Skip to main content

Advertisement

Log in

Using Quadratic Interpolated Beetle Antennae Search for Higher Dimensional Portfolio Selection Under Cardinality Constraints

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

In this paper, we presented a Quadratic Interpolated Beetle Antennae Search (QIBAS), a variant of the Beetle Antennae Search (BAS) algorithm to solve the higher dimensional portfolio selection problem. The computational efficiency of BAS and its probabilistic global convergence made it viable to solve real-world optimization-based problems. Despite its numerous application, it is less accurate, not scalable, and its performance deteriorates as the dimension of the problem increases. To overcome this, QIBAS integrates BAS with the robust approximation of quadratic interpolation. We employed QIBAS to a well-known finance problem known as Portfolio Selection as a testbed. Traditionally, the portfolio problem is modeled as a convex optimization problem, which is efficient to solve but inaccurate. The cardinality constrained model with higher dimensional stock data includes stringent real-world constraints. It is more accurate but computationally challenging and not tractable, making it a perfect candidate to test QIBAS. The primary goal is to minimize the risk and maximize the profit while selecting the portfolio. We included up to 250 companies in simulation and compared the results with BAS and two state-of-the-art swarm metaheuristic algorithms, i.e., Particle Swarm Optimization and Genetic algorithm. The results showed the promising performance of QIBAS in comparison with other algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • AlHalaseh, R. H. S., Islam, A., & Bakar, R. (2019). An extended stochastic goal mixed integer programming for optimal portfolio selection in the amman stock exchange. International Journal of Financial Research, 10(2), 36–51.

    Article  Google Scholar 

  • Baykasoğlu, A., Yunusoglu, M. G., & Özsoydan, F. B. (2015). A grasp based solution approach to solve cardinality constrained portfolio optimization problems. Computers & Industrial Engineering, 90, 339–351.

    Article  Google Scholar 

  • Bian, B., Chen, X., Dai, M., & Qian, S. (2019). Penalty method for portfolio selection with capital gains tax. Available at SSRN 3441553.

  • Chang, T.-J., Meade, N., Beasley, J. E., & Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, 27(13), 1271–1302.

    Article  Google Scholar 

  • Cheng, L., Liu, W., Yang, C., Huang, T., Hou, Z.-G., & Tan, M. (2017). A neural-network-based controller for piezoelectric-actuated stick-slip devices. IEEE Transactions on Industrial Electronics, 65(3), 2598–2607.

    Article  Google Scholar 

  • Chen, D., Li, S., Wu, Q., & Luo, X. (2019). New disturbance rejection constraint for redundant robot manipulators: An optimization perspective. IEEE Transactions on Industrial Informatics, 16(4), 2221–32.

    Article  Google Scholar 

  • Chen, Y.-T., Sun, E. W., & Yu, M.-T. (2018). Risk assessment with wavelet feature engineering for high-frequency portfolio trading. Computational Economics, 52(2), 653–684.

    Article  Google Scholar 

  • Cui, X., Gao, J., & Shi, Y. (2019) Multi-period mean–variance portfolio optimization with management fees. Operational Research, 1–22.

  • Davis, M. H., & Norman, A. R. (1990). Portfolio selection with transaction costs. Mathematics of operations research, 15(4), 676–713.

    Article  Google Scholar 

  • De Nard, G., Ledoit, O., & Wolf, M. (2018). Factor models for portfolio selection in large dimensions: The good, the better and the ugly. Journal of Financial Econometrics, 19(2), 236–57.

    Article  Google Scholar 

  • Elton, E. J., Gruber, M. J., & Padberg, M. W. (1977). Simple rules for optimal portfolio selection: The multi group case. Journal of Financial and Quantitative Analysis, 12, 329–345.

    Article  Google Scholar 

  • Gong, C., Xu, C., & Wang, J. (2018). An efficient adaptive real coded genetic algorithm to solve the portfolio choice problem under cumulative prospect theory. Computational Economics, 52(1), 227–252.

    Article  Google Scholar 

  • Jiang, X., & Li, S. (2017) Bas: Beetle antennae search algorithm for optimization problems. arXiv preprint arXiv:1710.10724.

  • Jiang, X., & Li, S. (2017) Beetle antennae search without parameter tuning (bas-wpt) for multi-objective optimization. arXiv preprint arXiv:1711.02395.

