An Integral Predictive Model of Financial Distress

CCUR > College of Business > Department of Banking & Finance > periodical articles > Item 987654321/24172

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/24172

Title:	An Integral Predictive Model of Financial Distress
Authors:	Lee, M (Lee, Mushang) Wu, TC (Wu, Tsui-Chih)
Contributors:	財金系
Keywords:	financial-distress-prediction model stepwise regression data mining rough set decision tree neural network logistic regression
Date:	2012-11
Issue Date:	2013-02-19 13:23:09 (UTC+8)
Abstract:	Traditional statistic models for financial distress are subject to constraints which may lead to imprecise prediction. To contribute to the issue, we construct a two-staged integral model by applying a stepwise regression analysis and a data-mining approach. Specifically, we employ stepwise regression and rough set analysis in feature selection to sieve out variables, and perform decision tree, neural network, and logistic regression analysis to classify firms with financial distress. The findings show that the rates of accuracy for the combinations in descending order are stepwise regression-logistic, stepwise regression-neutral network, stepwise regression-decision tree, rough set theory-neutral network, rough set theory-decision tree, and rough set theory-logistic.
Relation:	JOURNAL OF TESTING AND EVALUATION 卷: 40 期: 6 頁數: 931-938
Appears in Collections:	[Department of Banking & Finance ] periodical articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	368	View/Open

View Licence