Robust portfolio selection problems: a comprehensive review

Ghahtarani, Alireza; Saif, Ahmed; Ghasemi, Alireza

doi:10.1007/s12351-022-00690-5

Cited by 17 publications

(7 citation statements)

References 176 publications

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“…For example, Fliege and Werner [25, pp. 426–428 & 430–432] , Georgantas et al [23] , and Ghahtarani et al [24] still adopt 2-objective models. Pavlou et al [26] still empirically focus on 2-objective models and one multiple-objective model and verify (1) as quite robust in comparison to other 2-objective models.…”

Section: Multiple-objective Optimization Portfolio Optimization and P...mentioning

confidence: 99%

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Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths

Liao

Liu

et al. 2022

Operations Research Perspectives

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Section: Multiple-objective Optimization Portfolio Optimization and P...mentioning

confidence: 99%

“…Many researchers investigate multiple-objective robust optimization (as surveyed by Ide and Schöbel, Georgantas et al [22] , [23] , and Ghahtarani et al [24] ). However, the researchers infrequently explicitly compute the optimal solutions for multiple-objective portfolio optimization.…”

Section: Introductionmentioning

confidence: 99%

Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths

Liao

Liu

et al. 2022

Operations Research Perspectives

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“…Some research works [15,16] concentrated on the application of robust optimization on basic mean-variance, mean value-at-risk (mean-VaR), and mean conditional-value-at-risk (mean-CVaR) problems, but did not consider variants of the problem like robust index tracking, robust and sparse portfolio selection problems, and so on. More relevant works can be found in [17][18][19][20] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Robust and Sparse Portfolio: Optimization Models and Algorithms

Zhao,

Jiang,

Yang

2023

Mathematics

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The robust and sparse portfolio selection problem is one of the most-popular and -frequently studied problems in the optimization and financial literature. By considering the uncertainty of the parameters, the goal is to construct a sparse portfolio with low volatility and decent returns, subject to other investment constraints. In this paper, we propose a new portfolio selection model, which considers the perturbation in the asset return matrix and the parameter uncertainty in the expected asset return. We define three types of stationary points of the penalty problem: the Karush–Kuhn–Tucker point, the strong Karush–Kuhn–Tucker point, and the partial minimizer. We analyze the relationship between these stationary points and the local/global minimizer of the penalty model under mild conditions. We design a penalty alternating-direction method to obtain the solutions. Compared with several existing portfolio models on seven real-world datasets, extensive numerical experiments demonstrate the robustness and effectiveness of our model in generating lower volatility.

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“…In principle, a passive strategy includes all stocks under an index based on their proportion. However, as pointed out by Ghahtarani et al, 4 the need for frequent rebalancing transactions to closely track the index might lead to a massive amount of transaction costs. In this way, Chen and Kwon 5 state that it might be desirable to keep fewer assets than the target index.…”

Section: Introductionmentioning

confidence: 99%

Investment portfolio tracking using model predictive control

Melo

Cardoso

Jesus

et al. 2022

Optim Control Appl Methods

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The composition of an investment portfolio aiming to increase the financial returns while reducing the exposure to risk is a topic of growing interest in the world. In this direction, we propose a model predictive control (MPC) strategy in order to optimize the investment portfolio selection taking into account the tracking of a reference portfolio with desired return. In addition, an analysis comparing different sizes of the prediction horizon according to VPH-MPC (Varying Predictive Horizon-MPC) and FPH-MPC (Fixed Predictive Horizon-MPC) algorithms is conducted. Finally, we propose an optimal control problem using the tracking error as a function of loss of CVaR (Conditional Value at Risk) measurement. Numerical experiments are run based on Brazilian Stock Exchange data. The experimental results are compared with the Markowitz portfolio optimization model, a conventional tracking strategy, and benchmarks from the Brazilian financial market. This comparison indicates a good tracking performance obtained by the proposed MPC in the two versions while satisfying the constraints.

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Robust portfolio selection problems: a comprehensive review

Cited by 17 publications

References 176 publications

Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths

Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths

Robust and Sparse Portfolio: Optimization Models and Algorithms

Investment portfolio tracking using model predictive control

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