Robust investment strategies with discrete asset choice constraints using DC programming

Gülpınar, Nalân; Thi, Hoai An Le; Moeini, Mahdi

doi:10.1080/02331930903500274

Cited by 23 publications

(8 citation statements)

References 16 publications

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“…More precisely, we studied a cross-efficiency approach by introducing a new secondary goal. This approach consists in constructing a multi-objective model that can be transformed to an LP model in many ways, for example, the classical max-min method [8,11]. The introduced approach has been extended (in a natural way) for evaluation of DM U s with undesirable outputs.…”

Section: Resultsmentioning

confidence: 99%

“…In order to solve this multiobjective model, many approaches can be chosen from the literature of multiobjective programming. One of the classical approaches consists in using the max-min method [8,11]. In this approach, we introduce a supplementary variable, e.g., φ for transforming the multi-objective model (23)- (29) to the following linear programming (LP) model (where J = {1, .…”

Section: Data Envelopment Analysis For Undesirable Outputsmentioning

confidence: 99%

“…In order to attain this objective, we bring together all necessary materials, such as suitable production possibility set (PPS) and a new secondary goal. This procedure produces a multi-objective model for which we use maxmin model that focuses on worst-case evaluations [8,11]. Some experiments have been carried out on a practical data set [22].…”

Section: Introductionmentioning

confidence: 99%

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A Cross-Efficiency Approach for Evaluating Decision Making Units in Presence of Undesirable Outputs

Moeini

Karimi

Khorram

2015

Advances in Intelligent Systems and Computing

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Abstract. Data Envelopment Analysis (DEA) is a mathematical programming approach for measuring efficiency of Decision Making Units (DM U s). In traditional DEA, a ratio of weighted outputs to inputs is examined and, for each DM U , some optimal weights are obtained. The method of cross-efficiency is an extension to DEA by which a matrix of scores is computed. The elements of the matrix are computed by means of the weights obtained via usual models of DEA. The cross-efficiency may have some drawbacks, e.g., the cross-efficiency scores may be multiple due to the presence of several optima. To overcome this issue, secondary goals are used. However, this method has never been used for peer evaluation of DMUs with undesirable outputs. In this paper, our objective is to bridge this gap. For this end, we introduce a new secondary goal, test it on an empirical example with undesirable outputs, report the results, and finally, we give some concluding remarks.

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Section: Resultsmentioning

confidence: 99%

Section: Data Envelopment Analysis For Undesirable Outputsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Cross-Efficiency Approach for Evaluating Decision Making Units in Presence of Undesirable Outputs

Moeini

Karimi

Khorram

2015

Advances in Intelligent Systems and Computing

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“…There are two min-max models: discrete and continuous one [3]. In the discrete min-max approach, the optimal investment strategy is determined in view of all specified discrete rival scenarios simultaneously [2]. But, it requires the specification of number of discrete scenarios.…”

Section: Introductionmentioning

confidence: 99%

Solving continuous min max problem for single period portfolio selection with discrete constraints by DCA

Thi¹,

Tran²

2012

Optimization

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“…For analysis using the loss function and the disappointment function, both when Σ Σ Σ is known and not known, see [97] for details. See [37], [115] and [55] for other robust portfolio optimization models.…”

Section: Robust Optimization In the Mean-variance Modelmentioning

confidence: 99%

Mean-Variance Portfolio Optimization : Eigendecomposition-Based Methods

Mayambala¹

2015

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Modern portfolio theory is about determining how to distribute capital among available securities such that, for a given level of risk, the expected return is maximized, or for a given level of return, the associated risk is minimized. In the pioneering work of Markowitz in 1952, variance was used as a measure of risk, which gave rise to the wellknown mean-variance portfolio optimization model. Although other mean-risk models have been proposed in the literature, the mean-variance model continues to be the backbone of modern portfolio theory and it is still commonly applied. The scope of this thesis is a solution technique for the mean-variance model in which eigendecomposition of the covariance matrix is performed.The first part of the thesis is a review of the mean-risk models that have been suggested in the literature. For each of them, the properties of the model are discussed and the solution methods are presented, as well as some insight into possible areas of future research.The second part of the thesis is two research papers. In the first of these, a solution technique for solving the mean-variance problem is proposed. This technique involves making an eigendecomposition of the covariance matrix and solving an approximate problem that includes only relatively few eigenvalues and corresponding eigenvectors. The method gives strong bounds on the exact solution in a reasonable amount of computing time, and can thus be used to solve large-scale mean-variance problems.The second paper studies the mean-variance model with cardinality constraints, that is, with a restricted number of securities included in the portfolio, and the solution technique from the first paper is extended to solve such problems. Near-optimal solutions to large-scale cardinality constrained mean-variance portfolio optimization problems are obtained within a reasonable amount of computing time, compared to the time required by a commercial general-purpose solver.v Populärvetenskaplig sammanfattningFör den som har kapital att investera kan det vara svårt att avgöra vilka investeringar som är mest fördelaktiga. Till stöd för beslutet kan matematiska modeller användas och denna avhandling handlar om hur man kan beräkna lösningar till sådana modeller. De investeringsalternativ som betraktas är finansiella instrument som är föremål för daglig handel, som aktier och obligationer.En investerare placerar kapital i finansiella instrument eftersom de förväntas ge en god avkastning över tiden. Samtidigt är sådana placeringar alltid förknippade med risktagande. Förväntad avkastning och risk varierar kraftigt mellan olika instrument. Till exempel ger placeringar i statsobligationer typiskt mycket låg avkastning till mycket låg risk, medan placeringar i aktier i nystartade bolag som utvecklar nya läkemedel kan ge mycket hög avkastning samtidigt som risken är mycket hög.Att investera kan ses som en avvägning mellan den förväntade avkastningen och den risk som investeringen innebär, och typiskt är hög förväntad avkastning också associerade med e...

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Robust investment strategies with discrete asset choice constraints using DC programming

Cited by 23 publications

References 16 publications

A Cross-Efficiency Approach for Evaluating Decision Making Units in Presence of Undesirable Outputs

A Cross-Efficiency Approach for Evaluating Decision Making Units in Presence of Undesirable Outputs

Solving continuous min max problem for single period portfolio selection with discrete constraints by DCA

Mean-Variance Portfolio Optimization : Eigendecomposition-Based Methods

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