Preference Based Portfolio for Private Investors: Discrete Choice Analysis Approach

This paper proposes an approach that combines data envelopment analysis (DEA) with the analytic hierarchy process (AHP) and conjoint analysis, as multi-criteria decision-making methods to evaluate teachers’ performance in higher education. This process of evaluation is complex as it involves consideration of both objective and subjective efficiency assessments. The efficiency evaluation in the presence of multiple different criteria is done by DEA and results heavily depend on their selection, values, and the weights assigned to them. Objective efficiency evaluation is data-driven, while the subjective efficiency relies on values of subjective criteria usually captured throughout the survey. The conjoint analysis helps with the selection and determining the relative importance of such criteria, based on stakeholder preferences, obtained as an evaluation of experimentally designed hypothetical profiles. An efficient experimental design can be either symmetric or asymmetric depending on the structure of criteria covered by the study. Obtained importance might be a guideline for selecting adequate input and output criteria in the DEA model when assessing teachers’ subjective efficiency. Another reason to use conjoint preferences is to set a basis for weight restrictions in DEA and consequently to increase its discrimination power. Finally, the overall teacher’s efficiency is an AHP aggregation of subjective and objective teaching and research efficiency scores. Given the growing competition in the field of education, a higher level of responsibility and commitment is expected, and it is therefore helpful to identify weaknesses so that they can be addressed. Therefore, the evaluation of teachers’ efficiency at the University of Belgrade, Faculty of Organizational Sciences illustrates the usage of the proposed approach. As results, relatively efficient and inefficient teachers were identified, the reasons and aspects of their inefficiency were discovered, and rankings were made.

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“…. , I) can be expressed as the ratio of the utility range for a particular attribute and the sum of the utility ranges for all attributes [60]:…”

Section: Conjoint Analysismentioning

confidence: 99%

Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

et al. 2020

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“…. ,..., 1 0 , n j x j   (5) In the MAD model, the objective function is to minimize the absolute deviation of the portfolio.…”

Section: Mean-absolute Deviation Modelmentioning

confidence: 99%

“…The model above [( 8), ( 9), (4), (5)] is equivalent to the linear programming model as shown below:…”

Section: ) ( X Wmentioning

confidence: 99%

“…In MV model, the risk and mean return of investors are measured with portfolio variance and portfolio mean return, respectively. The MV model has been studied in portfolio management according to past research [5][6][7][8][9]. Konno and Yamazaki [10] have introduced the mean-absolute deviation (MAD) model by using absolute deviation in replacement of variance as the portfolio risk measure, because the MV model assumes that the assets' returns are normally distributed.…”

Section: Introductionmentioning

confidence: 99%

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Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model

Siew

Jaaman

2021

Entropy

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Investors wish to obtain the best trade-off between the return and risk. In portfolio optimization, the mean-absolute deviation model has been used to achieve the target rate of return and minimize the risk. However, the maximization of entropy is not considered in the mean-absolute deviation model according to past studies. In fact, higher entropy values give higher portfolio diversifications, which can reduce portfolio risk. Therefore, this paper aims to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by incorporating the maximization of entropy. In addition, the proposed model incorporates the optimal value of each objective function using a goal-programming approach. The objective functions of the proposed model are to maximize the mean return, minimize the absolute deviation and maximize the entropy of the portfolio. The proposed model is illustrated using returns of stocks of the Dow Jones Industrial Average that are listed in the New York Stock Exchange. This study will be of significant impact to investors because the results show that the proposed model outperforms the mean-absolute deviation model and the naive diversification strategy by giving higher a performance ratio. Furthermore, the proposed model generates higher portfolio mean returns than the MAD model and the naive diversification strategy. Investors will be able to generate a well-diversified portfolio in order to minimize unsystematic risk with the proposed model.

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“…In the experimental procedure, individuals are presented with sets of alternatives that differ in the levels of their component attributes, and for each choice set they are asked to choose the most preferred alternative. The outputs from DCA, the so-called utility results, are numerical values that reflect the extent to which each attribute and level influences customer choice (Kuzmanovic et al, 2020).…”

Section: Discrete Choice Analysismentioning

confidence: 99%

Heterogeneity of Serbian consumers' preferences for local wines: Discrete choice analysis

Kuzmanovic

Makajić-Nikolić

2020

Ekonomika poljoprivrede

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The unique and at the same time very complex nature of the wine requires a more thorough selection process compared to other consumer products. Wine consumers are faced with a number of brands, grape varieties, and regions of origin, diverse tastes and prices. Using discrete choice experiment this study explores the importance that consumers in Serbia attach to five attributes of the local wine brands. Respondents were asked to evaluate 10 choice tasks, each with three different wines, and to choose the one that they would like to have with friends or family. The sample results indicate the high importance of the wine brand, while the price proved to be the least important. However, preference-based segmentation identified three clusters that differ primarily in the type of wine they favor, but also whether they like or not sweet and sparkling wines. It turns out that the price is a moderately important attribute in all clusters.

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Preference Based Portfolio for Private Investors: Discrete Choice Analysis Approach

Cited by 8 publications

References 37 publications

Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

Using Data Envelopment Analysis and Multi-Criteria Decision-Making Methods to Evaluate Teacher Performance in Higher Education

Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model

Heterogeneity of Serbian consumers' preferences for local wines: Discrete choice analysis

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