Early warning analysis of financial risk of new energy enterprises based on neural network

DOI

10.2991/978-94-6463-276-7_23How to use a DOI?

Keywords

Financial Risk Early Warning; Neural Network; New Energy Enterprises; Financial Data; Prediction Model

Abstract

In the new energy sector, financial risk management is crucial. Nonlinear financial data can challenge traditional early warning models. Neural networks can better predict financial risk for new energy enterprises. New energy enterprises’ financial risk research is reviewed first. Then we discuss neural network theory and modeling. For training and testing, we use energy company data. We outperform classical logistic regression in terms of accuracy, recall rate, and F1 score. Since the model uses the same country and industry for training and testing, its universality must be confirmed. The model’s black-box nature must also be overcome. For new energy enterprises, the study provides relevant insights for future research.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lixing Zhu
AU  - Xue Yang
AU  - Xue Jiang
AU  - Yuqi Tian
AU  - Junjie Yan
PY  - 2023
DA  - 2023/10/27
TI  - Early warning analysis of financial risk of new energy enterprises based on neural network
BT  - Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)
PB  - Atlantis Press
SP  - 223
EP  - 229
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-276-7_23
DO  - 10.2991/978-94-6463-276-7_23
ID  - Zhu2023
ER  -