Making Decision on Selection of Optimal Stock Portfolio Employing Meta Heuristic Algorithms for Multi-Objective Functions Subject to Real-Life Constraints

Advances in Mathematical Finance and Applications

مقاله 17، دوره 8، شماره 2، مرداد 2023، صفحه 645-666   

نوع مقاله: َApplied-Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2021.1915292.1525

نویسندگان

Ali Sepehri¹؛ Hassan Ghodrati Ghazaani^{* 2}؛ Hossein Jabbari³؛ Hossein Panahian²

¹Department of Industrial Management- Financial, Kashan Branch, Islamic Azad University, Kashan, Iran

²Department of Management, Kashan Branch, Islamic Azad University, Kashan, Iran

³Department of Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran

چکیده

The purpose of this study is to make investment decisions with the approach of data envelopment analysis and making decision on selection of optimal stock portfolio employing meta heuristic algorithms for multi-objective functions subject to real-life constraints. The statistical population of this research in capital decision-making and selection of the optimal capital composition is 183 of the selected companies of Tehran Stock Exchange, which were finally 42 companies as justified investment options. After measuring the risk and return of efficient companies, the real limitations of the budget, requirements and expectations of the investor, determination and composition of the investment were formulated as a multi-objective model. For optimal decision, the modified genetic meta heuristic algorithm and MATLAB software with dual operators were used. Elimination of the risk minimization function in sensitivity analysis improved the level of decision return but also led to more risk. Eliminating the maximizing return function improved decision-making risk but also reduced investment return. Elimination of investment requirements and expectations improved returns and increased investment risk, but more companies became involved in optimal investment.

کلیدواژه‌ها

Capital Decision Making؛ Simulation؛ Stock Portfolio Optimization؛ Real-Life Constraints؛ Multi-Objective Function

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