Optimizing 3-objective portfolio selection with equality constraints and analyzing the effect of varying constraints on the efficient sets

Yue Qi; Xiaolin Li; Su Zhang

doi:10.3934/jimo.2020033

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July 2021, 17(4): 1531-1556. doi: 10.3934/jimo.2020033

Optimizing 3-objective portfolio selection with equality constraints and analyzing the effect of varying constraints on the efficient sets

Yue Qi ^1, , Xiaolin Li ^2, and Su Zhang ^2,,

1.	China Academy of Corporate Governance & Department of Financial Management, Business School, Nankai University, 94 Weijin Road, Tianjin 300071, China
2.	Department of Financial Management, Business School, Nankai University, 94 Weijin Road, Tianjin 300071, China

^* Corresponding author: Su Zhang

Received May 2019 Revised August 2019 Published February 2020

Fund Project: The research is supported by the National Social Science Fund of China 2018 (Grant No. 18BGL063)

Markowitz proposes portfolio selection as a 2-objective model and emphasizes computing (whole) efficient sets and nondominated sets. Computing the sets has long been a topic in multiple-objective optimization. Researchers have gradually recognized other criteria in addition to variance and expected return. To formulate the additional criteria, researchers propose multiple-objective portfolio selection. However, computing the corresponding efficient set and nondominated set is not fully achieved. Moreover, discovering the sets' properties and utilizing the properties remain typically unanswered.

In this paper, we extend Sharpe's and Merton's model by adding a general linear objective and imposing equality constraints. To optimize the model, we analytically derive the minimum-variance surface (defined later), prove it as a nondegenerate paraboloid, and prove the nondominated set as a paraboloidal segment. We also analytically derive the efficient set and prove it as a 2-dimensional translated cone. We then prove that the set subsumes the efficient set of the corresponding traditional model, so the efficient set expands as the general linear objective is added. Furthermore, constraints can be changed or added. We utilize the translated-cone properties and readily compute the changing constraints' effect on the efficient sets by formulae or linear-equation systems.

Keywords: Multiple-objective optimization, multiple-objective portfolio selection, efficient set, nondominated set, paraboloid, constraint.

Mathematics Subject Classification: Primary: 90B50; Secondary: 90C29.

Citation: Yue Qi, Xiaolin Li, Su Zhang. Optimizing 3-objective portfolio selection with equality constraints and analyzing the effect of varying constraints on the efficient sets. Journal of Industrial & Management Optimization, 2021, 17 (4) : 1531-1556. doi: 10.3934/jimo.2020033

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[34]	J. B. Guerard and A. Mark, The optimization of efficient portfolios: The case for an R & D quadratic term, Research in Finance, 20 (2003), 217-247. doi: 10.1016/S0196-3821(03)20011-3. Google Scholar
[35]	C. R. Harvey, J. C. Liechty, M. W. Liechty and P. Müller, Portfolio selection with higher moments, Quant. Finance, 10 (2010), 469-485. doi: 10.1080/14697681003756877. Google Scholar
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[37]	M. Hirschberger, R. E. Steuer, S. Utz, M. Wimmer and Y. Qi, Computing the nondominated surface in tri-criterion portfolio selection, Oper. Res., 61 (2013), 169-183. doi: 10.1287/opre.1120.1140. Google Scholar
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Figure 1. Major methods to solve (1) and (3) in the central and right parts, respectively

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Figure 4. The minimum-variance surface

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Figure 2. An efficient set and efficient sets under changing constraints in $ \mathbb{R}^n $

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Figure 3. The existence of many nondominated portfolios for $ z_1 = 1 $ of (4) for the proof of Theorem 4.5

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Figure 5. The nondominated set

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Figure 6. The existence of zero-covariance portfolio $ \mathbf{{z}}^{zcp} $ for (2)

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Table 1. The result of the hypotheses (24)-(27)

For (24)	For (25)	For (26)	For (27)
mean for $ \mathbf{{x}}^e $: 0.0052	mean for $ \mathbf{{x}}^e $: 0.0022	mean for $ \mathbf{{x}}^e $: 0.0052	mean for $ \mathbf{{x}}^e $: 0.0022
mean for $ \mathbf{{x}}^n $: 0.0054	mean for $ \mathbf{{x}}^n $: 0.0020	mean for $ \mathbf{{x}}^p $: 0.0055	mean for $ \mathbf{{x}}^p $: 0.0019
p-value: 0.9585	p-value: 0.0130	p-value: 0.9268	p-value: 0.0085
accept $ H_0 $	reject $ H_0 $	accept $ H_0 $	reject $ H_0 $

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For (24)	For (25)	For (26)	For (27)
mean for $ \mathbf{{x}}^e $: 0.0052	mean for $ \mathbf{{x}}^e $: 0.0022	mean for $ \mathbf{{x}}^e $: 0.0052	mean for $ \mathbf{{x}}^e $: 0.0022
mean for $ \mathbf{{x}}^n $: 0.0054	mean for $ \mathbf{{x}}^n $: 0.0020	mean for $ \mathbf{{x}}^p $: 0.0055	mean for $ \mathbf{{x}}^p $: 0.0019
p-value: 0.9585	p-value: 0.0130	p-value: 0.9268	p-value: 0.0085
accept $ H_0 $	reject $ H_0 $	accept $ H_0 $	reject $ H_0 $

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2019 Impact Factor: 1.366

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American Institute of Mathematical Sciences

Optimizing 3-objective portfolio selection with equality constraints and analyzing the effect of varying constraints on the efficient sets

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American Institute of Mathematical Sciences

Optimizing 3-objective portfolio selection with equality constraints and analyzing the effect of varying constraints on the efficient sets

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