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A bi-level programming approach for global investment strategies with financial intermediation

Author

Listed:
  • Francisco Benita
  • Francisco López-Ramos
  • Stefano Nasini

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

Abstract

Most mathematical programming models for investment selection and portfolio management rely on centralized decisions about both budget allocation in different (real and financial) investment options and portfolio composition within the different options. However, in more realistic market scenarios investors do not directly select the portfolio composition, but only provide guidelines and requirements for the investment procedure. Financial intermediaries are then responsible for the detailed portfolio management, resulting in a hierarchical investor-intermediary decision setting. In this work, a bi-level mixed-integer quadratic optimization problem is proposed for the decentralized selection of a portfolio of financial securities and real investments. Single-level reformulation techniques are presented, along with valid-inequalities which allow speeding-up their resolution procedure, when large-scale instances are taken into account. We conducted computational experiments on large historical stock market data from the Center for Research in Security Prices to validate and compare the proposed bi-level investment framework (and the resulting single-level reformulations), under different levels of investor's and intermediary's risk aversion and control. The empirical tests reveled the impact of decentralization on the investment performance, and provide a comparative analysis of the computational effort corresponding to the proposed solution approaches.

Suggested Citation

  • Francisco Benita & Francisco López-Ramos & Stefano Nasini, 2019. "A bi-level programming approach for global investment strategies with financial intermediation," Post-Print hal-02117530, HAL.
    • Handle: RePEc:hal:journl:hal-02117530
    DOI: 10.1016/j.ejor.2018.10.009
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    Cited by:

    1. Francisco López-Ramos & Stefano Nasini & Armando Guarnaschelli, 2019. "Road network pricing and design for ordinary and hazmat vehicles: Integrated model and specialized local search," Post-Print hal-02510066, HAL.
    2. Nasini, Stefano & Verschelde, Marijn & Merlevede, Bruno, 2024. "Optimal transfer prices and technology in decentralized business groups," European Journal of Operational Research, Elsevier, vol. 319(3), pages 920-942.
    3. Ritu Arora & Chandra K. Jaggi, 2023. "An aspect of bilevel interval linear fractional transportation problem with disparate flows: a fuzzy programming approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(6), pages 2276-2288, December.
    4. Beikverdi, Majid & Tehrani, Nasim Ghanbar & Shahanaghi, Kamran, 2024. "A Bi-level model for district-fairness participatory budgeting: Decomposition methods and application," European Journal of Operational Research, Elsevier, vol. 314(1), pages 340-362.
    5. Vera Ivanyuk, 2021. "Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation," Economies, MDPI, vol. 9(3), pages 1-19, June.
    6. Gong, J.W. & Li, Y.P. & Lv, J. & Huang, G.H. & Suo, C. & Gao, P.P., 2022. "Development of an integrated bi-level model for China’s multi-regional energy system planning under uncertainty," Applied Energy, Elsevier, vol. 308(C).
    7. Benita, Francisco & Nasini, Stefano & Nessah, Rabia, 2022. "A cooperative bargaining framework for decentralized portfolio optimization," Journal of Mathematical Economics, Elsevier, vol. 103(C).
    8. Li-Chen Cheng & Yu-Hsiang Huang & Ming-Hua Hsieh & Mu-En Wu, 2021. "A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions," Mathematics, MDPI, vol. 9(23), pages 1-16, November.
    9. Todor Stoilov & Krasimira Stoilova & Miroslav Vladimirov, 2021. "Explicit Value at Risk Goal Function in Bi-Level Portfolio Problem for Financial Sustainability," Sustainability, MDPI, vol. 13(4), pages 1-14, February.

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