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Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity

Nebojsa Bacanin1, Milan Tuba1

  • 1Faculty of Computer Science, Megatrend University Belgrade, 11070 Belgrade, Serbia.

Thescientificworldjournal
|July 4, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a modified firefly algorithm (FA) to solve complex portfolio optimization problems with realistic constraints. The enhanced algorithm improves performance and diversity for better investment selection.

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Area of Science:

  • Computational Finance
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Portfolio optimization is computationally challenging with realistic constraints.
  • Nature-inspired metaheuristics are suitable but underutilized, especially swarm intelligence algorithms.
  • No swarm intelligence approach exists for cardinality constrained mean-variance (CCMV) portfolio optimization with entropy constraints.

Purpose of the Study:

  • To introduce a modified firefly algorithm (FA) for the CCMV portfolio problem with an entropy constraint.
  • To address the exploration deficiencies of the standard FA in constrained optimization.
  • To enhance portfolio selection by incorporating an entropy diversity constraint.

Main Methods:

  • Modification of the standard firefly algorithm (FA) to improve exploration.
  • Application of the modified FA to the cardinality constrained mean-variance (CCMV) portfolio model.
  • Inclusion of an entropy diversity constraint to enhance portfolio diversification.

Main Results:

  • The modified firefly algorithm (FA) demonstrated superior performance compared to existing state-of-the-art algorithms.
  • The addition of the entropy diversity constraint further improved the optimization results.
  • The proposed method effectively handles complex portfolio optimization problems.

Conclusions:

  • The modified firefly algorithm (FA) is a promising approach for solving constrained portfolio optimization problems.
  • Incorporating entropy constraints enhances the diversification and performance of investment portfolios.
  • This research contributes a novel swarm intelligence application to financial optimization.