MULTIOBJECTIVE MODEL PREDICTIVE CONTROL IN STOCK PORTFOLIO OPTIMIZATION

  • Marlina Y Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia https://orcid.org/0009-0001-7958-2706
  • Solikhatun Solikhatun Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia https://orcid.org/0009-0007-3588-0975
DOI: https://doi.org/10.30598/barekengvol19iss3pp2191-2206
Keywords: Model Predictive Control, Multi-Objective Optimization, Particle Swarm Optimization, Stock Optimization

Abstract

This paper proposes a Multi-Objective Model Predictive Control (MO-MPC) framework for stock portfolio optimization, designed to achieve an optimal balance between return maximization and risk minimization in volatile financial markets. This approach integrates Stochastic Model Predictive Control (SMPC) to predict asset returns and dynamically adjust portfolio allocation based on a discrete-time state-space model. The optimization problem is formulated as a multi-objective optimization and is solved using Multi-Objective Particle Swarm Optimization (MOPSO). Simulation results show that the MO-MPC approach significantly outperforms conventional methods regarding wealth maximization and risk minimization. Moreover, SMPC performs better than MOPSO in maximizing portfolio value and reducing risk. These findings confirm the potential of SMPC as an adaptive and reliable strategy for financial decision-making under uncertainty.

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References

J. Sen and S. Dasgupta, “PORTFOLIO OPTIMIZATION: A COMPARATIVE STUDY,” 2024, doi: 10.5772/intechopen.112407.doi: https://doi.org/10.5772/intechopen.112407

C. Jiang, “RESEARCH ON PORTFOLIO IN THE US STOCK,” Highlights in Business Economics and Management, vol. 24, pp. 1331–1337, 2024, doi: https://doi.org/10.54097/tm72qg20.

Y. L. Becker, H. Fox, and F. Peng, “AN EMPIRICAL STUDY OF MULTI-OBJECTIVE ALGORITHMS FOR STOCK RANKING,” pp. 239–259, doi: https://doi.org/10.1007/978-0-387-76308-8_14.

W. Chen, Z. Liu, and L. Jia, “A HYBRID APPROACH FOR PORTFOLIO CONSTRUCTION: COMBING TWO‐STAGE ENSEMBLE FORECASTING MODEL WITH PORTFOLIO OPTIMIZATION,” Comput Intell, vol. 40, no. 2, 2023, doi: https://doi.org/10.1111/coin.12617.

S. Cahyaningtias, T. Asfihani, and S. Subchan, “DESIGN CONTROL OF SURFACE MARINE VEHICLE USING DISTURBANCE COMPENSATING MODEL PREDICTIVE CONTROL (DC-MPC),” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 15, no. 1, pp. 167–178, Mar. 2021, doi: https://doi.org/10.30598/barekengvol15iss1pp167-178.

J. Teller, A. Kock, and H. G. Gemünden, “RISK MANAGEMENT IN PROJECT PORTFOLIOS IS MORE THAN MANAGING PROJECT RISKS: A CONTINGENCY PERSPECTIVE ON RISK MANAGEMENT,” Project Management Journal, vol. 45, no. 4, pp. 67–80, 2014, doi: https://doi.org/10.1002/pmj.21431.

N. Bačanin and M. Tuba, “FIREFLY ALGORITHM FOR CARDINALITY CONSTRAINED MEAN-VARIANCE PORTFOLIO OPTIMIZATION PROBLEM WITH ENTROPY DIVERSITY CONSTRAINT,” The Scientific World Journal, vol. 2014, pp. 1–16, 2014, doi: https://doi.org/10.1155/2014/721521.

D. J. Docimo, Z. Kang, K. A. James, and A. G. Alleyne, “PLANT AND CONTROLLER OPTIMIZATION FOR POWER AND ENERGY SYSTEMS WITH MODEL PREDICTIVE CONTROL,” J Dyn Syst Meas Control, vol. 143, no. 8, 2021, doi: https://doi.org/10.1115/1.4050399.

A. N. Fadhilah and A. P. Subriadi, “MEASURING DYNAMIC CAPABILITIES OF IT RESOURCES,” Int J Adv Sci Eng Inf Technol, vol. 11, no. 3, pp. 1132–1142, 2021, doi: https://doi.org/10.18517/ijaseit.11.3.10929.

