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Swarm Intelligence for Cardinality-Constrained Portfolio Problems

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 6423)

Abstract

This work presents Particle Swarm Optimization (PSO), a collaborative population-based swarm intelligent algorithm for solving the cardinality constraints portfolio optimization problem (CCPO problem). To solve the CCPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The computational test results indicate that the proposed PSO outperformed basic PSO algorithm, genetic algorithm (GA), simulated annealing (SA), and tabu search (TS) in most cases.

Keywords

  • Particle swarm optimization
  • cardinality constrained portfolio optimization problem
  • Markowitz mean-variance model
  • nonlinear mixed quadratic programming problem
  • swarm intelligence

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Deng, GF., Lin, WT. (2010). Swarm Intelligence for Cardinality-Constrained Portfolio Problems. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16696-9_44

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  • DOI: https://doi.org/10.1007/978-3-642-16696-9_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16695-2

  • Online ISBN: 978-3-642-16696-9

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