عنوان مقاله [English]
نویسندگان [English]چکیده [English]
The purpose of this study is to provide an optimal selection model for multi-round equity portfolios based on the value of exposed risk periods, with transaction costs. Multi-stock portfolios allow the investor to revise the contents of the basket over time and adjust it to fit new information. For sample, ten portfolios of five shares were randomly selected from companies listed in Tehran Stock Exchange during the years of 1388-1393, which, with an annual risk-free return (20%), average quarterly returns of more than 0.1, were selected. The proposed model is optimized using two continuous and cumulative particle genetic algorithms. In order to measure the efficiency of the results of the two algorithms, a risk-based value criterion has been used and the result of the research suggests higher efficiency of the results of the particle cumulative algorithm compared to the genetic algorithm.
25. Cairns, A. and Dowd, K. (2003). (UBS Pensions series 17) Long-Term value at risk. Financial Markets Group. London School of Economics and Political Science, London, UK.
26. Carvalho, M. and Ludermir, T.B. (2007). Particle swarm optimization of neural network architectures and weights.Hybrid Intelligent Systems,17-19 Sept. 2007.
27. Cong, F. and Oosterlee, C.W. (2016). Multi-period mean-variance portfolio optimization based on monte-carlo simulation. Journal of Economics Dynamics and control, 64, pp. 23-38.
28. Cura, T. (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear analysis: Real world applications, 10(4), 2396-2406.
29. Huiling, W., Yang, Zeng., Haixiang Yao., (2013), Multi-period Markowitz's mean–variance portfolio selection with state-dependent exit probability, Economic Modelling, Vol 36, PP 69-74.
30. Gülpınar, N. and Rustem, B. (2007). Worst-case robust decisions for multi-period mean-variance portfolio optimization. European Journal of Operational Research, 183(3), pp. 981-1000.
31. Liu, Y.J., Zhang, W.G. and Xu, W.J. (2012). Fuzzy multi-period portfolio selection optimization models using multiple criteria. Automatica, 48 (12), pp. 3042-3053.
32. Mei, X., DeMiguel, V. and Nogales, F.J. (2016). Multiperiod portfolio optimization with multiple risky assets and general transaction costs. Journal of Banking & Finance, 69, pp. 108-120.
33. Mohamed, A. (2005). Would students T-GARCH improve VaR estimates?, Master Thesis, University of Jyvaskyla. Finland.
34. Najafi Moghadam,Ali; Rahnama Roodpooshti,Fraydoon; Farrokhi,Mahvash. (2014).Optimization of Stock Portfolio based of Ant Colony & Greay Theory. IRJABS,VOL 8(7).780-788.
35. Shen, R. and Zhang, S. (2008). Robust portfolio selection based on a multi-stage scenario tree. European Journal of Operational Research, 191 (3), pp. 864-887.
36. Skaf, J. and Boyd, S. (2009). Multi-period portfolio optimization with constraints and transaction costs. Technical report, pp. 1-23.
37. Sun, J., Fnag, W., Wu, X., Lai, C.H. and Xu, W. (2011). Solving the multi-stage portfolio optimization problem with a novel particle swarm optimization. Expert Systems with Applications, 38 (6), pp. 6727-6735.
38. Takano, Y. and Gotoh, J.Y. (2011). Constant rebalanced portfolio optimization under nonlinear transaction costs. Asia-Pacific Finance Markets,18 (2), pp. 191-211.
39. Wei, S.Z. and Ye, Z.X. (2007). Multi-period optimization portfolio with bankruptcy control in stochastic market. Applied Mathematics and Computation, 186 (1), pp. 414-425.
40. Yan, W., Miao, R. and Li, S. (2007). Multi-period semi-variance portfolio selection: Model and numerical solution. Applied Mathematics and Computation, 194 (1), pp. 128-134.
41. Yu, X. (2015). Multi-period Mean-dynamic VaR Optimal Portfolio Selection: Model and Algorithm. The Open Automation and Control Systems Journal, 7(1).
42. Zhang, X.L. and Zhang, K.C. (2009). Using genetic algorithm to solve a new multi-period stochastic optimization model. Journal of Computational and Applied Mathematics, 231(1), pp. 114-123.
43. Zhang, W.G., Liu, Y.J. and Xu, W.J. (2012). A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222 (2), pp. 341-349.