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Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network

Author

Listed:
  • Zixian Liu

    (Liaoning University
    Operations Office of Shenyang Branch of the People’s Bank of China)

  • Guansan Du

    (Liaoning University
    University of Southern Queensland)

  • Shuai Zhou

    (Liaoning University)

  • Haifeng Lu

    (Shenyang Branch of People’s Bank of China)

  • Han Ji

    (Shenyang Branch of People’s Bank of China)

Abstract

The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory of Internet financial risks is introduced and a theoretical framework for analyzing and forecasting internet financial risks is established. Second, the theory of the BPNN and the algorithms based on deep learning are introduced. Then, the model based on the BPNN and deep learning is implemented to improve the early warning of Internet financial risks, analyze the data image of China's Gross Domestic Product (GDP), currency (M2), non-performing loan records, and the Shanghai Composite Index from 2006 to 2020, and forecast the risks in 2021. Through the model based on deep learning and BPNN, it can be found that the trends of the growth rate of China's GDP take on the shapes of V and L, and the trend of M2 is opposite to that of GDP. In the whole year, there is a low at the beginning and the end of the year, and the monthly non-performing loans and the Shanghai Composite Index decrease. The forecast made by the model is that there will be many fluctuations in 2021. At present, China’s economy just enters the era of the new normal, which helps to build a more scientific and sensitive early warning system for financial risks. The model based on the BPNN and deep learning greatly improves the timeliness of forecasts and has a positive impact on the stability of China’s financial environment.

Suggested Citation

  • Zixian Liu & Guansan Du & Shuai Zhou & Haifeng Lu & Han Ji, 2022. "Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1481-1499, April.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-021-10229-z
    DOI: 10.1007/s10614-021-10229-z
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    References listed on IDEAS

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    1. Zichun Su & Jialin Jiang, 2020. "Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction," Future Internet, MDPI, vol. 12(2), pages 1-14, February.
    2. Xun Huang & Cheng-Zhao Zhang & Jia Yuan, 2020. "Predicting Extreme Financial Risks on Imbalanced Dataset: A Combined Kernel FCM and Kernel SMOTE Based SVM Classifier," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 187-216, June.
    3. Hui Wang & Runzhe Liu & Yang Zhao & Xiaohui Du & Zhihan Lv, 2021. "Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems," Complexity, Hindawi, vol. 2021, pages 1-10, April.
    4. Yu-Wei Lin & Yuqian Zhou & Faraz Faghri & Michael J Shaw & Roy H Campbell, 2019. "Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-22, July.
    5. Zhangyao Zhu & Na Liu & Wei Wang, 2021. "Early Warning of Financial Risk Based on K-Means Clustering Algorithm," Complexity, Hindawi, vol. 2021, pages 1-12, March.
    6. Qin, Yong & Li, Kun & Liang, Zhanhao & Lee, Brendan & Zhang, Fuyong & Gu, Yongcheng & Zhang, Lei & Wu, Fengzhi & Rodriguez, Dragan, 2019. "Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal," Applied Energy, Elsevier, vol. 236(C), pages 262-272.
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    Cited by:

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    3. Haitao Lu & Xiaofeng Hu, 2024. "Enhancing Financial Risk Prediction for Listed Companies: A Catboost-Based Ensemble Learning Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 9824-9840, June.
    4. Yan Guo & Dezhao Tang & Wei Tang & Senqi Yang & Qichao Tang & Yang Feng & Fang Zhang, 2022. "Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    5. Mu-Yen Chen & Arun Kumar Sangaiah & Ting-Hsuan Chen & Edwin David Lughofer & Erol Egrioglu, 2022. "Deep Learning for Financial Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1277-1281, April.

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