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Multi-Period Portfolio Optimization Model with Cone Constraints and Discrete Decisions

Author

Listed:
  • Ümit Sağlam

    (Department of Management and Supply Chain, College of Business and Technology, East Tennessee State University, Johnson City, TN 37614, USA
    These authors contributed equally to this work.)

  • Hande Y. Benson

    (Department of Decision Sciences and MIS, LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA
    These authors contributed equally to this work.)

Abstract

This work develops a practical multi-period optimization approach that incorporates real-world constraints, including discrete decisions and conic risk constraints. Expanding upon earlier single-period models, our framework employs a binary scenario tree derived from monthly returns of randomly selected S&P 500 stocks to represent market evolution across multiple periods. The formulation captures essential portfolio constraints, such as transaction fees, sector diversification, and minimum investment thresholds, resulting in a robust and comprehensive optimization approach. To efficiently solve the resulting mixed-integer second-order cone programming (MISOCP) problem, we employ an outer approximation algorithm with a warmstart strategy, which significantly improves solution runtimes and computational efficiency. Numerical experiments demonstrate the model’s effectiveness, showing an average improvement of 10.71 % in iteration count and 15.24 % in computational time when using the warmstart approach.

Suggested Citation

  • Ümit Sağlam & Hande Y. Benson, 2025. "Multi-Period Portfolio Optimization Model with Cone Constraints and Discrete Decisions," JRFM, MDPI, vol. 18(4), pages 1-16, April.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:4:p:218-:d:1637359
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