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Particle swarm optimization approach to portfolio construction

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  • Ren‐Raw Chen
  • Wiliam Kaihua Huang
  • Shih‐Kuo Yeh

Abstract

Particle swarm optimization (PSO) is an artificial intelligence technique that can be used to find approximate solutions to extremely difficult or impossible numeric optimization problems. Recently, PSO algorithms have been widely used in solving complex financial optimization problems. This paper presents a PSO approach to solve a portfolio construction problem, since this methodology is a population‐based heuristic algorithm that is suitable for solving high‐dimensional constrained optimization problems. The computational results show that PSO algorithms have advantages in optimizing the Sortino ratio, especially in speed, when the size of the portfolio is large.

Suggested Citation

  • Ren‐Raw Chen & Wiliam Kaihua Huang & Shih‐Kuo Yeh, 2021. "Particle swarm optimization approach to portfolio construction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(3), pages 182-194, July.
    • Handle: RePEc:wly:isacfm:v:28:y:2021:i:3:p:182-194
    DOI: 10.1002/isaf.1498
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    References listed on IDEAS

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    1. Lahmiri, Salim, 2018. "Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 444-451.
    2. Marco Corazza & Giovanni Fasano & Riccardo Gusso, 2011. "Particle Swarm Optimization with non-smooth penalty reformulation for a complex portfolio selection problem," Working Papers 2011_10, Department of Economics, University of Venice "Ca' Foscari".
    3. Lahmiri, Salim, 2016. "Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 388-396.
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    Cited by:

    1. Chang Li & Daniel C. Coster, 2022. "Improved Particle Swarm Optimization Algorithms for Optimal Designs with Various Decision Criteria," Mathematics, MDPI, vol. 10(13), pages 1-16, July.

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