A Hybrid Model of Stochastic Dynamic Programming and Genetic Algorithm for Multistage Portfolio Optimization with GlueVaR Risk Measurement

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Industrial Management, Faculty of Economics and Management, Semnan University, Semnan, Iran.

2 Prof., Department of Industrial Management, Faculty of Economics and Management, Tarbiat Modares University, Tehran, Iran.

3 Assistant Prof., Department of Mathematical Finance, Kharazmi University, Tehran, Iran.

4 Assistant Prof., Department of Industrial Management, Faculty of Economics and Management, Semnan University, Semnan, Iran.

Abstract

Objective: The selection of an optimal investment portfolio for a long-term period does not seem logical. So the investors should update their investment portfolios over specific time periods if needed. Since the problem dimensions significantly increase after the periods, a definitive solution to the problem is not achievable.
Methods: In this regard, the Multistage Approximate Stochastic Dynamic Programming has been used to make the best portfolio over each period by using a stochastic return rate. The Monte Carlo was used for scenario development, and GlueVar was selected as a risk measurement criterion. The approximation technique was used to resolve for large dimensions; however, some optimized solutions may be eliminated so we used the Genetic Algorithm for the rapid search around the optimal solution to obtain a better one, if possible.
Results: Top 100 companies listed in the Tehran Stock Exchange between 2011 and 2017 were investigated. This study investigated and compared the return and risk of investment portfolios based on the proposed method, Genetic Algorithm, and stock portfolio with equal weights. The modeling was done with MATLAB and tests were carried out with SPSS.
Conclusion: The results indicated a higher performance of the proposed method in comparison with the other mentioned methods.

Keywords

References

ابریشمی، آذین؛ یوسفی زنوز، رضا (1394). انتخاب سبد سهام با استفاده از بهینه­سازی استوار. تحقیقات مالی، 16 (2)، 201- 218.
پاکمرام، عسگر؛ بحری ثالث، جمال؛ ولی­زاده، مصطفی (1396). انتخاب ­و بهینه­سازی سبد سهام با ­استفاده از الگوریتم ژنتیک، با بهره­گیری از مدل میانگین ـ نیمه واریانس مارکوویتز. فصلنامه مهندسی مالی و مدیریت اوراق بهادار، 8 (31)، 19- 42.
خالوزاده، حمید؛ امیری، نسیبه (1385). تعیین سبد سهام بهینه در بازار بورس ایران بر اساس نظریه ارزش در معرض ریسک. مجله تحقیقات اقتصادی، (73)، 211- 231.  
رجبی، مهسا؛ خالوزاده، حمید (1395). بهینه­سازی و مقایسه سبد سهام در بورس اوراق بهادار تهران با بهره­مندی از الگوریتم­های بهینه­سازی تکاملی چندهدفه. تحقیقات مالی، 16 (2)، 253-270.
شریفی سلیم، علیرضا؛ مؤمنی، منصور؛ مدرس یزدی، محمد؛ راعی، رضا (1394). برنامه­ریزی تصادفی چندهدفه برای انتخاب سبد سهام، مدیریت صنعتی، 7 (3)، 489-510.
عبدالعلی­زاده شهیر، سیمین؛ عشقی، کوروش (1382). کاربرد الگوریتم ژنتیک در انتخاب یک مجموعه دارایی از سهام بورس اوراق بهادار. فصلنامه پژوهش­های اقتصادی ایران، 17 (5)، 175- 192.
علی­پور جورشری، ارمغان؛ یاکیده، کیخسرو؛ محفوظی، غلامرضا (1396). بهینه­سازی سبد سهام با حداقل میانگین انحرافات مطلق کارایی­های متقاطع، مدیریت صنعتی، 9 (3)، 475-496.
 قدوسی، سعید؛ تهرانی، رضا؛ بشیری، مهدی (1391). بهینه­سازی سبد سهام با استفاده از روش تبرید شبیه­سازی‌شده. تحقیقات مالی، 17 (1)، 141- 158.
گودرزی، مهشید؛ یاکیده، کیخسرو؛ محفوظی، غلامرضا (1395). بهینه­سازی سبد سهام با تلفیق کارایی متقاطع و نظریه بازی­ها. مدیریت صنعتی، 8 (4)، 685-706.
محبی، نگین؛ نجفی، امیرعباس (1397). بهینه‌سازی سبد سرمایه‌گذاری چنددوره‎ای با رویکرد برنامه‌ریزی پویا.  مطالعات مدیریت صنعتی، 16(50)، 1-26.
 
