A Study on Early-warning of Enterprise Financial Crisis Based on Mixed Multiple Classifier Prediction
Mingrong Deng, Anqi Chen
Available Online May 2016.
- https://doi.org/10.2991/icpel-16.2016.9How to use a DOI?
- Early-warning, Multiple Classifier, Mixed Prediction, Financial situation of multiple classifiers.
- To help enterprises to know how to spot signs of problem and crisis ahead of time, and get forewarning and intervene in advance so as to safeguard survival and development of the enterprise by reversing the passive and disadvantageous status, we creatively divided them into healthy financial companies, companies with potential financial crisis and the distressed ones, to judge the financial status of healthy companies and whether the healthy companies are on the verge of financial crisis accurately. Based on previous researches, this paper constructs a forewarning model based on combination of multiple classifiers to apply it to company crisis forewarning under situation of multiple classification of company financial status. Experiment results show that the combined model has good identification ability. On one hand, it integrates classification information of various basic classifiers and increases classification accuracy. On the other hand, this combined model takes other enterprises in potential crisis besides regular ST enterprises and non-ST enterprises into consideration, reveals the enterprise financial distress situation more clearly and broadens crisis forewarning scope, which has great significance for following studies.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Mingrong Deng AU - Anqi Chen PY - 2016/05 DA - 2016/05 TI - A Study on Early-warning of Enterprise Financial Crisis Based on Mixed Multiple Classifier Prediction BT - Proceedings of the 2016 International Conference on Politics, Economics and Law (ICPEL 2016) PB - Atlantis Press SP - 36 EP - 39 SN - 2352-5398 UR - https://doi.org/10.2991/icpel-16.2016.9 DO - https://doi.org/10.2991/icpel-16.2016.9 ID - Deng2016/05 ER -