Research on Financial Early Warning of Big Data Enterprises Based on Logistic Regression and BP Neural Network

DOI

10.2991/dramclr-19.2019.16How to use a DOI?

Keywords

combination forecasting model, large data enterprises,financial early warning

Abstract

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.

Copyright

© 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/).

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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  -