Bankruptcy prediction for Japanese firms: using Multiple Criteria Linear Programming data mining approach

Autor:	Wikil Kwak, Yong Shi, Susan W. Eldridge, Gang Kou
Rok vydání:	2006
Předmět:	Information Systems and Management Bankruptcy Computer science Bankruptcy prediction Predictor variables Prediction rate Data mining Statistics Probability and Uncertainty Multiple criteria linear programming computer.software_genre computer Management Information Systems Test (assessment)
Zdroj:	International Journal of Business Intelligence and Data Mining. 1:401
ISSN:	1743-8195 1743-8187
DOI:	10.1504/ijbidm.2006.010782
Popis:	Data mining applications have been getting more attention in general business areas, but there is a need to use more of these applications in accounting areas where accounting deals with large amounts of both financial and non-financial data. The purpose of this research is to test the effectiveness of a Multiple Criteria Linear Programming (MCLP) approach to data mining for bankruptcy prediction using Japanese bankruptcy data. Our empirical results show that Ohlson's (1980) predictor variables perform better than Altman's (1968) predictor variables using 1990s Japanese financial data. Our Type I (misclassification of bankrupt as non-bankrupt firms) prediction rate using the MCLP approach, Ohlson's (1980) variables and 1990s Japanese financial data is much higher than that reported by Kwak et al. (2005) using the MCLP approach, Ohlson's (1980) variables and 1990s US data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9e40397df76c58fd5d2c68f8318b6c59 https://doi.org/10.1504/ijbidm.2006.010782 Zobrazit plný text záznamu