Optimization and active management of Portfolio Using Aunt Colony Algorithm Considering Uncertainty and Robust Programming; Case: Tehran Stock Exchange

Subject Areas : Financial engineering

mohammad hosein  ranjbari vahid ¹ (Department of Financial Management, Faculty of Management, University of Tehran, Alborz Campus، karaj، iran.)
seyed jalal  sadeghi sharif ² (Department of financial management and Accounting Faculty of Management and Accounting, university of Shahid Beheshti Tehran Iran)
reza  eivazlu ³ (Department of financial management and Insurance, Faculty of Management university of Tehran Tehran Iran)
mohsen  mehrara ⁴ (Department of Economics Faculty of Economics, University of Tehran Tehran Iran)

Received: 2019-11-10 Accepted : 2020-02-19 Published : 2020-06-21

Keywords: Uncertainty, Portfolio, CVAR, Key Words: Robust Programming, Active Management,

Abstract :

AbstractBased on Markowitz theory of portfolio optimization, capital market is not predictable by any methods and the risk can only be diversified through portfolio formation and optimization. Recent works made huge developments in the basic model from modeling and risk measures perspectives. Spectral risk measures such as expected shortfall and value at risk are being used frequently as risk measures. In addition, researchers tend to consider uncertainty in risk and return evaluation via fuzzy, stochastic and robust modeling. However, a matter that has been neglected in many researches is portfolio management under uncertainty conditions. This paper propose a method for robust modeling of portfolio optimization and management using expected shortfall as risk measure and Bertsimas modeling as robust programming. The proposed model solved with artificial bee colony algorithm and results show a better performance of proposed model compared to classic methods in both the optimal portfolio formation and its management phase.

References:

1. Lotfi, Somayyeh, and Stavros A. Zenios. "Robust VaR and CVaR optimization under joint ambiguity in distributions, means, and covariances." European Journal of Operational Research2 (2018): 556-576.


2. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.


3. Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.


4. Lin, C. M., & Gen, M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied Mathematical Sciences, 1(5), 201-210.


5. 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.


6. Hao, F. F., & Liu, Y. K. (2009). Mean-variance models for portfolio selection with fuzzy random returns. Journal of Applied Mathematics and Computing, 30(1-2), 9.


7. Chang, T. J., Yang, S. C., & Chang, K. J. (2009). Portfolio optimization problems in different risk measures using genetic algorithm. Expert Systems with Applications, 36(7), 10529-10537.


8. Ben-Tal, A., Margalit, T., & Nemirovski, A. (2000). Robust modeling of multi-stage portfolio problems. In High performance optimization(pp. 303-328). Springer, Boston, MA.


9. Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied mathematics and computation, 214(1), 108-132.


10. Peykani, P., & ROGHANIAN, E. (2015). THE APPLICATION OF DATA ENVELOPMENT ANALYSIS AND ROBUST OPTIMIZATION IN PORTFOLIO SELECTION PROBLEMS.


11. Lin, C. M., & Gen, M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied Mathematical Sciences, 1(5), 201-210.


12. Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations research letters, 25(1), 1-13.


13. Peykani, P., & ROGHANIAN, E. (2015). THE APPLICATION OF DATA ENVELOPMENT ANALYSIS AND ROBUST OPTIMIZATION IN PORTFOLIO SELECTION PROBLEMS.