Abstract
This work aims to solve the problem that the traditional deep learning model has low prediction accuracy and is not suitable for enterprise default risk prediction. Firstly, it expounds the definition and influencing factors of corporate bond default, including macroeconomic factors, industry factors, policy factors, and financial factors. Secondly, the fault prediction model for manufacturing corporate bonds is realized based on Convolutional Neural Network. Finally, 20 manufacturing enterprises in the current financial market are selected. By establishing the evaluation index system and designing simulation experiments, their financial data is tested and analyzed to verify the model’s effectiveness. The experimental results reveal that the main differences between the experimental group (defaulting company) and the control group (non-defaulting company) lie in the internal financial indicators and the self-characteristics of the company. The overall capital flow rate of the defaulting company is lower than that of the non- defaulting company, and the average total operating interest rate and return on net assets are 15.16% and 11.6%, respectively, lower than 26.3% and 18.9% in the control group. Additionally, the prediction accuracy of the Convolutional Neural Network model for defaulting companies is 80%; the average prediction error is 1.87, which is 65.4% lower than that of Random Forest model. To sum up, the Convolutional Neural Network model shows better performance in corporate default prediction. This work effectively reduces the default risk of China's bond enterprises and provides important technical support to ensure the healthy development of the bond market.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
13 February 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12063-024-00454-8
References
Altman EI, Harmon M (2022) Practical Applications of Risky Corporate Bonds in 2021: A Bubble, or Rational Underwriting in a Low-Rate Environment? Pract Appl 9(3):1–6. https://doi.org/10.3905/pa.9.3.465
Andriasyan V, Yakimovich A, Petkidis A, Georgi F, Greber UF (2021) Microscopy deep learning predicts virus infections and reveals mechanics of lytic-infected cells. iScience 24(6):21. https://doi.org/10.1016/j.isci.2021.102543
Chen K, Guo W, Kang Y, Wang J (2022) Does the deleveraging policy increase the risk of corporate debt default: evidence from China. Emerg Mark Financ Trade 58(3):601–613. https://doi.org/10.1080/1540496X.2020.1809376
Girdler B, Moon H, Bae MR, Ryu SS, Bae J, Yu MS (2021) Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images. Int Forum Allergy Rhinol 31(53):77. https://doi.org/10.1002/alr.22854
Gupta T, Sachdeva SN (2021) Prediction of compressive and flexural strengths of jarosite mixed cement concrete pavements using artificial neural networks. Road Mater Pavement Des 22(7):58. https://doi.org/10.1080/14680629.2019.1702583
Ingle V, Deshmukh S (2021) Ensemble deep learning framework for stock market data prediction (EDLF-DP). Glob Transit Proc 2(1):47–66. https://doi.org/10.1016/j.gltp.2021.01.008
Jiang J, Meng X, Liu Y, Wang H (2022) An Enhanced TSA-MLP Model for Identifying Credit Default Problems. SAGE Open 12(2):21582440221094584. https://doi.org/10.1177/21582440221094586
Khanday NY, Sofi SA (2021) Deep insight: Convolutional neural network and its applications for COVID-19 prognosis. Biomed Signal Process Control 69(30):43. https://doi.org/10.1016/j.bspc.2021.102814
Lee SI, Yoo SJ (2021) Multimodal deep learning for finance: integrating and forecasting international stock markets. J Supercomput 76(10):8294–8312. https://doi.org/10.1007/s11227-019-03101-3
Mirzaei NE, Hilletofth P, Pal R (2021) Challenges to competitive manufacturing in high-cost environments: checklist and insights from Swedish manufacturing firms. Oper Manag Res 14:272–292. https://doi.org/10.1007/s12063-021-00193-0
