Designing and developing an optimal stock portfolio model using the locust algorithm and comparing it with Markowitz
Subject Areas :Reza Basiri 1 , Saeed Aghasi 2 , Mahdi Ashrafian Qainani 3
1 - Doctoral student of Management Department, Dahaghan Branch, Islamic Azad University, Dahaghan, Iran.
2 - Assistant Professor, Department of Management, Deh.C., Islamic Azad University, Dehagan, Iran (Responsible Author)
3 - senior expert in management department, Dahaghan branch, Islamic Azad University, Dahaghan, Iran
Keywords: Grasshopper Optimization Algorithm, Markowitz Model, Portfolio Optimization, Tehran Stock Exchange,
Abstract :
Objective: To select the optimal stock portfolio using the Grasshopper Optimization Algorithm (GOA) and compare it with the Markowitz model in the Tehran Stock Exchange.
Research Methodology: This study is descriptive-analytical, ex-post facto, and applied. The population includes all companies listed on the Tehran Stock Exchange during 2015 to 2020 (1394 to 1399 Iranian calendar), and the sample consists of 16 more active companies. Data were collected through document analysis, processed in Excel, and analyzed using both the Grasshopper Optimization Algorithm and the Markowitz model.
Findings: The Grasshopper Optimization Algorithm shows lower risk (standard deviation) and higher confidence level compared to the Markowitz model; therefore, based on the minimum risk criterion, the GOA is more efficient than the Markowitz model.
Originality / Scientific Contribution: Applying the metaheuristic Grasshopper Optimization Algorithm for portfolio optimization and scientifically comparing it with the classic Markowitz model in the Tehran Stock Exchange provides a novel and scientific tool for investors to construct portfolios with lower risk.
- Babajani, J., Taghva, M., Blue, G., & Abdollahi, M. (2019). Forecasting Stock Prices In Tehran Stock Exchange Using Recurrent Neural Network Optimized by Artificial Bee Colony Algorithm. Financial Management Strategy, 7(2), 195-228. doi: 10.22051/jfm.2019.21049.1714.[In Persian].
- Bayat, Ali, Asadi, Lida. (2016). Stock portfolio optimization: usefulness of birds algorithm and Markowitz model. Financial engineering and securities management. 8(32). 63-85.[In Persian].
- Khalili-Iraqi, Maryam. (2016). Choosing the optimal share price using optimal planning. Economic research. 6 (20) 214-193. [In Persian].
- Adebiyi, A., Ayo, C., Adebiyi, M.O., Otokiti, S.,( 2012). Stock price prediction using neural network with hybridized market indicators. J. Emerg. Trends Comput. Inf. Sci. 3 (1), 1–9.
- Anagnostopoulos, K. P., & Mamanis, G. (2010). A portfolio optimization model with three objectives and discrete variables. Computers & Operations Research, 37(7), 1285-1297.
- Chang, P.T. & Lee, J. H. (2014). “A Fuzzy DEA & Knapsack formulation integrated model for project selection”. Computer & Operation Research, 39, 112-125.
- Charles, P. Jones, A , 2012 , Investments: Analysis and Management , Wiley
- Chen, J.-S., Lin, Y.-T., (2009). A partitioned portfolio insurance strategy by a relational genetic algorithm. Expert Syst. Appl. 36(2), 727–734
- Saremi. S. Mirjalili,A. Lewis, A (2017) Grasshopper Optimisation Algorithm: Theory and application. Advances in Engineering Software. 105, 30-47
- Yang, J. Xu, J. woo, D. (2012) “A novel portfolio selection model in a hybrid uncertain environment”,omega the International Journal of Management Science, vol. 37, pp. 439-449
- Zhang, C., Zhu, W., Yang, S., 2007. Banking operational risk management on DS evidence theory. In: International Conference on Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007. IEEE, pp. 640–644.
