AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

被引：28

作者：

Zhou, Ligang ^{[1
]}

Lai, Kin Keung ^{[2
,3
]}

机构：

[1] Macau Univ Sci & Technol, Sch Business, Taipa, Macau, Peoples R China

[2] Univ Hong Kong, Dept Ind & Mfg Syst Engn, Pokfulam, Hong Kong, Peoples R China

[3] Shaanxi Normal Univ, Int Business Sch, Xian, Peoples R China

来源：

COMPUTATIONAL ECONOMICS | 2017年 / 50卷 / 01期

基金：

中国国家自然科学基金;

关键词：

AdaBoost algorithms; Bankruptcy prediction; Missing data; SUPPORT VECTOR MACHINES; GENETIC ALGORITHMS; FINANCIAL RATIOS; INSOLVENCY; FEATURES;

D O I：

10.1007/s10614-016-9581-4

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Very little existing research in corporate bankruptcy prediction discusses modeling where there are missing values. This paper investigates AdaBoost models for corporate bankruptcy prediction with missing data. Three AdaBoost models integrated with different imputation methods are tested on two data sets with very different sample sizes. The experimental results show that the AdaBoost algorithm combined with imputation methods has strong predictive accuracy in both data sets and it is a useful alternative for bankruptcy prediction with missing data.

引用

页码：69 / 94

页数：26

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