Abstract
In the era of "Internet plus," the world economy is becoming more and more globalized and informationalized. China's enterprises are facing unprecedented opportunities for their operation and development. However, it is also facing the financial uncertainties brought about by the fluctuations of the general economic environment, and the company is facing increasing financial risks. The reason why most enterprises encounter a serious financial crisis or even close down in the later stage is that they do not pay full attention to the initial financial problems and do not take effective measures to deal with the crisis in time. Financial risk warning has become an important part of modern enterprise financial management. This paper mainly puts forward the optimized BP neural system as the financial early warning model and ensures its high prediction accuracy. In the research, the operation principle and related reasoning process of the model are described, its shortcomings are analyzed, and solutions are put forward. Through the financial risk analysis of listed companies from 2017 to 2020, we find that the correct rate of the prediction results of the financial distress of normal companies in the selected companies based on the optimized BPNN has reached more than 80%, which proves the effectiveness of the optimized BPNN.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Hong Y, Sun W, Bai Q et al (2016) SOM-BPNN-based financial early-warning for listed companies. J Comput Theor Nanosci 13(10):6860–6866
Yan X, Weihan W, Chang M (2021) Research on financial assets transaction prediction model based on LSTM neural network. Neural Comput Appl 33(1):257–270
Li Q, Wu J, Chen Y et al (2020) A new response approximation model of the quadrant detector using the optimized BPNN. IEEE Sens J 20(8):4345–4352
Zhou S, Chong-Yang Z et al (2019) Dual-optimized adaptive Kalman filtering algorithm based on BPNN and variance compensation for laser absorption spectroscopy. Opt Express 27(22):31874–31888
Zhang D, Li W, Wu X et al (2019) Application of simulated annealing genetic algorithm optimized back propagation(BP) neural network in fault diagnosis. Int J Modeling Simul Sci Comput 10(04):46–49
Wang Z, Zhang Y, Ren Z et al. Modeling of anisotropic magnetostriction under DC bias based on an optimized BPNN. IEEE Transactions on Magnetics, 2020, PP(99):1–1.
H Zhou, Sun G, Fu S et al. A big data mining approach of PSO based BPNN for financial risk management with IoT. IEEE Access, 2019, PP(99):1–1.
Wisesa O, Adriansyah A, Khalaf OI (2020) Prediction analysis sales for corporate services telecommunications company using gradient boost algorithm. 2nd international conference on broadband communications, wireless sensors and powering. BCWSP 2020, pp(2020):101–106.
Wong ZY, Chin WC, Tan SH (2016) Daily value-at-risk modeling and forecast evaluation: the realized volatility approach. J Finance Data Sci 2(3):171–187
Singh, P., and Agrawal, R. A customer centric best connected channel model for heterogeneous and iot networks, J Org End User Comput, 2018, pp(30:4): 32–50.
Financial Classification of Listed Companies in China Based on BPNN Method. J Financ Risk Manag, 2016, 05(3):171–177.
Portfolio BV, Forecasting R (2016) Portfolio risk forecasting. Soc Sci Electron Publish 16(1):35–68
Zheng Xu, Zhu G, Metawa N, Zhou Q (2022) Machine learning based customer meta-combination brand equity analysis for marketing behavior evaluation. Inf Process Manag 59(1):102800
Zhao P (2018) Quantitative analysis of portfolio based on optimized BPNN. Cognit Syst Res 52:709–714
Niu X (2018) IoT study on the total risk management and cluster-coordinated development based on synergy theory. Cognit Syst Res 52:809–815
Cai CX, Kim M, Shin Y et al (2019) FARVaR: functional autoregressive value-at-risk. J Financ Economet 17(2):284–337
Ma Y, Li L, Yin Z et al (2021) Research and application of network status prediction based on BPNN for intelligent production line. Procedia Comput Sci 183(20):189–196
Sapp T (2016) Efficient estimation of distributional tail shape and the extremal index with applications to risk management. J Math Finance 6(4):626–659
Partey ST, Dakorah AD, Zougmoré RB et al (2020) Gender and climate risk management: evidence of climate information use in Ghana. Clim Change 158(1):61–75
Liu Z (2021) Construction and verification of color fundus image retinal vessels segmentation algorithm under BPNN. J Supercomput 77(7):7171–7183
Lucarelli G, Borrotti M (2020) A deep Q-learning portfolio management framework for the cryptocurrency market. Neural Comput Applic 32:17229–17244
Lukason O, Vissak T (2019) Internationalization and failure risk patterns: evidence from young Estonian manufacturing exporters. Int J Commer Manag 29(1):25–43
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
These are no potential competing interests in our paper. And all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, X., Wang, J. & Yang, C. Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy. Neural Comput & Applic 35, 2045–2058 (2023). https://doi.org/10.1007/s00521-022-07377-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-022-07377-0