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A network-based data mining approach to portfolio selection via weighted clique relaxations

Author

Listed:
  • Vladimir Boginski
  • Sergiy Butenko
  • Oleg Shirokikh
  • Svyatoslav Trukhanov
  • Jaime Gil Lafuente

Abstract

We introduce a new network-based data mining approach to selecting diversified portfolios by modeling the stock market as a network and utilizing combinatorial optimization techniques to find maximum-weight s-plexes in the obtained networks. The considered approach is based on the weighted market graph model, which is used for identifying clusters of stocks according to a correlation-based criterion. The proposed techniques provide a new framework for selecting profitable diversified portfolios, which is verified by computational experiments on historical data over the past decade. In addition, the proposed approach can be used as a complementary tool for narrowing down a set of “candidate” stocks for a diversified portfolio, which can potentially be analyzed using other known portfolio selection techniques. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Vladimir Boginski & Sergiy Butenko & Oleg Shirokikh & Svyatoslav Trukhanov & Jaime Gil Lafuente, 2014. "A network-based data mining approach to portfolio selection via weighted clique relaxations," Annals of Operations Research, Springer, vol. 216(1), pages 23-34, May.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:23-34:10.1007/s10479-013-1395-3
    DOI: 10.1007/s10479-013-1395-3
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    2. Oleg Shirokikh & Grigory Pastukhov & Vladimir Boginski & Sergiy Butenko, 2013. "Computational study of the US stock market evolution: a rank correlation-based network model," Computational Management Science, Springer, vol. 10(2), pages 81-103, June.
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    Cited by:

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    2. V. A. Kalyagin & A. P. Koldanov & P. A. Koldanov & P. M. Pardalos, 2018. "Optimal decision for the market graph identification problem in a sign similarity network," Annals of Operations Research, Springer, vol. 266(1), pages 313-327, July.
    3. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    4. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    5. Justo Puerto & Moises Rodr'iguez-Madrena & Andrea Scozzari, 2019. "Location and portfolio selection problems: A unified framework," Papers 1907.07101, arXiv.org.
    6. Bruno Nogueira & Rian G. S. Pinheiro, 2020. "A GPU based local search algorithm for the unweighted and weighted maximum s-plex problems," Annals of Operations Research, Springer, vol. 284(1), pages 367-400, January.
    7. Bruno Scalzo Dees & Ljubisa Stankovic & Anthony G. Constantinides & Danilo P. Mandic, 2019. "Portfolio Cuts: A Graph-Theoretic Framework to Diversification," Papers 1910.05561, arXiv.org, revised Oct 2019.
    8. Shreya Patki & Roy H. Kwon & Yuri Lawryshyn, 2024. "Centrality-Based Equal Risk Contribution Portfolio," Risks, MDPI, vol. 12(1), pages 1-17, January.
    9. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2018. "Asset allocation: new evidence through network approaches," Papers 1810.09825, arXiv.org.
    10. Paolo Giudici & Gloria Polinesi & Alessandro Spelta, 2022. "Network models to improve robot advisory portfolios," Annals of Operations Research, Springer, vol. 313(2), pages 965-989, June.
    11. Seo Woo Hong & Pierre Miasnikof & Roy Kwon & Yuri Lawryshyn, 2021. "Market Graph Clustering via QUBO and Digital Annealing," JRFM, MDPI, vol. 14(1), pages 1-13, January.
    12. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2021. "Asset allocation: new evidence through network approaches," Annals of Operations Research, Springer, vol. 299(1), pages 61-80, April.
    13. Millington, Tristan & Niranjan, Mahesan, 2021. "Stability and similarity in financial networks—How do they change in times of turbulence?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    14. Lu, Ya-Nan & Li, Sai-Ping & Zhong, Li-Xin & Jiang, Xiong-Fei & Ren, Fei, 2018. "A clustering-based portfolio strategy incorporating momentum effect and market trend prediction," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 1-15.
    15. Fei Ren & Ya-Nan Lu & Sai-Ping Li & Xiong-Fei Jiang & Li-Xin Zhong & Tian Qiu, 2017. "Dynamic Portfolio Strategy Using Clustering Approach," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.

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