References
Abdolalizade Shahir, S., & Eshghi, K. (2004). Using Genetic Algorithm in Selecting a Portfolio in Stock Exchange. Journal of Iran Economic Researches, 17 (5), 175-192. (in Persian)
Abrishami, A., & Yousefi Zenouz, R. (2015) Portfolio Selection by Robust Optimization. Journal of Financial Researches, 16 (2), 201-218. (in Persian)
Azar, A., & Ramouz, N., & Atefatdoust, A. (2012). The Application of Non-inferior Set Estimation (NISE) Method in Optimum Portfolio Selection (Case Study: Tehran Security Exchange). Journal of Financial Researches, 14 (2), 1-14. (in Persian)
Belles-Sampera, J., Guillen, M., Santolino, M. (2014). Beyond Value-at-Risk: GlueVaR distortion risk measure. Risk Analysis, 34(1), 121-134.
Birge, J. R. & Louveaux, F. V. (2000). A multicut algorithm for two-stage stochastic linear programs. European Journal of Operational Research, 34, 384–392.
Chang, T. J., Meade, N., Beasley, J. E. & Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimization. Computers & Operations Research, 27 (13), 1271-1302.
DeMiguel, V., Garlappi, L. & Uppal, R. (2009). Optimal versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy? Review of Financial Studies, 22(5), 1915–1953.
Erica, E., Handari, B. & Hertono, C. (2018). AIP Conference Proceedings Agglomerative clustering and genetic algorithm in portfolio optimization. AIP Conference Proceedings 2023, 02017, https://doi.org/10.1063/1.506421.
Goodarzi, M., Yakideh.K., Mahfoozi.GH. (2017). Portfolio optimization by synthesis of cross efficiency and Game theory. Journal of Industrial Management, 8(4), (2017), 685-706. (in Persian)
Jia, Jianmin & Dyer, James S. (1996). A Standard Measure of Risk and Risk-Value Models. Management Science, 42(12), 1691-1705.
Karamanis, D. (2013). Stochastic Dynamic Programming Methods for the Portfolio Selection Problem. Thesis. London School of Business.
Khalouzadeh, H., Amiri, N. (2006). Optimal Portfolio Selection in Iran Stock Eechange Via Value at Risk Theory. Journal of Economic Researches, (73), 211-231. (in Persian)
Kumar, C., Najmud Doja, M. (2018). A novel framework for portfolio selection model using modified ANFIS and fuzzy sets. Journal of Computers.185(3), 453-485.
Li, P., Han, Y., Xia, Y. (2016). Portfolio Optimization Using Asymmetry Robust Mean Absolute Deviation Model. Finance Research Letters, 21(44), 1-10.
Lin, C.C., Liu, Y.T. (2008). Genetic algorithms for portfolio selection problems with minimum transaction lots. European Journal of Operational Research, 185(1), 393-404.
Pakmaram, A., & Bahri Sales, J., & Valizadeh, M. (2017). Selection and Portfolio Optimization by Genetic Algorithms using the Mean Semi-Variance Markowitz Model. Journal of Financial Engineering and Securities Management, 8 (31), 19-42. (in Persian)
Qodsi, S., Tehrani, R., & Bashiri, M. (2014) Portfolio optimization with simulated annealing algorithm. Journal of Financial Researches, 17 (1), 141-158. (in Persian)
Rajabi, M., & Khaloozadeh, H. (2016). Optimal Portfolio Prediction in Tehran Stock Market using Multi-Objective Evolutionary Algorithms, NSGA-II and MOPSO. Journal of Financial Researches, 16 (2), 253-270. (in Persian)
Rockafellar, R. T. & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21–41.
Roudier, F. (2007). Portfolio optimization and Genetic Algorithms. Thesis. Zurich.
Sampera, J., Guillen, M., Santolino, M. (2014). Beyond Value-at-Risk: GlueVaR distortion risk measure. Risk Anal, 34(1), 121-134.
Sharma, A., Mehra, A. (2016). Financial analysis based sectoral portfolio optimization under second order stochastic dominance. Journal of Management, 2(3), 55–82.
Industrial Management Journal
Volume 11, Issue 3
2019
Pages 517-542

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