Stock portfolio optimization in fireworks algorithm using risk value and comparison with Particle Swarm Optimization (PSO)

Document Type : Original Article

Authors

1 PhD Student in Public Administration-Finance, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

2 Associate Prof, Department of Economics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran .

3 Assistant Prof, Department of Industrial Engineering, Naghshejahan Higher Education Institute, Isfahan, Iran.

Abstract

The nature of business and investment activities is such that earning a return requires risk tolerance. Choosing a stock portfolio is a difficult and difficult task that the investor sees in the face of the many and varied choices that she must choose as one of the best methods. The present study deals with the problem of stock portfolio optimization according to the Value at Risk based intelligent fireworks algorithm and compares it with Particle Swarm Optimization algorithm with the historical simulation method using MATLAB software. The parameters of meta-heuristic algorithms were adjusted by Taguchi method using MINITAB software. Not suspended, used. For reliability of the study, generalized Dickey-Fuller test and Phillips-Prone test were used. To evaluate the accuracy of the Conditional Value at Risk model, the kupiec proportion of failure test, Christoffersen independence test and Conditional coverage test are used.  A comparison was also made between the models by Lopez test. The execution time of the Particle Swarm Optimization was less than that of the fireworks algorithm at all three levels of confidence, but the convergence speed of the fireworks algorithm was faster than that of the Particle Swarm Optimization at all levels. Findings showed that the Value at Risk model using the fireworks algorithm, despite the longer execution time due to better convergence speed and higher rank of Lopez test has a more appropriate validity for stock portfolio optimization.

Keywords

References

1.Alimi, A., & Kordestani, G. (2009). The use of Residual Income Valuation Model as a Benchmark for Portfolio Selection. Financial Management perspective, (1). (In Persian)
2.Bahri Sales, J., Pakmaram, A., & Valizadeh, M. (2018). Selection and Portfolio Optimization by Mean–Variance Markowitz Model and Using the Different Algorithms, Financial Knowledge of Securities Analysis, 37(11), 43-53. (In Persian)
3.Bayat, A., & Asadi, L. (2017). Stock Portfolio optimization: Effectiveness of particle swarm optimization and Markowitz model. Financial Engineering and Portfolio Management, 32(8), 63-85. (In Persian)
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.Computation (CEC), 2069-2077.
Cheng, R., Bai, Y., Zhao, Y., Tan, X., & Xu, T. (2019). Improved fireworks algorithm with information exchange for function optimization. Knowledge-Based Systems(163), 82-90.
Cura, (2009).  Particle  Swarm  Optimization  Approach  to  Portfolio Optimization, Nonlinear Analysis: Real World Applications, No.10, p.p.2396–2406.
Estrada, J. (2007). Mean-semivariance behavior: Downside risk and capital asset pricing. International Review of Economics and Finance, 16, 169-185.
8.Fallah, M., & Afsaneh, S. (2020). Comparison of Value Risk Models and Coppola-CVaR in Portfolio Optimization in Tehran Stock Exchange. Financial Management Perspective, 29 (10), 125-146. (In Persian)
9.Fallahpour, S., & Baghban, M. (2015). Application of Copula-CVaR in Portfolio Optimization and Comparative with Mean-CVaR, Quarterly Journal of Economic Research and Policies. 22(72): 155-172. (In Persian)
10.Fallahpour, F., & Rahimi, M. (2015). Estimating Conditional VaR Using Symmetric and Non-Symmetric Autoregressive Models in Old and Oil Markets. Financial Knowledge of Securities Analysis, 26(8), pp 1-18. (In Persian)
11.G‌h‌a‌h‌t‌a‌r‌a‌n‌I, A., & N‌a‌j‌a‌f‌I, A.A. (2014). R‌ob‌ust O‌pti‌mization of the P‌ort‌fo‌lio S‌e‌lec‌tion P‌rob‌lem U‌sing W‌eigh‌ted C‌ond‌it‌io‌nal V‌alue at-R‌isk A‌pp‌ro‌ach, Industrial Engineering & Management.30-1(1.2) :3-10. (In Persian)
Gordan, A., & Baptisa, A. (2002). Economic Implication of Using Mean-VaR Model for Portfolio Selection. Economic Dynamics & Control, 7: 159-193.
13.Khaloozadeh, H., & JamshidiEyni, E. (2016). Using intelligent methods in Solving Constrained Portfolio in Tehran Stock Exchange, Financial Knowledge of Securities Analysis, 29(9), pp 85-96. (In Persian)
Liu, Y.J., Zhang, W.G., & Xu, W.J. (2012). Fuzzy multi-period portfolio selection optimization models using multiple criteria. Automatica, 48 (12), pp. 3042-3053.
15.Malaei, M., SHeikhi, M. J., & KHodamoradi, Saeed. (2009). Optimization of Markowitz Risk Management Models, Value at Risk and Value at Risk Parametric Using local and Global Algorithms in Tehran Stock Exchange. Financial Management Perspective, (1), 67-97. (In Persian)
16.Mohammadi, Sh., Raei, R, & Feizabad, R. (2009).  Forecasting Value-at-Risk Using Conditional Volatility Models: Evidence from Tehran Stock Exchange. Financial Research Journal, 10 (25), pp. 109-124.
17.Mushkhian, S., & Najafi, A. A. (2015). Investment portfolio optimization using multi-objective particle swarm algorithm for a possible multi-period mean-half-variance-skew model. Financial Engineering and Portfolio Management, 23(6), pp 133-147. (In Persian)
18.Raei, R., & Saeedi, A. (2004). Fundamentals of Financial Engineering and Risk Management, The Organization For Researching and Composing University Textbooks in the Humanities, Faculty of Management University of Tehran, First Print Publication, 136-156.
19.Raei, R, & Alibeiki, H. (2010).   Portfolio optimization using particle swarm optimization method, Financial Research Journal.12(29). pp 21-40. (In Persian)
20.Rajabi, M., &  Khaloozadeh, H. (2015).  Optimal Portfolio Prediction in Tehran Stock Market using Multi-Objective Evolutionary Algorithms, NSGA-II and MOPSO, Financial Research Journal. 2(16). pp 253-270. (In Persian)
21.Sadeghi, H., & Shams, M. (2014). Calculating the value at risk based on the Fisher Corniche approximation of the normal distribution, Asset Management & Financing. 2(1), pp 1-20. (In Persian)Computation (CEC),  2069-2077.
Markowitz H. (1952). Portfolio selection, Journal of Finance (7), 77–91.
Zheng, S., Janecek, A., Tan, Y.(2013). Enhanced fireworks algorithm[Z]. IEEE Congress on Evolutionary Computation (CEC), 2013: 2069-2077.
Yin Peng, Y., & Jing Yu, Wang. (2006). A particle swarm optimization approach to the nonlinear resource allocation problem. Applied Mathematics and Computation, 183: 232–242.
Financial Management Perspective
Volume 11, Issue 35
November 2021
Pages 9-37

Files

Share

How to cite

Statistics