Making Decision on Selection of Optimal Stock Portfolio Employing Meta Heuristic Algorithms for Multi-Objective Functions Subject to Real-Life Constraints
الموضوعات :Ali Sepehri 1 , Hassan Ghodrati Ghazaani 2 , Hossein Jabbari 3 , Hossein Panahian 4
1 - Department of Industrial Management- Financial, Kashan Branch, Islamic Azad University, Kashan, Iran
2 - Department of Management, Kashan Branch, Islamic Azad University, Kashan, Iran
3 - Department of Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran
4 - Department of Management, Kashan Branch, Islamic Azad University, Kashan, Iran
الکلمات المفتاحية: multi-objective function, Real-Life Constraints, Simulation, Capital Decision Making, Stock Portfolio Optimization,
ملخص المقالة :
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.
[1] Ajdari, F., Rahnamay Roodposhti, F., Hamidian, M., Jafari, S.M., Baghani, A., Select a stock portfolio to invest and identify top companies with the limitations of L and using the machine learning method, Journal of Financial Management Strategy, 2019, (in Persian). Doi: 10.22051/jfm.2019.24505.1968
[2] Aouni, B., Colapinto, C., La Torre, D., Financial portfolio management through the goal programming model: Current state-of-the-art, European Journal of Operational Research, 2014, 234(2), P. 536-5.
[3] Ban, G. Y., El Karoui, N., Lim, A. E. B., Machine Learning and Portfolio Optimization, Management Science, 2018, 64(3), P. 1136-1154. Doi: 10.1287/mnsc.2016.2644
[4] Charnes, A., Cooper, W. W., Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research, 1978, 2(6), P. 429-444. Doi: 10.1016/0377-2217(78)90138-8
[5] Darabi, R., Baghban, M., Application of Clayton Copula in Portfolio Optimization and its Comparison with Markowitz Mean-Variance Analysis, Advances in Mathematical Finance and Applications, 2018, 3(1), P. 33-51. Doi: 10.22034/amfa.2018.539133
[6] Hovd, M., Stoican, F., On the design of exact penalty functions for MPC using mixed integer programming, Computers & Chemical Engineering, 2014,70, P. 104–113. Doi: 10.1016/j.compchemeng.2013.07.001
[7] Kalayci, C.B., Ertenlice, O., Akbay, M.A., A comprehensive review of deterministic models and applications for mean- variance portfolio optimization, Expert Systems with Applications, 2019, 125, P. 345-368.
[8] Khoo, H. L., Dynamic Penalty Function Approach for Ramp Metering with Equity Constraints, Journal of King Saud University – Science, 2011, 23(3), P. 273–279. Doi: 10.1016/j.jksus.2010.12.004
[9] Kubota, K., Takehara, H., Does the Fama and French Five-Factor Model Work Well in Japan? International Review of Finance, 2018, 18(1), P.137-146. Doi: 10.1111/irfi.12126
[10] Kuehn, L-A., Simutin, M., Wang, J. J., A labor capital asset pricing model, The Journal of Finance, 2017,72(5), P. 2131-2178. Doi:10.2139/ssrn.2139850
[11] Markowitz, H., Portfolio Selection, The Journal of Finance, 1952, 7(1), P. 77-91. Doi: 10.2307/2975974
[12] Meghwani, S. S., Thakur, M., Multi-criteria algorithms for portfolio optimization under practical constraints, Swarm and evolutionary computation, 2017, 37, P. 104-125. Doi: 10.1016/j.swevo.2017.06.005
[13] Miryekemami, S. A., Sadeh, E., Amini Sabegh, Z., Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2017, 2(4), P.107-120. Doi: 10.22034/amfa.2017.536271
[14] Rezaei, N., Elmi, Z., Behavioural Finance Models and Behavioural Biases in Stock Price Forecasting, Advances in Mathematical Finance and Applications, 2018, 3(4), P. 67-82. Doi:10.22034/amfa.2019.576127.1118
[15] Roy, R., Shijin, S., Dissecting anomalies and dynamic human capital: The global evidence, Borsa Istanbul Review, 2018, 18(1), P. 1-32. Doi: 10.1016/j.bir.2017.08.005
[16] Sadaf, A., Ghodrati, H., An improved genetic algorithm method for selection and optimizing the share portfolio, International Journal of Computer Science and Mobile Computing, 2015, 4(1), P. 342-353.
[17] Sen, T., Saha, S., Ekbal, A., Laha, A. K., Bi-objective portfolio optimization using archive multi-objective simulated annealing, In 2014 International Conference on High Performance Computing and Applications, ICHPCA 2014: IEEE, 2015. Doi: 10.1109/ICHPCA.2014.7045343
[18] Sharpe, W. F., Mutual Fund Performance, The Journal of Business, 1966, 39(1), P. 119-138. Doi:10.1086/294846
[19] Xinjie, D., Stock Trend Prediction with Technical Indicators using SVM, Stanford University, 2019.
[20] Zanjirdar, M., Kasbi, P., Madahi, Z., Investigating the effect of adjusted DuPont ratio and its components on investor & quot; s decisions in short and long term, Management Science Letters, 2014, 4(3), P.591-596. Doi: 10.5267/j.msl.2014.1.003
