Research on Financial Early Warning of Big Data Enterprises Based on Logistic Regression and BP Neural Network
- 10.2991/dramclr-19.2019.16How to use a DOI?
- combination forecasting model, large data enterprises,financial early warning
Based on the difference between big data enterprises and traditional enterprises, this paper first constructs two single financial risk early warning models: logistic regression model and BP neural network model, then introduces default probability of logistic regression model output into BP neural network model, and establishes a non-linear combination based on BP neural network model. A new forecasting method is proposed, and an early warning model of financial crisis for large data enterprises is constructed and an empirical study is carried out. The results show that, compared with single model, the combined forecasting model has no significant improvement in the forecasting accuracy of financial early warning for large data enterprises, but the combined forecasting model is more stable. This provides a new idea for the financial risk early warning research of large data enterprises in China.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - HongMei Zhang AU - Jian He PY - 2019/11 DA - 2019/11 TI - Research on Financial Early Warning of Big Data Enterprises Based on Logistic Regression and BP Neural Network BT - Proceedings of the Fourth Symposium on Disaster Risk Analysis and Management in Chinese Littoral Regions (DRAMCLR 2019) PB - Atlantis Press SP - 158 EP - 163 SN - 1951-6851 UR - https://doi.org/10.2991/dramclr-19.2019.16 DO - 10.2991/dramclr-19.2019.16 ID - Zhang2019/11 ER -