Financial ratio selection for business crisis prediction

被引：49

作者：

Lin, Fengyi ^{[3
]}

Liang, Deron ^{[1
,2
]}

Chen, Enchia ^{[4
]}

机构：

[1] Natl Cent Univ, Software Res Ctr, Jongli City 320, Taoyuan County, Taiwan

[2] Natl Cent Univ, Dept Comp Sci & Informat Engn, Jongli City 320, Taoyuan County, Taiwan

[3] Natl Taipei Univ Technol, Dept Business Management, Taipei, Taiwan

[4] Natl Taiwan Ocean Univ, Dept Comp Sci, Taipei, Taiwan

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 12期

关键词：

Financial predictors (variables); Feature selection; Financial prediction; SVM; SUPPORT VECTOR MACHINES; DISCRIMINANT-ANALYSIS; NEURAL-NETWORKS; INTEGRATION; ALGORITHMS;

D O I：

10.1016/j.eswa.2011.05.035

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent research has used financial ratios to establish the diagnosis models for business crises. This research explores a broader coverage of financial features, namely the recommended financial ratios from TEJ (Taiwan Economic Journal) database in addition to those financial ratios studied in prior literature. The aim of this research is to discover potentially useful but previously unaware financial features for better prediction accuracy. In this study, we had applied data mining techniques to identify five useful financial ratios, which two of them, tax rates and continuous four quarterly EPS are previously unaware to the research community. Our empirical experiment indicates that our proposed feature set outperforms those models proposed by prior scholars in terms of the prediction accuracy. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：15094 / 15102

页数：9

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