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Study on early warning of E-commerce enterprise financial risk based on deep learning algorithm

Author

Listed:
  • Yali Cao

    (Beijing Technology and Business University)

  • Yue Shao

    (University of International Business and Economics)

  • Hongxia Zhang

    (Jiyang College, Zhejiang Agriculture and Forestry University)

Abstract

With the development trend of economic progress, the capital business of e-commerce enterprises has become complicated. The financial risk of listed companies is a problem that needs to be paid attention to. The financial risk of e-commerce companies is a complex and gradual process, and its unique reasons may be many. E-commerce companies are facing financial risks or difficulties, and bankruptcy and liquidation are also increasing. Financial risk has seriously affected e-commerce companies and society. As a result, the early warning methods of financial risks have been constantly improved. With the arrival of the new economic era in the era of knowledge economy, the early warning of financial risks in e-commerce companies has become a hot issue in the financial management of e-commerce companies. Based on the deep learning algorithm, this paper studies from the perspective of establishing the financial early warning model based on deep learning and constructing the financial risk early warning mechanism of e-commerce companies, and analyzes and forecasts the financial risks of listed companies. Through the construction of financial security early warning system, crisis signals can be diagnosed as soon as possible, and crisis signals can be prevented and solved timely and effectively.

Suggested Citation

  • Yali Cao & Yue Shao & Hongxia Zhang, 2022. "Study on early warning of E-commerce enterprise financial risk based on deep learning algorithm," Electronic Commerce Research, Springer, vol. 22(1), pages 21-36, March.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:1:d:10.1007_s10660-020-09454-9
    DOI: 10.1007/s10660-020-09454-9
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    References listed on IDEAS

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    1. Gianpaolo Iazzolino & Maria Elena Bruni & Patrizia Beraldi, 2013. "Using DEA and financial ratings for credit risk evaluation: an empirical analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 20(14), pages 1310-1317, September.
    2. Oet, Mikhail V. & Bianco, Timothy & Gramlich, Dieter & Ong, Stephen J., 2013. "SAFE: An early warning system for systemic banking risk," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4510-4533.
    3. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
    4. Pierluigi Bologna, 2015. "Structural Funding and Bank Failures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 47(1), pages 81-113, February.
    5. Paolo Giudici & Laura Parisi, 2017. "Sovereign risk in the Euro area: a multivariate stochastic process approach," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1995-2008, December.
    6. Yong Ma & Yulu Chen, 2014. "Financial Imbalance Index as a New Early Warning Indicator: Methods and Applications in the Chinese Economy," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 22(6), pages 64-86, November.
    7. Jon Frost & Ayako Saiki, 2014. "Early Warning for Currency Crises: What Is the Role of Financial Openness?," Review of International Economics, Wiley Blackwell, vol. 22(4), pages 722-743, September.
    8. Jobst, Andreas A., 2013. "Multivariate dependence of implied volatilities from equity options as measure of systemic risk," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 112-129.
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    Cited by:

    1. Guansan Du & Frank Elston, 2022. "RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models," Operations Management Research, Springer, vol. 15(3), pages 925-940, December.
    2. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    3. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    4. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    5. Wu, Haiyan & Qiao, Yuxuan & Luo, Chuxiang, 2024. "Cross-border e-commerce, trade digitisation and enterprise export resilience," Finance Research Letters, Elsevier, vol. 65(C).
    6. Ronghua Xu & Yiran Liu & Meng Liu & Chengang Ye, 2023. "Sustainability of Shipping Logistics: A Warning Model," Sustainability, MDPI, vol. 15(14), pages 1-15, July.

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