Markowitz revisited: Social portfolio engineering

Gasser, S.; Rammerstorfer, Margarethe; Weinmayer, Karl

doi:10.1016/j.ejor.2016.10.043

Cited by 93 publications

(55 citation statements)

References 32 publications

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“…The MVA is a quantitative tool that allows the decision maker to make asset allocation by considering the interchange between expected return rate and the measure of risk [17][18][19]. The portfolio optimization employed mean and variance that solved expected return rate for each asset, standard deviation which is measure of risk and covariance matrix between these assets.…”

Section: Mathematical Model 21 Mean-variance Analysismentioning

confidence: 99%

A Modified Boltzmann Machine for Solving Distribution System Expansion Planning in Malaysia

Tahar¹,

Yaakob²,

Ahmed³

2018

IJEECS

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<p>This paper proposes an effective technique to solve Distribution System Expansion Planning (DSEP) problem by using the artificial neural network. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer quadratic programming problem. It consists of two layers neural network which combine Hopfield network and Boltzmann machine (BM) in upper and lower layer respectively named as Modified BM. The originality of the proposed technique is it will delete the unit of the second layer, which is not selected in the first layer in its execution. Then, the second layer is restructured using the selected units. Due to this feature, the proposed technique will improve time consuming and accuracy of solution. Referring to the case study demonstrated in this paper, the significance outputs obtained are the improvement in computational time and accuracy of solution provided. As the solution provided various of options, the proposed technique will help decision makers in solving DSEP problem. As a result, the performance of strategic investment planning in DSEP certainly enhanced.</p>

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Section: Mathematical Model 21 Mean-variance Analysismentioning

confidence: 99%

A Modified Boltzmann Machine for Solving Distribution System Expansion Planning in Malaysia

Tahar¹,

Yaakob²,

Ahmed³

2018

IJEECS

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“…As stated by Brunnermeier and Oehmke [4], complexity is a relevant concept in nance and, in particular, when building an optimization model for portfolio selection. Some authors have been recently concerned about adapting and extending the classical bicriteria Markowitz's meanvariance [5] methodology to integrate additional linear criteria such as dividends, liquidity, or sustainability for a suitable portfolio investor [6][7][8][9][10][11]. In these pioneering works, the above researchers propose exact optimization techniques to solve complex portfolio selection problems although they cannot deal e ciently with nonlinear objectives.…”

Section: Introductionmentioning

confidence: 99%

Computing the Mean‐Variance‐Sustainability Nondominated Surface by ev‐MOGA

et al. 2019

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Despite the widespread use of the classical bicriteria Markowitz mean-variance framework, a broad consensus is emerging on the need to include more criteria for complex portfolio selection problems. Sustainable investing, also called socially responsible investment, is becoming a mainstream investment practice. In recent years, some scholars have attempted to include sustainability as a third criterion to better reflect the individual preferences of those ethical or green investors who are willing to combine strong financial performance with social benefits. For this purpose, new computational methods for optimizing this complex multiobjective problem are needed. Multiobjective evolutionary algorithms (MOEAs) have been recently used for portfolio selection, thus extending the mean-variance methodology to obtain a mean-variance-sustainability nondominated surface. In this paper, we apply a recent multiobjective genetic algorithm based on the concept of ε-dominance called ev-MOGA. This algorithm tries to ensure convergence towards the Pareto set in a smart distributed manner with limited memory resources. It also adjusts the limits of the Pareto front dynamically and prevents solutions belonging to the ends of the front from being lost. Moreover, the individual preferences of socially responsible investors could be visualised using a novel tool, known as level diagrams, which helps investors better understand the range of values attainable and the tradeoff between return, risk, and sustainability.

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“…The authors in [4] used the multiperiod mean-variance model to investigate a defined contribution pension plan investment problem during the accumulation phase. To incorporate social responsibility, a modification of the Markowitz model was proposed in [5]. Konno [6] proposed a mean-absolute deviation portfolio optimization model and applied it to Tokyo stock market.…”

Section: Introductionmentioning

confidence: 99%

Applying Least Squares Support Vector Machines to Mean‐Variance Portfolio Analysis

Wang

Kim

2019

Mathematical Problems in Engineering

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Portfolio selection problem introduced by Markowitz has been one of the most important research fields in modern finance. In this paper, we propose a model (least squares support vector machines (LSSVM)-mean-variance) for the portfolio management based on LSSVM. To verify the reliability of LSSVM-mean-variance model, we conduct an empirical research and design an algorithm to illustrate the performance of the model by using the historical data from Shanghai stock exchange. The numerical results show that the proposed model is useful when compared with the traditional Markowitz model. Comparing the efficient frontier and total wealth of both models, our model can provide a more measurable standard of judgment when investors do their investment.

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Markowitz revisited: Social portfolio engineering

Cited by 93 publications

References 32 publications

A Modified Boltzmann Machine for Solving Distribution System Expansion Planning in Malaysia

A Modified Boltzmann Machine for Solving Distribution System Expansion Planning in Malaysia

Computing the Mean‐Variance‐Sustainability Nondominated Surface by ev‐MOGA

Applying Least Squares Support Vector Machines to Mean‐Variance Portfolio Analysis

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