Journal of the Korean Society of Industry Convergence (한국산업융합학회 논문집)

The Korean Society of Industry Convergence (한국산업융합학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Portfolios Using Swarm Intelligence Algorithms

군집 지능 알고리즘을 활용한 포트폴리오 연구

  • Woo Sik Lee (College of Business Administration, Gyeongsang National University)
  • 이우식 (경상국립대학교 경영대학)
  • Received : 2024.08.19
  • Accepted : 2024.09.30
  • Published : 2024.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

While metaheuristics have profoundly impacted various fields, domestic financial portfolio optimization research, particularly in asset allocation, remains underdeveloped. This study investigates metaheuristic algorithms for investment strategy optimization. Results reveal that metaheuristic-optimized portfolios outperform the Dow Jones Index in Sharpe ratios, highlighting their potential to significantly enhance risk-adjusted returns. A comparative analysis of Ant Colony Optimization (ACO) and Cuckoo Search Algorithm (CSA) shows CSA's slight superiority in risk-adjusted performance. This advantage is attributed to CSA's maintained randomness and Lévy flight model, which effectively balance local and global search, whereas ACO may converge prematurely due to path reinforcement. These findings underscore metaheuristics' capacity to maximize expected returns at given risk levels, offering flexible, robust solutions for investment strategy optimization.

Keywords

References

  1. D. Ahn, and S. Park, "Linear programming models using a Dantzig type risk f or portf olio optimization," The Korean Journal of applied Statistics, vol. 35, no. 2, pp. 229-250, (2022).
  2. W. Yoo, and Y. Choi, "A Study on the Improvement of Strategic Asset Allocation Using Global Investor's Reference Portfolio," Korea Finance Association Conference, pp. 214-324, (2019).
  3. R. Mansini, W. Ogryczak, M. Speranza, "Twenty years of linear programming based portfolio optimization," European Journal of Operational Research, vol. 234, pp. 518-535, (2014).
  4. T. Kim, "The Optimal Mean-Variance Portfolio Formulation by Mathematical Planning," Korean Society of Industrial and Systems Engineering, vol. 32 no. 4, pp. 63-71, (2009).
  5. E. P. Setiawan, "Comparing bio-inspired heuristic algorithm for the mean-CVaR portfolio optimization," Journal Physic: Conference Series, vol. 1581, no. 1, (2020).
  6. R. Aghamohammadi, R. Tehrani, A. Raad, "Portfolio Optimization Based on Semi Variance and Another Perspective of Value at Risk using NSGA II, MOACO, and MOABC algorithms," Advances in Mathematical Finance & Applications, vol. 7, no. 1, pp. 99-115, (2022).
  7. H. Zhu, Y. Chen, K. Wang, "Swarm Intelligence Algorithms for Portfolio Optimization," Lecture Notes in Computer Science. vol. 6145, Springer, Berlin, pp. 306-313, (2010).
  8. K. Can, O. Polat, M. Akbay, "An efficient hybrid metaheuristicstic algorithm f or cardinality constrained portfolio optimization," Swarm and Evolutionary Computation, vol. 54, (2020).
  9. J. Kim, and J. Lee, "A Study on the Ef f icient Selection of the Assets in the Reduced Search Space using Monte-Carlo Genetic Algorithm," Journal of Korean Institute of Intelligent Systems, vol. 30, no. 1, pp. 21-27, (2020).
  10. P. Jang, "Performance Analysis of Trading Strategy using Gradient Boosting Machine Learning and Genetic Algorithm," Journal of the Korea Society of Computer and Information, vol. 27, no. 11, pp. 147-155, (2022).
  11. S. Kim and C. Kim, and K. Han, "Using Tabu Search for L(2,1)-coloring Problem of Graphs with Diameter 2," Journal of Digital Convergence, vol. 20, no. 2, pp. 345-351, (2022).
  12. W. Lee, "A Study on Portfolios Using Simulated Annealing and Tabu Search Algorithms," Journal of The Korean Society of Industry Convergence, vol. 27, no. 2, pp. 467-473, (2024).
  13. M. Abdel-Basset and R. Mohamed and M. Jameel, "Spider wasp optimizer: a novel metaheuristic optimization algorithm," Artificial Intelligence Review, vol. 56, pp. 11675-11738, (2023).
  14. M. Ghasemi, M. Zare, A. Zahedi, M. Akbari, S. Mirjalili and L. Abualigaph, "Geyser Inspired Algorithm: A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization," Journal of Bionic Engineering, vol. 21, pp. 374-408, (2024).
  15. H. Shehadeh, "Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization," Neural Computing and Applications, vol. 35, pp. 10733-10749, (2023).
  16. N. Dey, "Applications of Ant Colony Optimization and its Variants: Case Studies and New Developments," Springer, (2024).
  17. C. Brown and L. Liebovitch, and R. Glendon, "Levy Flights in Dobe Ju/'hoansi Foraging Patterns," Human Ecology, vol. 35, pp.129-138, (2007).
  18. X. Yang and S. Deb, "Cuckoo search: recent advances and applications," Neural Computing and Applications, vol. 24, pp.169-174, (2013).
  19. Y. Yun, J. Chae, "Development of Cuckoo Search Based Optimization Approach for a Double Row Layout Problem," Journal of Logistics Science & Technology, vol. no. 1, pp. 29-46, (2020).