Research on Financial Early Warning of Listed Companies Based on Lasso-logistic Model

DOI

10.2991/febm-18.2018.60How to use a DOI?

Keywords

Financial warning; Lasso algorithm; Logistic model; Variable selection

Abstract

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.

Copyright

© 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/).

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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  -