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The Equity Fund Risk Predictions Via Quantum-Classical Hybrid Neural Networks

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Proceedings of the 13th International Conference on Computer Engineering and Networks (CENet 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1126))

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Abstract

Among the recent studies on the use of machine learning methods to study fund stocks, there are few studies on the use of quantum-classical hybrid models. In this paper, we investigate the application of a quantum-classical hybrid neural network algorithm in equity fund risk prediction. Specifically, we evaluate the performance of quantum-classical hybrid neural networks for stock fund risk prediction. In our experiments, we collected historical data from several stock fund portfolios. And then, we use the collected data to do the appropriate processing and encode the feature data onto quantum states. Subsequently, we utilized the proposed quantum-classical hybrid neural network to predict the risk indices of the funds. We measure the performance and accuracy of the model by calculating and counting the mean square error of the prediction results against the actual results of the test set, as predicted by the above algorithm. The experimental results show that the quantum-classical hybrid neural network proposed in this paper can achieve better risk prediction for stock funds, that is, the model can capture the nonlinearity between the input features of stock data and risk indices.

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Author information

Authors and Affiliations

  1. School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Peng Wang, Qinsheng Zhu & Hao Wu

  2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Xiaoyu Li

  3. Kash Institute of Electronics and Information Industry, Kash, 844000, China

    Shunkun Yang

  4. Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, USA

    Shan Yang

Authors
  1. Peng Wang
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  2. Qinsheng Zhu
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  3. Hao Wu
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  4. Xiaoyu Li
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  5. Shunkun Yang
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  6. Shan Yang
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Corresponding author

Correspondence to Qinsheng Zhu .

Editor information

Editors and Affiliations

  1. Wuxi University, Wuxi, Jiangsu, China

    Yonghong Zhang

  2. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China

    Lianyong Qi

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Qi Liu

  4. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Guangqiang Yin

  5. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Xiaodong Liu

Ethics declarations

This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS-202210); Chengdu Innovation and Technology Project, No. 2021-YF09-00114-GX; The Research on key technologies of complex image processing based on quantum-generated adversarial networks which is the key research of Sichuan Province in 2022, the project number is 2022YFG0315.

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Wang, P., Zhu, Q., Wu, H., Li, X., Yang, S., Yang, S. (2024). The Equity Fund Risk Predictions Via Quantum-Classical Hybrid Neural Networks. In: Zhang, Y., Qi, L., Liu, Q., Yin, G., Liu, X. (eds) Proceedings of the 13th International Conference on Computer Engineering and Networks. CENet 2023. Lecture Notes in Electrical Engineering, vol 1126. Springer, Singapore. https://doi.org/10.1007/978-981-99-9243-0_32

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  • DOI: https://doi.org/10.1007/978-981-99-9243-0_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9242-3

  • Online ISBN: 978-981-99-9243-0

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