Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization

Deng, Guang-Feng; Lin, Woo-Tsong; Lo, Chih-Chung

doi:10.1016/j.eswa.2011.09.129

Cited by 120 publications

(74 citation statements)

References 19 publications

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“…The portfolio model was tested on various restricted and unrestricted risky investment portfolios and compared with the GA. The PSO demonstrated high computational efficiency in portfolio optimisation reported in the literature (Deng et al, 2012;Liu et al, 2012;Zhu, Wang, Wang, & Chen, 2011).…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

The application of water cycle algorithm to portfolio selection

Moradi

Sadollah

Eskandar

et al. 2017

Economic Research-Ekonomska Istraživanja

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Portfolio selection is one of the most vital financial problems in literature. The studied problem is a nonlinear multi-objective problem which has been solved by a variety of heuristic and metaheuristic techniques. In this article, a metaheuristic optimiser, the multiobjective water cycle algorithm (MOWCA), is represented to find efficient frontiers associated with the standard mean-variance (M-V) portfolio optimisation model. The inspired concept of WCA is based on the simulation of water cycle process in the nature. Computational results are obtained for analyses of daily data for the period January 2012 to December 2014, including S&P100 in the US, Hang Seng in Hong Kong, FTSE100 in the UK, and DAX100 in Germany. The performance of the MOWCA for solving portfolio optimisation problems has been evaluated in comparison with other multi-objective optimisers including the NSGA-II and multiobjective particle swarm optimisation (MOPSO). Four well-known performance metrics are used to compare the reported optimisers. Statistical optimisation results indicate that the applied MOWCA is an efficient and practical optimiser compared with the other methods for handling portfolio optimisation problems.

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Section: Particle Swarm Optimisationmentioning

confidence: 99%

The application of water cycle algorithm to portfolio selection

Moradi

Sadollah

Eskandar

et al. 2017

Economic Research-Ekonomska Istraživanja

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“…Some researchers proposed exact solution methods (ie., Bienstock, 1996;Bertsimas and Shioda, 2009;Li et al, 2006;Shaw et al, 2008;Murray and Shek, 2012); Cesarone et al, 2013;Cui et al, 2013;Sun et al, 2013;Le Thi et al, 2009. Since exact solution methods were able to solve only a fraction of practically useful LAM models, many heuristic algorithms had also been proposed (ie., Fernández and Gómez, 2007;Ruiz-Torrubiano and Suarez, 2010;Anagnostopoulos and Mamanis, 2011;Woodside-Oriakhi et al, 2011;Deng et al, 2012). In these studies, it appears that the computational complexity of the solution given by the LAM (Limited Asset Markowitz) model is much greater than the one spent by the classical Markowitz model.…”

Section: Introductionmentioning

confidence: 97%

The Admissible Multiperiod Mean Variance Portfolio Selection Problem with Cardinality Constraints

Zhang¹,

Li²

2017

Industrial Engineering and Management Systems

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Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

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“…Golmakani and Fazel [10] de önerdikleri PSO algoritması ile GA algoritmasını karşılaştırmışlar ve daha iyi sonuç elde etmişlerdir. Deng et al [11] PSO tekniğini geliştirerek önerdikleri algoritma ile literatürdeki PSO tekniklerinden daha iyi sonuçlar elde etmişlerdir. PSO tekniği, Corazza et al [12] tarafından bir penaltı fonksiyonu eklenerek algoritma performansı geliştirilmiştir.…”

Section: Introductionunclassified

Particle swarm optimization algorithm for mean-variance portfolio optimization: A case study of Istanbul Stock Exchange

Akyer¹,

Kalaycı²,

Aygören³

2018

Pamukkale J Eng Sci

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Yatırım olanaklarının genişlemesi ile birlikte portföye dahil edilebilecek menkul kıymet sayısı oldukça artmıştır. Bu durumda, yatırımcı, menkul kıymet olanakları kümesinden bütün menkul kıymetlere yatırım yapmak istese de, işlem maliyetleri oldukça artacak ve portföyün yönetilebilirliği zorlaşacaktır. Belirtilen bu dezavantajlardan kurtulmanın en temel yolu portföye dahil edilecek menkul kıymet sayısını sınırlandırmaktır. Bu şartlar altında, Harry M. Markowitz [4] tarafından geliştirilen ortalama varyans modeline portföyde yer alacak menkul kıymet sayısı kısıtı eklenmelidir. Yeni oluşturulan problemin literatürdeki adı elaman sayısı kısıtlı portföy optimizasyonu problemidir.Eleman sayısı kısıtlı portföy optimizasyonu problemi bir kombinatorik optimizasyon problemidir ve literatürde NP-Zor sınıfına girmektedir. Bu problem modelleri standart kuadratik programlama ile çözülebilir iken, günümüzde problem boyutlarının büyümesi standart programların çözüm için yetersiz kalmasına sebep olmuştur. Bu nedenle, problemin çözümü için sürü tabanlı bir meta-sezgisel olan parçacık sürü optimizasyonu (PSO) tekniği uyarlanmış ve Borsa İstanbul endekslerine uygulanmıştır.

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Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization

Cited by 120 publications

References 19 publications

The application of water cycle algorithm to portfolio selection

The application of water cycle algorithm to portfolio selection

The Admissible Multiperiod Mean Variance Portfolio Selection Problem with Cardinality Constraints

Particle swarm optimization algorithm for mean-variance portfolio optimization: A case study of Istanbul Stock Exchange

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