IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v22y2022i1d10.1007_s10660-020-09454-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09454-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-020-09454-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Varotto, Simone & Zhao, Lei, 2018. "Systemic risk and bank size," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 45-70.
    2. Mpho Bosupeng, 2018. "Leading Indicators and Financial Crisis: A Multi-Sectoral Approach Using Signal Extraction," Journal of Empirical Studies, Conscientia Beam, vol. 5(1), pages 20-44.
    3. Jobst, Andreas A., 2014. "Measuring systemic risk-adjusted liquidity (SRL)—A model approach," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 270-287.
    4. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    5. Schupp, Fabian & Silbermann, Leonid, 2017. "The Role of Structural Funding for Stability in the German Banking Sector," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168166, Verein für Socialpolitik / German Economic Association.
    6. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    7. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    8. Domenico Di Gangi & Fabrizio Lillo & Davide Pirino, 2015. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Papers 1509.00607, arXiv.org, revised Jul 2018.
    9. Corsi, Fulvio & Lillo, Fabrizio & Pirino, Davide & Trapin, Luca, 2018. "Measuring the propagation of financial distress with Granger-causality tail risk networks," Journal of Financial Stability, Elsevier, vol. 38(C), pages 18-36.
    10. Huotari, Jarkko, 2015. "Measuring financial stress – A country specific stress index for Finland," Bank of Finland Research Discussion Papers 7/2015, Bank of Finland.
    11. Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
    12. Pierluigi Bologna, 2017. "Banks’ maturity transformation: risk, reward, and policy," Temi di discussione (Economic working papers) 1159, Bank of Italy, Economic Research and International Relations Area.
    13. Di Gangi, Domenico & Lillo, Fabrizio & Pirino, Davide, 2018. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 117-141.
    14. Meriläinen, Jari-Mikko & Junttila, Juha, 2020. "The relationship between credit ratings and asset liquidity: Evidence from Western European banks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    15. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.
    16. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    17. Balaga Mohana Rao & Puja Padhi, 2020. "Common Determinants of the Likelihood of Currency Crises in BRICS," Global Business Review, International Management Institute, vol. 21(3), pages 698-712, June.
    18. International Monetary Fund, 2011. "United Kingdom: Stress Testing the Banking Sector Technical Note," IMF Staff Country Reports 2011/227, International Monetary Fund.
    19. Gianpaolo Iazzolino & Rossella Gabriele, 2016. "Energy Efficiency and Sustainable Development: An Analysis of Financial Reliability in Energy Service Companies Industry," International Journal of Energy Economics and Policy, Econjournals, vol. 6(2), pages 222-233.
    20. Tamara Ayœs, Armando Lenin & Villegas, Gladis Cecilia & Leones Castro, María Cristina & Salazar Bocanegra, Juan Antonio, 2018. "Modelaci—n del riesgo de insolvencia en empresas del sector salud empleando modelos logit || Modeling of Insolvency Risk in Health Sector Companies Using Logit Models," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 128-145, Diciembre.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:22:y:2022:i:1:d:10.1007_s10660-020-09454-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.