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Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions

Author

Listed:
  • Xu, Zhengmeng
  • Wang, Yujie
  • Feng, Xiaotong
  • Wang, Yilin
  • Li, Yanli
  • Lin, Hai

Abstract

This paper presents the Quantum Gramian Angular Field (QGAF) method, which integrates quantum computing with deep learning to enhance forecasting in time series analysis. This method effectively converts stock return time series data into a format compatible with Convolutional Neural Network (CNN) training. Our empirical tests, conducted on stock market data from China, Hong Kong, and the United States, demonstrate that QGAF significantly outperforms the traditional GAF approach in accuracy. These findings underscore the potential of combining quantum computing and deep learning in financial time series forecasting.

Suggested Citation

  • Xu, Zhengmeng & Wang, Yujie & Feng, Xiaotong & Wang, Yilin & Li, Yanli & Lin, Hai, 2024. "Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions," Finance Research Letters, Elsevier, vol. 67(PA).
    • Handle: RePEc:eee:finlet:v:67:y:2024:i:pa:s1544612324008705
    DOI: 10.1016/j.frl.2024.105840
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    References listed on IDEAS

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    1. Zhi Su & Xuanye Cai & You Wu, 2023. "Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 30(15), pages 2052-2059, September.
    2. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    3. Haibin Xie & Yuying Sun & Pengying Fan, 2023. "Return direction forecasting: a conditional autoregressive shape model with beta density," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
    4. Luo, Qin & Ma, Feng & Wang, Jiqian & Wu, You, 2024. "Changing determinant driver and oil volatility forecasting: A comprehensive analysis," Energy Economics, Elsevier, vol. 129(C).
    5. Charles H. Bennett & David P. DiVincenzo, 2000. "Quantum information and computation," Nature, Nature, vol. 404(6775), pages 247-255, March.
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    Cited by:

    1. Siyang Mei & Yuxi Zhang & Xin Liu & Feng Li, 2025. "Presenting an innovative methodology to effectively handle investment risk in financial markets," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(10), pages 3390-3408, October.

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