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

Authors: Wikil Kwak, Yong Shi, Susan W. Eldridge, Gang Kou

Addresses: Department of Accounting, College of Business Administration, University of Nebraska at Omaha, Omaha, NE 68182, USA. ' The Chinese Academy of Science Research Center on Data Technology and Knowledge Economy, No. 80 Zhongguancun East Road, Haidian District, Beijing 100080, China; College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA. ' Department of Accounting, College of Business Administration, University of Nebraska at Omaha, Omaha, NE 68182, USA. ' Computer Science Department, University of Nebraska at Omaha, Omaha, NE 68182-0500, USA

Abstract: 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.

Keywords: Japan; bankruptcy prediction; data mining; multiple criteria linear programming; MCLP.

DOI: 10.1504/IJBIDM.2006.010782

International Journal of Business Intelligence and Data Mining, 2006 Vol.1 No.4, pp.401 - 416

Available online: 30 Aug 2006 *

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