Document Type : Review Paper
Authors
Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Abstract
A combination of projects, assets, programs, and other components put together in a set is called a portfolio. Arranging these components helps to facilitate the efficient management of the set and subsequently leads to achieving the strategic goals. Generally, the components of the portfolio are quantifiable and measurable which makes it possible for management to manage, prioritize, and measure different portfolios. In recent years, the portfolio in various sectors of economics, management, industry, and especially project management has been widely applied and numerous researches have been done based on mathematical models to choose the best portfolio. Among the various mathematical models, the application of data envelopment analysis models due to the unique features as well as the capability of ranking and evaluating performances has been taken by some researchers into account. In this regard, several articles have been written on selecting the best portfolio in various fields, including selecting the best stocks portfolio, selecting the best projects, portfolio of manufactured products, portfolio of patents, selecting the portfolio of assets and liabilities, etc. After presenting the Markowitz mean-variance model for portfolio optimization, these pieces of research have witnessed significant changes. Moreover, after the presentation of the fuzzy set theory by Professor Lotfizadeh, despite the ambiguities in the selection of multiple portfolios, a wide range of applications in portfolio optimization was created by combining mathematical models of portfolio optimization.
Keywords
- Portfolio Optimization
- Data Envelopment Analysis
- Fuzzy Sets
- Intuitionistic Fuzzy Sets
- Markowitz Model
Main Subjects
- Heybati, F., & Haddadzadeh, R. (2008). Portfolio optimization using markowitz’s mean-semi variance method on tehran stock exchange. Future studies management, 1(19), 39-55. (In Persian). https://jmfr.srbiau.ac.ir/article_5714.html
- Rotela Junior, P., Rocha, L. C. S., Aquila, G., Balestrassi, P. P., Peruchi, R. S., & Lacerda, L. S. (2017). Entropic data envelopment analysis: a diversification approach for portfolio optimization. Entropy, 19(9), 352.
- Tavana, M., Keramatpour, M., Santos-Arteaga, F. J., & Ghorbaniane, E. (2015). A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and integer programming. Expert systems with applications, 42(22), 8432-8444.
- Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30(9), 1078-1092.
- Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
- Atanassov, K. (2016). Intuitionistic fuzzy sets. International Journal Bioautomation, 20, 1.
- Edalatpanah, S. A. (2019). A data envelopment analysis model with triangular intuitionistic fuzzy numbers. International journal of data envelopment analysis, 7(4), 47-58.
- Markowitz, H. M. (1978). Portfolio selection. Wily, New York.
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
- Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the royal statistical society: series A (General), 120(3), 253-281.
- Edalatpanah, S. A. (2018). Neutrosophic perspective on DEA. Journal of applied research on industrial engineering, 5(4), 339-345.
- Sharifighazvini, M. R., Ghezavati, V. R., Raissi, S., & Makui, A. (2018). Integration of a new mcdm approach based on the dea, fanp with monlp for efficiency-risk assessment to optimize project portfolio by branch and bound: a real case-study. Economic computation & economic cybernetics studies & research, 52(1).
- Ahmadi, M., Osman, M. H. M., & Aghdam, M. M. (2020). Integrated exploratory factor analysis and Data Envelopment Analysis to evaluate balanced ambidexterity fostering innovation in manufacturing SMEs. Asia pacific management review, 25(3), 142-155.
- Mashayekhi, Z., & Omrani, H. (2016). An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied soft computing, 38, 1-9.
- Chen, W., Gai, Y., & Gupta, P. (2018). Efficiency evaluation of fuzzy portfolio in different risk measures via DEA. Annals of operations research, 269(1-2), 103-127.
- Chen, W., Li, S. S., Zhang, J., & Mehlawat, M. K. (2020). A comprehensive model for fuzzy multi-objective portfolio selection based on DEA cross-efficiency model. Soft computing, 24(4), 2515-2526.
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