  • Katsikis, V. N. (2007). Computational methods in portfolio insurance. Applied Mathematics and Computation, 189(1), 9–22.

    Article  Google Scholar 

  • Katsikis, V. N. (2008). Computational methods in lattice-subspaces of c [a, b] with applications in portfolio insurance. Applied Mathematics and Computation, 200(1), 204–219.

    Article  Google Scholar 

  • Katsikis, V. N. (2009). A matlab-based rapid method for computing lattice-subspaces and vector sublattices of rn: Applications in portfolio insurance. Applied Mathematics and Computation, 215(3), 961–972.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. in Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, pp. 1942–1948, IEEE.

  • Khan, A.T., Senior, S.L., Stanimirovic, P.S., & Zhang, Y. (2018). Model-free optimization using eagle perching optimizer. arXiv preprint arXiv:1807.02754.

  • Khan, A. T., Cao, X., Brajevic, I., Stanimirovic, P. S., Katsikis, V. N., & Li, S. (2022). Non-linear activated beetle antennae search: A novel technique for non-convex tax-aware portfolio optimization problem. Expert Systems with Applications, 197, 116631.

    Article  Google Scholar 

  • Khan, A. H., Cao, X., Katsikis, V. N., Stanimirović, P., Brajević, I., Li, S., et al. (2020). Optimal portfolio management for engineering problems using nonconvex cardinality constraint: A computing perspective. IEEE Access, 8, 57437–57450.

    Article  Google Scholar 

  • Khan, A. T., Cao, X., & Li, S. (2022). Dual beetle antennae search system for optimal planning and robust control of 5-link biped robots. Journal of Computational Science, 60, 101556.

    Article  Google Scholar 

  • Khan, A. T., Cao, X., Li, S., Hu, B., & Katsikis, V. N. (2020). Quantum beetle antennae search: A novel technique for the constrained portfolio optimization problem. Science China Information Sciences, 64(5), 1–4.

    Google Scholar 

  • Khan, A. T., Cao, X., Li, S., Katsikis, V. N., Brajevic, I., & Stanimirovic, P. S. (2022). Fraud detection in publicly traded us firms using beetle antennae search: A machine learning approach. Expert Systems with Applications, 191, 116148.

    Article  Google Scholar 

  • Khan, A. T., Cao, X., Li, Z., & Li, S. (2022). Evolutionary computation based real-time robot arm path-planning using beetle antennae search. EAI Endorsed Transactions on AI and Robotics, 1, 1–10.

    Article  Google Scholar 

  • Khan, A. T., & Li, S. (2022). Smart surgical control under rcm constraint using bio-inspired network. Neurocomputing, 470, 121–129.

    Article  Google Scholar 

  • Khan, A. T., & Li, S. (2022). Human guided cooperative robotic agents in smart home using beetle antennae search. Science China Information Sciences, 65(2), 1–7.

    Article  Google Scholar 

  • Khan, A. T., Li, S., & Cao, X. (2021). Control framework for cooperative robots in smart home using bio-inspired neural network. Measurement, 167, 108253.

    Article  Google Scholar 

  • Ledoit, O., & Wolf, M. (2017). Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets goldilocks. The Review of Financial Studies, 30(12), 4349–4388.

    Article  Google Scholar 

  • Liao, L., & Ouyang, Z. (2020). Beetle antennae search based on quadratic interpolation. Application Research of Computers, 38(3).

  • Liu, Y.-J., & Zhang, W.-G. (2019). Possibilistic moment models for multi-period portfolio selection with fuzzy returns. Computational Economics, 53(4), 1657–1686.

    Article  Google Scholar 

  • Li, B., Zhu, Y., Sun, Y., Aw, G., & Teo, K. L. (2018). Multi-period portfolio selection problem under uncertain environment with bankruptcy constraint. Applied Mathematical Modelling, 56, 539–550.

    Article  Google Scholar 

  • Malandri, L., Xing, F. Z., Orsenigo, C., Vercellis, C., & Cambria, E. (2018). Public mood-driven asset allocation: The importance of financial sentiment in portfolio management. Cognitive Computation, 10(6), 1167–1176.

