Abstract
In stock market, selection of best combination of stocks to obtain maximum return with minimum portfolio risk is the most important issue in portfolio management. This paper presents a novel portfolio selection scheme using golden eagle optimization (GEO) approach with the aim of optimizing return and risk. GEO is a nature inspired approach to mimic the haunting behavior of the golden eagle. To conduct the performance evaluation, an experimental study has been conducted with a comparative analysis of proposed GEO based solution results with the results of artificial bee colony (ABC) and invasive weed optimization (IWO) on S&P BSE dataset (30 stocks) of Indian stock exchange. Study shows the better performance of the proposed GEO based solution approach among its peer methods on account of execution time, and obtained optimal solutions on efficient frontiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Markowitz HM (1952) Portfolio selection. J Financ 7(1):77–91
Deng GF, Lin WT, Lo CC (2012) Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Syst Appl 39(4):4558–4566
Tollo DG, Roli A (2008) Metaheuristics for the portfolio selection problem. Int J Opera Res 5(1):13–35
Mohammadi-Balani A, Nayeri MD, Azar A, Taghizadeh-Yazdi M (2021) Golden eagle optimizer: a nature-inspired metaheuristic algorithm. Comput Ind Eng 152:107050
Chen B, Lin Y, Zeng W, Xu H, Zhang D (2017) The mean-variance cardinality constrained portfolio optimization problem using a local search-based multi-objective evolutionary algorithm. Appl Intell 47(2):505–525
Hidayat Y, Lesmana E, Putra AS, Napitupulu H, Supian S (2018) Portfolio optimization by using linear programing models based on genetic algorithm. In: IOP conference series: materials science and engineering 2018. vol 300, no 1. IOP Publishing, pp 012001
Jalota H, Thakur M (2018) Genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint. Int J Syst Assur Eng Manag 9(1):294–305
Shahid M, Ansari MS, Shamim M, Ashraf Z (2022) A stochastic fractal search based approach to solve portfolio selection problem. In: Gunjan VK, Zurada JM (eds) Proceedings of the 2nd international conference on recent trends in machine learning, IoT, smart cities and applications 2021. Lecture notes in networks and systems, vol 237. Springer, Singapore
Erwin K, Engelbrecht A (2020) Improved set-based particle swarm optimization for portfolio optimization. In: IEEE symposium series on computational intelligence (SSCI) 2020. IEEE, pp 1573–1580
Zaheer KB, AbdAziz MIB, Kashif AN, Raza SMM (2018) Two stage portfolio selection and optimization model with the hybrid particle swarm optimization. MATEMATIKA: Malaysian J Ind Appl Math 125–141
Cura T (2021) A rapidly converging artificial bee colony algorithm for portfolio optimization. Knowl-Based Syst 233:107505
Sahala AP, Hertono GF, Handari BD (2020) Implementation of improved quick artificial bee colony algorithm on portfolio optimization problems with constraints. In: AIP conference proceedings 2020, vol 2242, no 1. AIP Publishing LLC, pp 030008
Ray J, Bhattacharyya S, Singh NB (2019) Conditional value-at-risk-based portfolio optimization: an ant colony optimization approach. In: Metaheuristic approaches to portfolio optimization 2019. IGI Global, pp 82–108
Abolmaali S, Roodposhti FR (2018) Portfolio optimization using ant colony method a case study on Tehran stock exchange. J Account 8(1)
Kalayci CB, Polat O, Akbay MA (2020) An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm Evolut Comput 54
Suthiwong D, Sodanil M, Quirchmayr G (2019) An Improved quick artificial bee colony algorithm for portfolio selection. Int J Comput Intell Appl 18(01):1950007
Rezani MA, Hertono GF, Handari BD (2020) Implementation of iterative k-means and ant colony optimization (ACO) in portfolio optimization problem. In: AIP conference proceedings 2020, vol 2242, no 1. AIP Publishing LLC, p 030022
Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148–175
Shahid M, Ansari MS, Shamim M, Ashraf Z (2022) A risk-budgeted portfolio selection strategy using invasive weed optimization. In: Tiwari R, Mishra A, Yadav N, Pavone M (eds) Proceedings of international conference on computational intelligence 2021. Algorithms for intelligent systems. Springer, Singapore, pp 363–371
Shahid M, Ashraf Z, Shamim M, Ansari MS (2022) A novel portfolio selection strategy using gradient-based optimizer. In: Saraswat M, Roy S, Chowdhury C, Gandomi AH (eds) Proceedings of international conference on data science and applications 2021. Lecture notes in networks and systems, vol 287. Springer, Singapore, p 287
Sefiane S, Bourouba H (2017) A cuckoo optimization algorithm for solving financial portfolio problem. Int J Bank Risk Insur 5(2):47
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hasan, F., Ahmad, F., Imran, M., Shahid, M., Ansari, M.S. (2023). Portfolio Selection Using Golden Eagle Optimizer in Bombay Stock Exchange. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-0047-3_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0046-6
Online ISBN: 978-981-99-0047-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)