Miura M, Maki S, Miura K, Takahashi H, Miyagi M, Inoue G (2021) Automated detection of cervical ossification of the posterior longitudinal ligament in plain lateral radiographs of the cervical spine using a convolutional neural network. Sci Rep 11(1):48. https://doi.org/10.7759/cureus.14041
Nawaz M, Masood M, Javed A, Iqbal J, Nazir T, Mehmood A (2021) Melanoma localization and classification through faster region-based convolutional neural network and SVM. Multimed Tools Appl 32(48):39. https://doi.org/10.1007/s11042-021-11120-7
Omodior O, Saeedpour PM, Rahman MK, Azad A, Clay K (2021) Using convolutional neural networks for tick image recognition - a preliminary exploration. Exp Appl Acarol 23(10):93. https://doi.org/10.1007/s10493-021-00639-x
Prahl A, Goh WP (2021) “Rogue machines” and crisis communication: When AI fails, how do companies publicly respond. Public Relat Rev 47(4):30. https://doi.org/10.1016/j.pubrev.2021.102077
Rangarajan AK, Ramachandran HK (2021) A preliminary analysis of AI-based smartphone application for diagnosis of COVID-19 using chest X-ray images. Expert Syst Appl 183(22):32. https://doi.org/10.1016/j.eswa.2021.115401
Ruchai AN, Kober VI, Dorofeev KA, Karnaukhov VN, Mozerov MG (2021) Classification of Breast Abnormalities Using a Deep Convolutional Neural Network and Transfer Learning. J Commun Technol Electron 66(6):32. https://doi.org/10.1134/S1064226921060206
Suneetha B, Jhansi RA, Padmaja M, Madhavi G, Prasuna K (2021) Brain tumor image classification using improved convolution neural networks. Appl Nanosci 32(57):33. https://doi.org/10.1007/s13204-021-01906-4
Takeda S, Mine Y, Yoshimi Y, Ito S, Murayama T (2021) Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network. J Dent Sci 16(3):30. https://doi.org/10.1016/j.jds.2020.10.012
Vinayahalingam S, Kempers S, Limon L, Deibel D, Tong X (2021) Classification of caries in third molars on panoramic radiographs using deep learning. Sci Rep 11(1):42. https://doi.org/10.1038/s41598-021-92121-2
Wang Y, Liu T, Zhang D, Xie Y (2021) Dual-convolutional neural network-based aerodynamic prediction and multi-objective optimization of a compact turbine rotor. Aerosp Sci Technol 116(40):65. https://doi.org/10.1016/j.ast.2021.106869
Yu X, Lu S, Guo L, Wang S, Zhang YD (2021) ResGNet-C: A graph convolutional neural network for detection of COVID-19. Neurocomputing 452(20):32. https://doi.org/10.1016/j.neucom.2020.07.144
Zamboni S, Kefato ZT, Girdzijauskas S, Norén C, Dal Col L (2022) Pedestrian trajectory prediction with convolutional neural networks. Pattern Recogn 121:108252. https://doi.org/10.1016/j.patcog.2021.108252
Zhang J, Mu X, Zhao P, Kang K, Ma C (2021c) Improving current interest with the item and review sequential patterns for the sequential recommendation. Eng Appl Artif Intell 104(36):90. https://doi.org/10.1016/j.engappai.2021.104348
Zhang Y, Lv X, Qiu J, Zhang B, Zhang L, Fang J (2021d) Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma. J Magn Reson Imaging 54(1):20. https://doi.org/10.1002/jmri.27538
Acknowledgements
The authors acknowledge the help from the university colleagues.
Funding
This research received no external funding.
Author information
Authors and Affiliations
Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Corresponding author
Ethics declarations
Ethics statement
All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Wenzhou Medical University.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12063-024-00454-8
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, C., Zhang, F., Chen, N. et al. RETRACTED ARTICLE: Application of artificial intelligence technology in financial data inspection and manufacturing bond default prediction in small and medium-sized enterprises (SMEs). Oper Manag Res 15, 941–952 (2022). https://doi.org/10.1007/s12063-022-00314-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12063-022-00314-3