    Article  Google Scholar 

  • Marjanovic, B. (2017). Huge stock market dataset, version 3. 2017. Retrieved October, 2019 from www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs/.

  • Rubinstein, M. (2002). Markowitz’s" portfolio selection": A fifty-year retrospective. The Journal of Finance, 57(3), 1041–1045.

    Article  Google Scholar 

  • Sánchez, J. C. M., Sotres-Ramos, D., & Guzmán, M. E. R. (2016). Portfolio selection with conditional covariance matrix and nonlinear programming. Advances and Applications in Statistics, 49(5), 343.

    Article  Google Scholar 

  • Sheng, D.-L., & Shen, P. (2020). Portfolio optimization with asset-liability ratio regulation constraints. Complexity. https://doi.org/10.1155/2020/1435356.

    Article  Google Scholar 

  • Singh, G., Rattan, M., Gill, S. S., & Mittal, N. (2019). Hybridization of water wave optimization and sequential quadratic programming for cognitive radio system. Soft Computing, 23(17), 7991–8011.

    Article  Google Scholar 

  • Wang, J., & Chen, H. (2018). Bsas: Beetle swarm antennae search algorithm for optimization problems. arXiv preprint arXiv:1807.10470.

  • Wang, L., Niu, Q., & Fei, M. (2007) A novel quantum ant colony optimization algorithm. in International Conference on Life System Modeling and Simulation, pp. 277–286, Springer.

  • Wang, S.-C. (2003). Genetic algorithm. Interdisciplinary Computing in Java Programming (pp. 101–116). Berlin: Springer.

    Chapter  Google Scholar 

  • Wang, Q., Chen, S., & Luo, X. (2019). An adaptive latent factor model via particle swarm optimization. Neurocomputing, 369, 176–84.

    Article  Google Scholar 

  • Wu, X.-L., & Liu, Y.-K. (2012). Optimizing fuzzy portfolio selection problems by parametric quadratic programming. Fuzzy Optimization and Decision Making, 11(4), 411–449.

    Article  Google Scholar 

  • Wu, Q., Shen, X., Jin, Y., Chen, Z., Li, S., Khan, A. H., & Chen, D. (2019). Intelligent beetle antennae search for uav sensing and avoidance of obstacles. Sensors, 19(8), 1758.

    Article  Google Scholar 

  • Yang, S., Wang, M. et al., (2004). A quantum particle swarm optimization. in Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), vol. 1, pp. 320–324, IEEE.

  • Yang, X., Lin, H., Zhang, Y., et al. (2020). Boosting exponential gradient strategy for online portfolio selection: An aggregating experts’ advice method. Computational Economics, 55(1), 231–251.

    Article  Google Scholar 

  • Yang, C., Peng, G., Li, Y., Cui, R., Cheng, L., & Li, Z. (2018). Neural networks enhanced adaptive admittance control of optimized robot-environment interaction. IEEE transactions on cybernetics, 49(7), 2568–2579.

    Article  Google Scholar 

  • Young, M. R. (1998). A minimax portfolio selection rule with linear programming solution. Management science, 44(5), 673–683.

    Article  Google Scholar 

  • Zaheer, K. B., Abd Aziz, M. I. B., Kashif, A. N., & Raza, S. M. M. (2018). Two stage portfolio selection and optimization model with the hybrid particle swarm optimization. Matematika: Malaysian Journal of Industrial and Applied Mathematics, 34(1), 125–141.

    Article  Google Scholar 

  • Zhu, Z., Zhang, Z., Man, W., Tong, X., Qiu, J., & Li, F. (2018). A new beetle antennae search algorithm for multi-objective energy management in microgrid. in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1599–1603, IEEE.

  • Zhu, H., Wang, Y., Wang, K., & Chen, Y. (2011). Particle swarm optimization (pso) for the constrained portfolio optimization problem. Expert Systems with Applications, 38(8), 10161–10169.

    Article  Google Scholar 

Download references

Funding

There is no funding to mention.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xinwei Cao.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, A.T., Cao, X. & Li, S. Using Quadratic Interpolated Beetle Antennae Search for Higher Dimensional Portfolio Selection Under Cardinality Constraints. Comput Econ 62, 1413–1435 (2023). https://doi.org/10.1007/s10614-022-10303-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-022-10303-0

Keywords

Navigation