X. Zhang, W. Zhang, W. Xu, and W. Xiao, “POSSIBILISTIC APPROACHES TO PORTFOLIO SELECTION PROBLEM WITH GENERAL TRANSACTION COSTS AND A CLPSO ALGORITHM,” Comput Econ, vol. 36, no. 3, pp. 191–200, 2010, doi: https://doi.org/10.1007/s10614-010-9220-4.

R. Faia, T. Pinto, Z. Vale, and J. M. Corchado, “STRATEGIC PARTICLE SWARM INERTIA SELECTION FOR ELECTRICITY MARKETS PARTICIPATION PORTFOLIO OPTIMIZATION,” Applied Artificial Intelligence, vol. 32, no. 7–8, pp. 745–767, 2018, doi: https://doi.org/10.1080/08839514.2018.1506971.

A. Swishchuk, “MERTON INVESTMENT PROBLEMS IN FINANCE AND INSURANCE FOR THE HAWKES-BASED MODELS,” 2021, doi: https://doi.org/10.2139/ssrn.3812579.

Syaifudin, W.H., & Putri, E. R. M., THE APPLICATION OF MODEL PREDICTIVE CONTROL ON STOCK PORTFOLIO OPTIMIZATION WITH PREDICTION BASED ON GEOMETRIC BROWNIAN MOTION KALMAN FILTER, Journal of Industrial and Management Optimization, vol. 18, no. 5, pp 3433-3443, 2022, doi: https://doi.org/10.3934/jimo.2021119.

R. Chen, W. K. Huang, and S.-K. Yeh, “PARTICLE SWARM OPTIMIZATION APPROACH TO PORTFOLIO CONSTRUCTION,” Intelligent Systems in Accounting Finance & Management, vol. 28, no. 3, pp. 182–194, 2021, doi: https://doi.org/10.1002/isaf.1498.

S. Anam, M. R. A. Putra, Z. Fitriah, I. Yanti, N. Hidayat, and D. M. Mahanani, “HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 2, pp. 0797–0806, Jun. 2023, doi: https://doi.org/10.30598/barekengvol17iss2pp0797-0806.

D. Zhou, Y. Li, B. Jiang, and W. Jun, “A NOVEL MULTIOBJECTIVE QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION BASED ON THE RING MODEL,” Math Probl Eng, vol. 2016, pp. 1–15, 2016, doi: https://doi.org/10.1155/2016/4968938.

H. Zhu, Y. Wang, K. Wang, and Y. Chen, “PARTICLE SWARM OPTIMIZATION (PSO) FOR THE CONSTRAINED PORTFOLIO OPTIMIZATION PROBLEM,” Expert Syst Appl, vol. 38, no. 8, pp. 10161–10169, 2011, doi: https://doi.org/10.1016/j.eswa.2011.02.075.

S. Syaripuddin, F. D. T. Amijaya, W. Wasono, S. Tulzahrah, and R. Suciati, “APPLICATION OF QUADRATIC PROGRAMMING ON PORTFOLIO OPTIMIZATION USING WOLFE’S METHOD AND PARTICLE SWARM OPTIMIZATION ALGORITHM,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 18, no. 2, pp. 1067–1080, May 2024, doi: https://doi.org/10.30598/barekengvol18iss2pp1067-1080.

Q. Zhai, Y. Tao, M. Huang, S. Feng, and H. Li, “Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization,” Comput Intell Neurosci, vol. 2020, pp. 1–19, 2020, doi: https://doi.org/10.1155/2020/8834162.

N. None et al., “Selection of Appropriate Portfolio Optimization Strategy,” vol. 1, no. 1, pp. 58–81, 2023, doi: 10.31181/taci1120237.

K. Zaheer, M. I. B. A. Aziz, A. N. Kashif, and S. M. M. Raza, “Two Stage Portfolio Selection and Optimization Model With the Hybrid Particle Swarm Optimization,” Matematika, pp. 125–141, 2018, doi: https://doi.org/10.11113/matematika.v34.n1.1001.

M. Butler and D. Kazakov, “Particle Swarm Optimization of Bollinger Bands,” pp. 504–511, 2010, doi: 10.1007/978-3-642-15461-4_50.doi: https://doi.org/10.1007/978-3-642-15461-4_50

Published
2025-07-01
How to Cite
[1]
M. Y and S. Solikhatun, “MULTIOBJECTIVE MODEL PREDICTIVE CONTROL IN STOCK PORTFOLIO OPTIMIZATION”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 2191-2206, Jul. 2025.
Issue
Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 Marlina Y, Solikhatun Solikhatun

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