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2021 | OriginalPaper | Buchkapitel

8. A Constrained Portfolio Selection Model Solved by Particle Swarm Optimization Under Different Risk Measures

verfasst von : Akbar Esfahanipour, Pouya Khodaee

Erschienen in: Applying Particle Swarm Optimization

Verlag: Springer International Publishing

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Abstract

Portfolio selection has been one of the crucial problems in financial engineering. Investors’ interest is to construct a portfolio having a balance between the investor’s risk-taking and his/her expectations about the portfolio returns. The Markowitz model is a nonlinear constrained multi-objective optimization model that is usually impossible to solve at a good time. In this chapter, the purpose is to examine portfolio optimization models and applications of the particle swarm optimization (PSO) technique in solving these models. A constrained portfolio selection model has been developed, which is solved by the PSO technique as a metaheuristic approach using data from the Tehran Stock Exchange (TSE) to assess the developed model. In this case, the effects of three different risk measures have been analyzed on the constructed portfolios. The numerical results show that conditional value at risk (CVaR) performs better than the other two risk measures, including semivariance and variance. However, from the diversification perspective, the model with the variance risk measure produces a more diversified portfolio compared to the other two risk measures, although the differences are trivial.

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Vorheriges Kapitel The PSO Family: Application to the Portfolio Optimization Problem

Nächstes Kapitel Optimal Portfolio Selection with Particle Swarm Algorithm: An Application on BIST-30

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Mean absolute deviation.

Value at risk.

Mean-variance.

Mean absolute deviation.

Semivariance.

Variance with skewness.

Electromagnetism-like algorithm.

Genetic algorithm.

Genetic network programming.

Simulated annealing.

Non-dominated sorting genetic algorithm.

Tabu search.

Random population topology based on the average degree.

Random population topology based on the degree.

Dynamic random population topology based on the average degree.

Dynamic random population topology based on the degree.

Cardinality constrained mean-variance.

Hybrid particle swarm optimization.

Capital asset pricing model.

Modified VaR.

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Titel: A Constrained Portfolio Selection Model Solved by Particle Swarm Optimization Under Different Risk Measures
verfasst von: Akbar Esfahanipour
Pouya Khodaee
Verlag: Springer International Publishing
Buch: Applying Particle Swarm Optimization
Print ISBN: 978-3-030-70280-9

Electronic ISBN: 978-3-030-70281-6

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-70281-6_8