Research on Financial Stock Market Prediction Based on the Hidden Quantum Markov Model

Xingyao Song (), Wenyu Chen and Junyi Lu
Additional contact information
Xingyao Song: Business School, The University of Sydney, Sydney 2000, Australia
Wenyu Chen: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Junyi Lu: School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China

Mathematics, 2025, vol. 13, issue 15, 1-17

Abstract: Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts.

Keywords: hidden Markov model (HMM); quantum machine learning; HQMM; stock forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/15/2505/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/15/2505/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:15:p:2505-:d:1717005

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().