Multistage, Multiswarm Particle Swarm Optimization for Investment Portfolio Selection
Resumo
This study proposes a new metaheuristic-based approach, identified in the literature as MS2PSO, to solve the problem of investment portfolio selection in the Brazilian stock market. The study was divided into two experiments that evaluated the quality of the solutions obtained by the algorithm and the effectiveness of the recommended portfolios in different time windows and risk profiles. The results indicated that MS2PSO presented slower convergence but offered more satisfactory results compared to PSO. In addition, the portfolios recommended by the proposed method showed positive and negative performances according to the risk profile, and all of them outperformed the benchmark in terms of the overall highest gain obtained during the period. This study contributes to the development of the area of finance and economics by providing a sophisticated and efficient solution for investment portfolio selection.
Palavras-chave:
Portfolio selection problem, Mean-variance, Metaheuristic, Brazilian stock market
Referências
Chang, T. J., Meade, N., Beasley, J. E., and Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers and Operations Research, 27:1271– 1302.
Cura, T. (2021). A rapidly converging artificial bee colony algorithm for portfolio optimization. Knowledge-Based Systems, 233(107505).
Dhaini, M. and Mansour, N. (2021). Squirrel search algorithm for portfolio optimization. Expert Systems with Applications, 178:114968.
Kalayci, C. B., Polat, O., and Akbay, M. A. (2020). An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm and Evolutionary Computation, 54(100662).
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 International Conference on Neural Networks, volume 4, pages 1942–1948, Perth. IEEE.
Kumar, A. (2021). Multi-stage, multi-swarm pso for joint optimization of well placement and control.
Li, X. and Clerc, M. (2019). Swarm intelligence. In Gendreau, M. and Potvin, J.-Y., editors, Handbook of Metaheuristics, volume 272 of International Series in Operations Research and Management Science, chapter 11, pages 353–384. Springer, Cham, 3 edition.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1):77–91.
Ratnaweera, A., Halgamuge, S., and Watson, H. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 8(3):240–255.
Shi, Y. and Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In Porto, V. W., Saravanan, N., Waagen, D., and Eiben, A. E., editors, Evolutionary Programming VII, pages 591–600, Berlin, Heidelberg. Springer Berlin Heidelberg.
Silva, Y. L. T., Herthel, A. B., and Subramanian, A. (2019). A multi-objective evolutionary algorithm for a class of mean-variance portfolio selection problems. Expert Systems with Applications, 133:225–241.
Wong, W. and Ming, C. I. (2019). A review on metaheuristic algorithms: Recent trends, benchmarking and applications. In 2019 7th International Conference on Smart Computing Communications (ICSCC), pages 1–5.
Cura, T. (2021). A rapidly converging artificial bee colony algorithm for portfolio optimization. Knowledge-Based Systems, 233(107505).
Dhaini, M. and Mansour, N. (2021). Squirrel search algorithm for portfolio optimization. Expert Systems with Applications, 178:114968.
Kalayci, C. B., Polat, O., and Akbay, M. A. (2020). An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm and Evolutionary Computation, 54(100662).
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 International Conference on Neural Networks, volume 4, pages 1942–1948, Perth. IEEE.
Kumar, A. (2021). Multi-stage, multi-swarm pso for joint optimization of well placement and control.
Li, X. and Clerc, M. (2019). Swarm intelligence. In Gendreau, M. and Potvin, J.-Y., editors, Handbook of Metaheuristics, volume 272 of International Series in Operations Research and Management Science, chapter 11, pages 353–384. Springer, Cham, 3 edition.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1):77–91.
Ratnaweera, A., Halgamuge, S., and Watson, H. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation, 8(3):240–255.
Shi, Y. and Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In Porto, V. W., Saravanan, N., Waagen, D., and Eiben, A. E., editors, Evolutionary Programming VII, pages 591–600, Berlin, Heidelberg. Springer Berlin Heidelberg.
Silva, Y. L. T., Herthel, A. B., and Subramanian, A. (2019). A multi-objective evolutionary algorithm for a class of mean-variance portfolio selection problems. Expert Systems with Applications, 133:225–241.
Wong, W. and Ming, C. I. (2019). A review on metaheuristic algorithms: Recent trends, benchmarking and applications. In 2019 7th International Conference on Smart Computing Communications (ICSCC), pages 1–5.
Publicado
06/08/2023
Como Citar
DAL MOLIM, Thales F.; SOUZA, Francisco Carlos M..
Multistage, Multiswarm Particle Swarm Optimization for Investment Portfolio Selection. In: BRAZILIAN WORKSHOP ON ARTIFICIAL INTELLIGENCE IN FINANCE (BWAIF), 2. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 96-107.
DOI: https://doi.org/10.5753/bwaif.2023.230652.
