Research on Financial Early Warning of Listed Companies Based on Lasso-logistic Model
- 10.2991/febm-18.2018.60How to use a DOI?
- Financial warning; Lasso algorithm; Logistic model; Variable selection
The financial status is an important factor affecting the survival and development of enterprises. When we conduct financial early-warning model analysis, the selection of index variables and the estimation of model parameters directly affect the prediction accuracy of the early-warning model. Lasso is a variable selection method for shrinkage estimation. By constructing a penalty functions to realize variety selection and retaining the advantages of subset shrinkage; Lasso can be applied to time series, high-dimensional graphics discrimination and selection. In this paper, the Lasso method and the Logistic model are combined to construct an early-warning model reflecting the financial status within the enterprise. The experimental results show that the model can effectively select relatively important influencing factors and also has good predictive evokes.
- © 2018, 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 - Yutong Han AU - Qi Sun AU - Zhuoxi Yu PY - 2018/12 DA - 2018/12 TI - Research on Financial Early Warning of Listed Companies Based on Lasso-logistic Model BT - Proceedings of the Third International Conference on Economic and Business Management (FEBM 2018) PB - Atlantis Press SP - 262 EP - 266 SN - 2352-5428 UR - https://doi.org/10.2991/febm-18.2018.60 DO - 10.2991/febm-18.2018.60 ID - Han2018/12 ER -