An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem

Mashayekhi, Zahra; Omrani, Hashem

doi:10.1016/j.asoc.2015.09.018

Cited by 95 publications

(39 citation statements)

References 40 publications

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“…The values of objective function for models (26) and (13) are given by the following two relations: (32) is greater than equation (33). Thus, efficiency scores in models (26) and (27) are smaller than efficiency scores in models (13) and (14).…”

Section: Discussionmentioning

confidence: 99%

“…According to Table 4, the last column includes values of efficiency obtained from executing model (13). For calculating this column, the average values achieved from solving models (26) and (27) for step length of 0.2 have been used. Also, because there are one desirable and two undesirable outputs in this evaluation, [ ].…”

Section: Case Studymentioning

confidence: 99%

“…He also calculated the efficiency of banks. Sorting the genetic algorithm with uncertain data, Mashayekhi and Omrani [26] used a fuzzy approach as well. Aghayi [27] measured cost efficiency with fuzzy data in DEA.…”

Section: Introductionmentioning

confidence: 99%

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A Malmquist Productivity Index with Directional Distance Function and Uncertain Data

2018

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In the present study, by using the directional distance function with undesirable interval outputs, the Malmquist productivity index (MPI) and integrated data envelopment analysis (DEA) are presented for evaluating the function of decision making unites (DMUs). The MPI calculation is performed to compare the efficiency of the DMUs in distinct time periods. The uncertainty inherent in real-world problems is considered by using the best and worst-case scenarios, defining an interval for the MPI and reflecting the DMUs' advancement or regress. The optimal solution of the robust model lies in the efficiency interval, i.e., it is always equal to or less than the optimal solution in the optimistic case and equal to or greater than the optimal solution in the pessimistic case. We also present a case study in the banking industry to demonstrate the applicability and efficacy of the proposed integrated approach.

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Section: Discussionmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

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A Malmquist Productivity Index with Directional Distance Function and Uncertain Data

2018

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“…Their proposed methodology includes three stages: stock screening, portfolio selection with DEA method, and resource allocation. Mashayekhi and Omrani (2016) proposed a multi-objective model for portfolio selection at the stock exchange market. Their model combines DEA with Markovitz mean-variance model by incorporating the risk, return, and efficiency of each portfolio into the modelling.…”

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confidence: 99%

A technology portfolio optimization model considering staged financing and moratorium period under uncertainty

Shaverdi

Yaghoubi

2021

RAIRO-Oper. Res.

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Technology commercialization needs a large amount of financial resources and governments in developed and developing countries play a critical role in resource allocation to the technology commercialization, especially through “Technology Development Funds (TDFs)”. But, because of resource limitations, determining high priority technologies with higher impact on the country’s innovative performance and the optimal resource allocation to technology development is very important for science and technology policymakers. “Technology portfolio planning” has been developed and applied in this regard. Accordingly, a two-phase decision-making framework has been proposed. At the first phase, the priorities of technology fields are determined by using the best-worst method (BWM) and at the second phase, a two-stage stochastic technology portfolio planning model is developed by considering technological projects' risks and export market, as one of the important factor in the “Global Innovation Index” (GII) ranking. It also has been considered technology fields’ priorities, staged-financing, moratorium period, reinvestment strategy, and technology readiness levels (TRL) in allocating financial resources to technological projects. The main advantages of our proposed model are considering uncertainty and early signaling about underperforming technological projects. Due to the uncertain nature of the problem, our solution methodology is based on the Sample Average Approximation (SAA). In order to demonstrate the applicability of this model, a real case study and its computational results are presented.

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“…Quite a few authors use semi-deviation or semi-variance below the mean as a risk measure (downside risk) in portfolio selection [29,39,51,[84][85][86][87][88]. However, given that semi-variance is difficult to calculate [89], we, like many other authors [41,44,52,54,90], use variance in our work.…”

mentioning

confidence: 99%

Sustainability-Oriented Financial Resource Allocation in a Project Portfolio through Multi-Criteria Decision-Making

Dobrovolskienė

Tamošiūnienė

2016

Sustainability

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Abstract:Modern portfolio theory attempts to maximize the expected return of a portfolio for a given level of portfolio risk, or equivalently minimize risk for a given level of expected return. The reality, however, shows that, when selecting projects to a portfolio and allocating resources in the portfolio, an increasing number of organizations take into account other aspects as well. As a result of the sole purpose (risk-return), it offers only a partial solution for a sustainable organization. Existing project portfolio selection and resource allocation methods and models do not consider sustainability. Therefore, the aim of this article is to develop a sustainability-oriented model of financial resource allocation in a project portfolio by integrating a composite sustainability index of a project into Markowitz's classical risk-return scheme (mean-variance model). The model was developed by applying multi-criteria decision-making methods. The practicability of the model was tested by an empirical study in a selected construction company. The proposed sustainability-oriented financial resource allocation model could be used in allocating financial resources in any type of business. The use of the model would not only help organisations to manage risk and achieve higher return but would also allow carrying out sustainable projects, thereby promoting greater environmental responsibility and giving more consideration to the wellbeing of future generations. Moreover, the model allows quantifying the impact of the integration of sustainability into financial resource allocation on the return of a portfolio.

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An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem

Cited by 95 publications

References 40 publications

A Malmquist Productivity Index with Directional Distance Function and Uncertain Data

A Malmquist Productivity Index with Directional Distance Function and Uncertain Data

A technology portfolio optimization model considering staged financing and moratorium period under uncertainty

Sustainability-Oriented Financial Resource Allocation in a Project Portfolio through Multi-Criteria Decision-Making

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