International Journal of Nonlinear Analysis and Applications
A risk averse robust portfolio optimization under severe uncertainties by using IGDT approach: Iran Stock Exchange
Document Type : Research Paper
Authors
1 Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
2 Faculty of Industrial Engineering, University of Tehran, Iran
Abstract
Portfolio optimization in finance and economy is more than a mathematical model for improving performance under uncertainty constraints. Practically all organizations seek to create value by selecting the best portfolios that consume the least resources and obtaining high expected portfolio returns and controlling risk. In the context of the portfolio selection problem, severe uncertainties would significantly affect the technical and financial aspects. This paper presents a bi-level information gap decision theory (IGDT) risk averse decision-making tool for robust portfolio optimization problems to help organizations or investors for managing their portfolios and finding the best transactions with severe uncertainty variables (price and return) to process the forecast data generated by the learning prediction method in order to construct the optimal stock portfolios that a target profit is guaranteed. The heuristic solution approach is constructed and the augmented ε-constraint method is used to solve the proposed bi-level IGDT robust optimization problem. The effectiveness and efficiency of the proposed model are evaluated on the Iranian Stock Market. The results show the efficiency and effectiveness of the proposed model for selecting the best stocks. The Mont Carlo simulation method is applied for the validation of results.
Keywords
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[18] A. Fertis, M. Baes and H.J. L¨uthi, Robust risk management, Eur. J. Oper. Res. 222 (2012), no. 3, 663–672.
[19] A. Ghadimi Hamzehkolaei, G. Ghodrati Amiri, A. Gharagozlu, A. Vafaeinezhad and A. Zare Hosseinzadeh, Seismic zoning of urban areas considering the effect of physical conditions using Fuzzy logic theory: case study of Tehran’s 7th region, J. Struct. Construct. Engin. 5 (2018), no. 3, 5–15.
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[36] Y. Merzifonluoglu, Risk averse supply portfolio selection with supply, demand and spot market volatility, Omega 57 (2015), 40–53.
[37] A. Meucci, Risk and asset allocation, Springer, New York, 2005.
[38] W.L. Oberkampf, S.M. DeLand, B.M. Rutherford, K.V. Diegert and K.F. Alvin, Error and uncertainty in modeling and simulation, Reliab. Engin. Syst. Safety 75 (2002), no. 3, 333–357.
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[40] Z. Qin, S. Kar and H. Zheng, Uncertain portfolio adjusting model using semiabsolute deviation, Soft Comput. 20 (2016), no. 2, 717–725.
[41] R.T. Rockafellar and S. Uryasev, Optimization of conditional value-at-risk, J. Risk 2 (2000), 21–42.
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[46] W. F. Sharpe, Mean-absolute-deviation characteristic lines for securities and portfolios, Manag. Sci. 18 (1971), no. 2, B-1.
[47] T. Sriyakul and K. Jermsittiparsert, Risk-constrained design of autonomous hybrid refueling station for hydrogen and electric vehicles using information gap decision theory, Int. J. Hydrogen Energy 46 (2021), no. 2, 1682-1693.
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[49] S. Uryasev and P. M. Pardalos, Stochastic optimization: algorithms and applications, Springer Sci. Bus. Media 54 (2013).
[50] Z. Vafaeinezhad, Z. Kazemi, M. Mirmoeini, H. Piroti, E. Sadeghian, M. Mohammad Ali-Vajari, . . . and M. Jafari, Trends in cervical cancer incidence in Iran according to national cancer registry, J. Mazandaran Univer. Med. Sci.28 (2018), no. 161, 108–114.
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[52] S. Zaman and D. Grosu, Combinatorial auction-based allocation of virtual machine instances in clouds, J. Paral. Distrib. Comput. 73 (2013), no. 4, 495–508.
[53] S. Zymler, D. Kuhn and B. Rustem, Worst-case value at risk of nonlinear portfolios, Manag. Sci. 59 (2013), no. 1, 172- 188.
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Volume 14, Issue 4
April 2023Pages 359-385
- Receive Date: 01 December 2021
- Revise Date: 03 January 2022
- Accept Date: 11 February 2022