol. 44(5), pages 673-683, May.
    6. William F. Sharpe, 1963. "A Simplified Model for Portfolio Analysis," Management Science, INFORMS, vol. 9(2), pages 277-293, January.
    7. Pierre Bonami & Miguel A. Lejeune, 2009. "An Exact Solution Approach for Integer Constrained Portfolio Optimization Problems Under Stochastic Constraints," Post-Print hal-00421756, HAL.
    8. Pardalos, Panos M & Sandstrom, Mattias & Zopounidis, Costas, 1994. "On the Use of Optimization Models for Portfolio Selection: A Review and Some Computational Results," Computational Economics, Springer;Society for Computational Economics, vol. 7(4), pages 227-244.
    9. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Janusz Miroforidis, 2021. "Bounds on efficient outcomes for large-scale cardinality-constrained Markowitz problems," Journal of Global Optimization, Springer, vol. 80(3), pages 617-634, July.
    2. Xiaojin Zheng & Yutong Pan & Xueting Cui, 2018. "Quadratic convex reformulation for nonconvex binary quadratically constrained quadratic programming via surrogate constraint," Journal of Global Optimization, Springer, vol. 70(4), pages 719-735, April.
    3. Jize Zhang & Tim Leung & Aleksandr Aravkin, 2018. "A Relaxed Optimization Approach for Cardinality-Constrained Portfolio Optimization," Papers 1810.10563, arXiv.org.
    4. Zhi-Long Dong & Fengmin Xu & Yu-Hong Dai, 2020. "Fast algorithms for sparse portfolio selection considering industries and investment styles," Journal of Global Optimization, Springer, vol. 78(4), pages 763-789, December.
    5. Xiaojin Zheng & Yutong Pan & Zhaolin Hu, 2021. "Perspective Reformulations of Semicontinuous Quadratically Constrained Quadratic Programs," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 163-179, January.
    6. Carina Moreira Costa & Dennis Kreber & Martin Schmidt, 2022. "An Alternating Method for Cardinality-Constrained Optimization: A Computational Study for the Best Subset Selection and Sparse Portfolio Problems," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 2968-2988, November.
    7. Xiaojin Zheng & Xiaoling Sun & Duan Li, 2014. "Improving the Performance of MIQP Solvers for Quadratic Programs with Cardinality and Minimum Threshold Constraints: A Semidefinite Program Approach," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 690-703, November.
    8. Xingmei Li & Yaxian Wang & Qingyou Yan & Xinchao Zhao, 2019. "Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility," Fuzzy Optimization and Decision Making, Springer, vol. 18(1), pages 37-56, March.
    9. Antonio Frangioni & Claudio Gentile & James Hungerford, 2020. "Decompositions of Semidefinite Matrices and the Perspective Reformulation of Nonseparable Quadratic Programs," Mathematics of Operations Research, INFORMS, vol. 45(1), pages 15-33, February.
    10. Ye Tian & Miao Sun & Zuoliang Ye & Wei Yang, 2016. "Expanded models of the project portfolio selection problem with loss in divisibility," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(8), pages 1097-1107, August.
    11. Wei Xu & Jie Tang & Ka Fai Cedric Yiu & Jian Wen Peng, 2024. "An Efficient Global Optimal Method for Cardinality Constrained Portfolio Optimization," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 690-704, March.
    12. Rujun Jiang & Duan Li, 2019. "Second order cone constrained convex relaxations for nonconvex quadratically constrained quadratic programming," Journal of Global Optimization, Springer, vol. 75(2), pages 461-494, October.
    13. Dimitris Bertsimas & Ryan Cory-Wright, 2022. "A Scalable Algorithm for Sparse Portfolio Selection," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1489-1511, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mansini, Renata & Ogryczak, Wlodzimierz & Speranza, M. Grazia, 2014. "Twenty years of linear programming based portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 518-535.
    2. Philipp Baumann & Norbert Trautmann, 2013. "Portfolio-optimization models for small investors," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 77(3), pages 345-356, June.
    3. Polak, George G. & Rogers, David F. & Sweeney, Dennis J., 2010. "Risk management strategies via minimax portfolio optimization," European Journal of Operational Research, Elsevier, vol. 207(1), pages 409-419, November.
    4. Massol, Olivier & Banal-Estañol, Albert, 2014. "Export diversification through resource-based industrialization: The case of natural gas," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1067-1082.
    5. Chien-Ming Chen & Joe Zhu, 2011. "Efficient Resource Allocation via Efficiency Bootstraps: An Application to R&D Project Budgeting," Operations Research, INFORMS, vol. 59(3), pages 729-741, June.
    6. Woodside-Oriakhi, M. & Lucas, C. & Beasley, J.E., 2011. "Heuristic algorithms for the cardinality constrained efficient frontier," European Journal of Operational Research, Elsevier, vol. 213(3), pages 538-550, September.
    7. Panos Xidonas & Christis Hassapis & George Mavrotas & Christos Staikouras & Constantin Zopounidis, 2018. "Multiobjective portfolio optimization: bridging mathematical theory with asset management practice," Annals of Operations Research, Springer, vol. 267(1), pages 585-606, August.
    8. Zhou, Zhongbao & Jin, Qianying & Xiao, Helu & Wu, Qian & Liu, Wenbin, 2018. "Estimation of cardinality constrained portfolio efficiency via segmented DEA," Omega, Elsevier, vol. 76(C), pages 28-37.
    9. Fang, Yong & Chen, Lihua & Fukushima, Masao, 2008. "A mixed R&D projects and securities portfolio selection model," European Journal of Operational Research, Elsevier, vol. 185(2), pages 700-715, March.
    10. Yuanyao Ding, 2006. "Portfolio Selection under Maximum Minimum Criterion," Quality & Quantity: International Journal of Methodology, Springer, vol. 40(3), pages 457-468, June.
    11. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    12. Jongbin Jung & Seongmoon Kim, 2017. "Developing a dynamic portfolio selection model with a self-adjusted rebalancing method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 766-779, July.
    13. Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
    14. Sabastine Mushori & Delson Chikobvu, 2016. "A Stochastic Multi-stage Trading Cost model in optimal portfolio selection," EERI Research Paper Series EERI RP 2016/23, Economics and Econometrics Research Institute (EERI), Brussels.
    15. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2015. "Linear vs. quadratic portfolio selection models with hard real-world constraints," Computational Management Science, Springer, vol. 12(3), pages 345-370, July.
    16. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    17. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    18. Dimitris Bertsimas & Ryan Cory-Wright, 2022. "A Scalable Algorithm for Sparse Portfolio Selection," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1489-1511, May.
    19. Alessandra Carleo & Francesco Cesarone & Andrea Gheno & Jacopo Maria Ricci, 2017. "Approximating exact expected utility via portfolio efficient frontiers," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 115-143, November.
    20. Martin Branda & Max Bucher & Michal Červinka & Alexandra Schwartz, 2018. "Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization," Computational Optimization and Applications, Springer, vol. 70(2), pages 503-530, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jglopt:v:56:y:2013:i:4:p:1409-1423. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.