Financial Early-Warning Model Using L 1/2-Regularized Logistic Regression

被引：0
|
作者：
Xu, Zheng [1 ]
Liu, Zunxiong [2 ]
机构：
[1] East China Jiaotong Univ, Sch Elect & Elect Engn, Nanchang, Peoples R China
[2] East China Jiaotong Univ, Sch Informat Engn, Nanchang, Peoples R China
来源：
NEW INDUSTRIALIZATION AND URBANIZATION DEVELOPMENT ANNUAL CONFERENCE: THE INTERNATIONAL FORUM ON NEW INDUSTRIALIZATION DEVELOPMENT IN BIG-DATA ERA | 2015年
关键词：
financial early-warning model; L-1/2-regularized LR; L-1-regularized penalized; L-2-regularized penalized; sparse matrix;
D O I：
暂无
中图分类号：
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号：
1205 ; 120501 ;
摘要：
The financial early-warning indexes are variables and correlated, so sparse matrix of statistical learning is used to study financial early warning of listing Corporation. Binary Logistic Regression (LR) is a multivariate statistical method, to estimate the parameters of the multiple regression models by using the method of maximum likelihood. But LR has the potential over-fitting problem. Aiming at this problem, the financial early-warning model using L,-Regularized LR is put forward. Experiments are implemented on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets. The testing indicates that the fractional-Regularized LR is more suitable than general LR, 1,1-Regularized penalized LR and L2-Regularized penalized LR model to build financial early-warning model.
引用
收藏
页码：546 / 550
页数：5
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