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Cardinality-constrained portfolio selection via two-timescale duplex neurodynamic optimization.

Man-Fai Leung1, Jun Wang2, Hangjun Che3

  • 1School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|July 7, 2022
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Summary
This summary is machine-generated.

This study introduces a novel neurodynamic optimization method for portfolio selection. The approach enhances investment strategies by improving risk-adjusted returns and portfolio performance.

Keywords:
Cardinality constraintsNeurodynamic optimizationPortfolio selection

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Area of Science:

  • Computational Finance
  • Optimization Theory
  • Artificial Intelligence

Background:

  • Portfolio selection is a complex optimization problem, often involving trade-offs between risk and return.
  • Traditional methods may struggle with complex constraints like cardinality.
  • Neurodynamic optimization offers a promising avenue for tackling these challenges.

Purpose of the Study:

  • To formulate and solve the cardinality-constrained portfolio selection problem using neurodynamic optimization.
  • To adapt a two-timescale duplex neurodynamic approach for this specific problem.
  • To evaluate the proposed method against existing baseline approaches.

Main Methods:

  • Formulating portfolio selection as a biconvex optimization problem.
  • Applying a customized two-timescale duplex neurodynamic approach with particle swarm optimization for global search.
  • Utilizing recurrent neural networks operating at different timescales for local optimization.

Main Results:

  • The neurodynamic optimization approach demonstrated superior performance compared to three baseline methods.
  • Improvements were observed in key risk-adjusted performance criteria.
  • Enhanced portfolio returns were achieved across experimental datasets.

Conclusions:

  • The proposed neurodynamic optimization method is effective for cardinality-constrained portfolio selection.
  • The approach successfully navigates complex optimization landscapes to find better solutions.
  • This technique offers a significant advancement in computational finance for investment strategies.