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RETRACTED ARTICLE: Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model

Author

Listed:
  • Chenlu Dang

    (Xi’an International Studies University)

  • Fan Wang

    (Kyung Hee University)

  • Zimo Yang

    (Graduate School of Pan-Pacific International Studies, Kyung Hee University)

  • Hongxia Zhang

    (Jiyang College, Zhejiang Agriculture and Forestry University)

  • Yufeng Qian

    (Hubei University of Technology)

Abstract

The present work applies deep learning and blockchain technology to evaluate and control the risk of the supply chain finance market, to cope with the diversifications of the financial market development mode. Firstly, based on the relevant monetary inward theory, the potential risks are analyzed in the supply chain finance market. Besides, under the background of financial technology, the risk of the supply chain finance market is predicted and managed by intelligent technology. Secondly, the financing model of supply chain finance is analyzed to discuss the possible credit risk of supply chain finance. Meanwhile, the credit evaluation model of supply chain finance based on deep learning technology is constructed to predict the potential credit risk. Thirdly, blockchain technology is adopted to control and optimize the credit evaluation model to establish a credit system for supply chain enterprises with high credit and reliability and reduce potential supply chain financial risks. Finally, the designed model is simulated and tested. The experimental results show that the credit evaluation model of supply chain finance has a fitting effect of 0.989 on the sample data, indicating that it can effectively analyze the data. Result analysis shows that the designed model can effectively predict the potential credit risk of the enterprise. Moreover, a stable and reliable credit relationship network is established for supply chain finance by blockchain technology, which enhances the reliability of logistics transactions, and reduces potential risks faced by supply chain finance. The model provides effective technical means for studying the credit risk of supply chain finance.

Suggested Citation

  • Chenlu Dang & Fan Wang & Zimo Yang & Hongxia Zhang & Yufeng Qian, 2022. "RETRACTED ARTICLE: Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model," Operations Management Research, Springer, vol. 15(3), pages 662-675, December.
    • Handle: RePEc:spr:opmare:v:15:y:2022:i:3:d:10.1007_s12063-021-00252-6
    DOI: 10.1007/s12063-021-00252-6
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    1. Prosper Lamothe-Fernández & David Alaminos & Prosper Lamothe-López & Manuel A. Fernández-Gámez, 2020. "Deep Learning Methods for Modeling Bitcoin Price," Mathematics, MDPI, vol. 8(8), pages 1-13, July.
    2. Jiafu An & Raghavendra Rau, 2021. "Finance, technology and disruption," The European Journal of Finance, Taylor & Francis Journals, vol. 27(4-5), pages 334-345, March.
    3. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    4. Takuya Shintate & Lukáš Pichl, 2019. "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," JRFM, MDPI, vol. 12(1), pages 1-15, January.
    5. Huang, Jing & Yang, Wensheng & Tu, Yiliu, 2020. "Financing mode decision in a supply chain with financial constraint," International Journal of Production Economics, Elsevier, vol. 220(C).
    6. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
    7. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    8. Chowdhury, Reaz & Rahman, M. Arifur & Rahman, M. Sohel & Mahdy, M.R.C., 2020. "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    9. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    10. Han, Yonghui & Zhang, Fan & Huang, Liangxiong & Peng, Keming & Wang, Xianbin, 2021. "Does industrial upgrading promote eco-efficiency? ─A panel space estimation based on Chinese evidence," Energy Policy, Elsevier, vol. 154(C).
    11. Ghazanfar Ali Abbasi & Lee Yin Tiew & Jinquan Tang & Yen-Nee Goh & Ramayah Thurasamy, 2021. "The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-26, March.
    12. Gao, Huasheng & Hsu, Po-Hsuan & Li, Kai & Zhang, Jin, 2020. "The Real Effect of Smoking Bans: Evidence from Corporate Innovation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(2), pages 387-427, March.
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    Cited by:

    1. Olesya P. Kazachenok & Galina V. Stankevich & Natalia N. Chubaeva & Yuliya G. Tyurina, 2023. "Economic and legal approaches to the humanization of FinTech in the economy of artificial intelligence through the integration of blockchain into ESG Finance," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    2. Anandika Sharma & Anupam Sharma & Tarunpreet Bhatia & Rohit Kumar Singh, 2023. "Blockchain enabled food supply chain management: A systematic literature review and bibliometric analysis," Operations Management Research, Springer, vol. 16(3), pages 1594-1618, September.

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