Optimasi Portofolio dengan Pendekatan Kuantum-Inspiratif di Tengah Ketidakpastian Pasar Global

Authors

  • Calvin Reinhard Universitas Diponegoro Semarang
  • Clara Bella Universitas Diponegoro Semarang

DOI:

https://doi.org/10.51903/qg723r30

Keywords:

portfolio optimization, quantum-inspired optimization, market uncertainty

Abstract

This study proposes a Quantum-Inspired Optimization (QIO)-based portfolio optimization model to address financial market dynamics characterized by high volatility and global uncertainty. This model utilizes a Q-bit probabilistic representation and an amplitude rotation mechanism to explore the solution space more adaptively than conventional approaches such as Mean-Variance Markowitz, heuristic Genetic Algorithms, and Particle Swarm Optimization. Daily stock price data from the LQ45 index are used as the test object, with additional external indicators, such as the global volatility index (VIX) and benchmark interest rates, to integrate systemic risk into the optimization process. Simulation results show that QIO produces higher portfolio returns, lower risk, a better Sharpe Ratio, and smaller maximum drawdown compared to benchmark models. These findings demonstrate that the quantum-inspired approach has significant potential for application in modern portfolio management, particularly in volatile market conditions, while also contributing to the development of quantitative methods in finance.

References

Anjum, A., Rao, P. S. V. S., Sindhu, M., Bhatt, P., Priya, M. R., & Maranan, R. (2025). Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs. 2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025 - Proceedings, 1665–1671. https://doi.org/10.1109/ICMLAS64557.2025.10969038

Chaweewanchon, A., & Chaysiri, R. (2022). Markowitz Mean-Variance Portfolio Optimization with Predictive Stock Selection Using Machine Learning. International Journal of Financial Studies, 10(3). https://doi.org/10.3390/ijfs10030064

Chung, M., Lee, Y., Kim, J. H., Kim, W. C., & Fabozzi, F. J. (2022). The Effects of Errors in Means, Variances, and Correlations on the Mean-Variance Framework. Quantitative Finance, 22(10), 1893–1903. https://doi.org/10.1080/14697688.2022.2083009

Desalegn, G., Tangl, A., & Fekete-Farkas, M. (2022). From Short-Term Risk to Long-Term Strategic Challenges: Reviewing the Consequences of Geopolitics and COVID-19 on Economic Performance †. Sustainability (Switzerland), 14(21). https://doi.org/10.3390/su142114455

Edirisinghe, C., & Jeong, J. (2022). Mean–Variance Portfolio Efficiency under Leverage Aversion and Trading Impact. Journal of Risk and Financial Management, 15(3). https://doi.org/10.3390/jrfm15030098

Gharehchopogh, F. S. (2023). Quantum-Inspired Metaheuristic Algorithms: Comprehensive Survey and Classification. Artificial Intelligence Review, 56(6), 5479–5543. https://doi.org/10.1007/s10462-022-10280-8

Gong, X., Zhang, W., Xu, W., & Li, Z. (2022). Uncertainty Index and Stock Volatility Prediction: Evidence from International Markets. Financial Innovation, 8(1). https://doi.org/10.1186/s40854-022-00361-6

Gunjan, A., & Bhattacharyya, S. (2023). A Brief Review of Portfolio Optimization Techniques. Artificial Intelligence Review, 56(5), 3847–3886. https://doi.org/10.1007/s10462-022-10273-7

Habib, A. M., & Kayani, U. N. (2024). Price Reaction of Global Economic Indicators: Evidence from the COVID-19 Pandemic and the Russia–Ukraine Conflict. SN Business and Economics, 4(1). https://doi.org/10.1007/s43546-023-00619-w

Jang, J., & Seong, N. Y. (2023). Deep Reinforcement Learning for Stock Portfolio Optimization by Connecting with Modern Portfolio Theory. Expert Systems with Applications, 218. https://doi.org/10.1016/j.eswa.2023.119556

Kamila Shaliha, S., Zahwarani, A., Faizah Nijma Ilma, A., Rahajuni Fakultas Ekonomi dan Bisnis, D., & Jenderal Soedirman, U. (2025). Dinamika Ketidakpastian Pasar Keuangan Global terhadap Stabilitas Harga Emas Sebagai Safe Haven Asset. Journal of Economics Research and Policy Studies, 5(3), 845–857. https://doi.org/10.53088/JERPS.V5I3.2449

Kim, J., Kumar, A., Mallick, S., & Park, D. (2024). Financial Uncertainty and Interest Rate Movements: is Asian Bond Market Volatility Different? Annals of Operations Research, 334(1–3), 731–759. https://doi.org/10.1007/s10479-021-04314-7

Loke, Z. X., Goh, S. L., Kendall, G., Abdullah, S., & Sabar, N. R. (2023). Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies. IEEE Access, 11, 33100–33120. https://doi.org/10.1109/ACCESS.2023.3263198

Priyadarshini, I. (2024). Swarm-Intelligence-Based Quantum-Inspired Optimization Techniques for Enhancing Algorithmic Efficiency and Empirical Assessment. Quantum Machine Intelligence, 6(2). https://doi.org/10.1007/s42484-024-00201-z

Procacci, P. F., & Aste, T. (2022). Portfolio Optimization with Sparse Multivariate Modeling. Journal of Asset Management, 23(6), 445–465. https://doi.org/10.1057/s41260-022-00280-2

Rebentrost, P., & Lloyd, S. (2024). Quantum Computational Finance: Quantum Algorithm for Portfolio Optimization. KI - Kunstliche Intelligenz, 38(4), 327–338. https://doi.org/10.1007/s13218-024-00870-9

Sharma, S., & Kumar, V. (2022). A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future. Archives of Computational Methods in Engineering, 29(7), 5605–5633. https://doi.org/10.1007/s11831-022-09778-9

Singh, G., & Chaturvedi, A. K. (2024). Hybrid Modified Particle Swarm Optimization with Genetic Algorithm (GA) Based Workflow Scheduling in Cloud-Fog Environment for Multi-Objective Optimization. Cluster Computing, 27(2), 1947–1964. https://doi.org/10.1007/s10586-023-04071-1

Smales, L. A. (2022). Spreading the fear: The central role of CBOE VIX in global stock market uncertainty. Global Finance Journal, 51. https://doi.org/10.1016/j.gfj.2021.100679

Tang, W., Pan, Y., Xu, H., & Ge, Y. (2023). Quantum Genetic Algorithm with Fuzzy Control Based on Clustering Analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14267 LNAI, 323–334. https://doi.org/10.1007/978-981-99-6483-3_28

Wang, Z., Zhang, X., Zhang, Z. K., & Sheng, D. (2022). Credit Portfolio Optimization: A Multi-Objective Genetic Algorithm Approach. Borsa Istanbul Review, 22(1), 69–76. https://doi.org/10.1016/j.bir.2021.01.004

Yu, M., Umair, M., Oskenbayev, Y., & Karabayeva, Z. (2023). Exploring the Nexus Between Monetary Uncertainty and Volatility in Global Crude Oil: A Contemporary Approach of Regime-Switching. Resources Policy, 85. https://doi.org/10.1016/j.resourpol.2023.103886

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Published

2024-04-17