A comparison of neural network, statistical methods, and variable choice for life insurers' financial distress prediction

被引:47
作者
Brockett, Patrick L. [1 ]
Golden, Linda L.
Jang, Jaeho
Yang, Chuanhou
机构
[1] Univ Texas, McCombs Sch Business, Austin, TX 78712 USA
[2] Gannon Univ, Dahlkemper Sch Business, Erie, PA USA
关键词
D O I
10.1111/j.1539-6975.2006.00181.x
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back-propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty-two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back-propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty-two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons.
引用
收藏
页码:397 / 419
页数:23
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