Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis

Banihashemi, Shokoofeh; Navidi, Sarah

doi:10.1016/j.orp.2017.02.001

Cited by 28 publications

(15 citation statements)

References 17 publications

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“…Choi and Min [8] conducted a portfolio efficiency measure by incorporating the Treynor and Sharpe indices into the DEA portfolio efficiency index after considering the type of risk in the portfolio in their study. Banihashemi and Navid [9] added CVaR to the input indicators and constructed an evaluation model for application in conjunction with the range directional measure. Zhou et al [10] attempted to improve on the DEA production frontier as well as examining the rebalancing of portfolios.…”

Section: Research On Dea-based Efficiency Evaluationmentioning

confidence: 99%

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Liu

Ding

2021

Sustainability

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Since investors have diverse risk motives for green investments, this paper uses data envelopment analysis (DEA) and simulation to accurately evaluate the efficiency of green portfolios from the perspective of investors’ subjective risks and accordingly provide suitable investment selection strategies. On the one hand, the paper integrates investors’ risk preferences with efficiency evaluation models under the framework of behavioral finance, and then constructs a green portfolio efficiency evaluation model based on cumulative prospect theory on the basis of defining green portfolio efficiency. On the other hand, by bringing realistic Chinese stock data into the evaluation model and solving it with the help of large number iteration and DEA, the trends of frontier movements and selection options of green portfolios under the influence of different risk preferences are obtained and analyzed. The empirical simulation reveals that: (1) if investors’ risk aversion at return rises, it will not only reduce the expected prospective value of the green portfolio, but also shift down and flatten the frontier of the green portfolio; indicating that investors will tend to reduce their risk-tolerant attitude and prefer a conservative strategy under the same value condition. (2) If investors increase their risk-seeking in the case of losses, this will raise the expected prospect value of the green portfolio and lead to an inward and steeper green portfolio frontier; suggesting that, given equal value, investors prefer to increase their risk-taking capacity and use aggressive strategies in the hope of turning the profit around. (3) The efficiency results of green portfolios are very sensitive to changes in investors’ risk preferences, suggesting that investors need to select and match green portfolios with their own risk appetite levels. The above findings enrich and expand the risk types and evaluation models in previous green investment studies from the perspective of investors’ subjective risk.

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Section: Research On Dea-based Efficiency Evaluationmentioning

confidence: 99%

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Liu

Ding

2021

Sustainability

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“…To make the stock portfolio with negative data, Banihashemi et al (2017) applied the mean and variance model, RDM in direction…”

Section: Range Directional Modelmentioning

confidence: 99%

A model to reduce the risk of project selection utilizing data envelopment analysis

Sadeghiyan

Lotfı

Daneshian

et al. 2021

JM2

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Purpose Project selection management is a matter of challenge for project-oriented organizations, particularly, if the decision-makers are confronted with limited resources. One of the main concerns is selecting an optimal subset that can successfully satisfy the requirements of the organization providing enough resources to the best subset of the project. The projects for which there are not enough resources or those requiring whole resources of the organization will collapse soon after failed to success. Therefore, the issue is in the risk of choosing a set of projects so that can make a balance in investment versus on collective benefit. Design/methodology/approach A model is presented for project selection and has been tested on the 37 available projects. This model could increase the efficiency of the whole subset of the project significantly in comparison to the other model and it was because of choosing a diverse subset of projects. Findings Provides a general framework for project selection and a diverse and balanced subset of projects to increase the efficiency of the selected subset. Also, reduces the impact of uncertainty risk on the project selection process. Research limitations/implications For the purposes of project selection, any project whose results are uncertain is a risky project because, if the project fails, it will reduce combined project value. For example, a pharmaceutical company’s R&D project is affected by the uncertain results of a specific compound. If the company invests in different compounds, a failure with one will be offset by a good result on another. Therefore, with selecting a diverse set of projects, this paper will have a different set of risks. Originality/value This paper discusses the risk of selecting or being responsible for selecting a project under uncertainty. Most of the projects in the field of project selection generally consider the risks facing the projects or existing models that do not take into account the risk.

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“…Consequently, portfolios employed stocks with high kurtosis and stocks with depreciative skewness. Banihashemi and Navidi (2017) employee CVaR as a risk measure using various confidence level. They compare two measures of risk such as VaR and CVaR to identify the best one for portfolio optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Portfolio Construction by Using Different Risk Models: A Comparison among Diverse Economic Scenarios

Hunjra

Alawi

Colombage

et al. 2020

Risks

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We aim to construct portfolios by employing different risk models and compare their performance in order to understand their appropriateness for effective portfolio management for investors. Mean variance (MV), semi variance (SV), mean absolute deviation (MaD) and conditional value at risk (CVaR) are considered as risk measures. The price data were extracted from the Pakistan stock exchange, Bombay stock exchange and Dhaka stock exchange under diverse economic conditions such as crisis, recovery and growth. We take the average of GDP of the selected period of each country as a cut-off point to make three economic scenarios. We use 40 stocks from the Pakistan stock exchange, 92 stocks from the Bombay stock exchange and 30 stocks from the Dhaka stock exchange. We compute optimal weights using global minimum variance portfolio (GMVP) for all stocks to construct optimal portfolios and analyze the data by using MV, SV, MaD and CVaR models for each subperiod. We find that CVaR (95%) gives better results in each scenario for all three countries and performance of portfolios is inconsistent in different scenarios.

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Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis

Cited by 28 publications

References 17 publications

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

A model to reduce the risk of project selection utilizing data envelopment analysis

Portfolio Construction by Using Different Risk Models: A Comparison among Diverse Economic Scenarios

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