Predicting Japanese corporate bankruptcy in terms of financial data using neural network
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Introduction To Business Data Mining
Introduction To Business Data Mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Data mining from 1994 to 2004: an application-orientated review
International Journal of Business Intelligence and Data Mining
A Multi-criteria Convex Quadratic Programming model for credit data analysis
Decision Support Systems
Multiple Criteria Mathematical Programming and Data Mining
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Does Altman's z-score predict the economic viability of health maintenance organisations?
International Journal of Electronic Finance
Knowledge-Rich Data Mining in Financial Risk Detection
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
A family of optimization based data mining methods
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
When to choose an ensemble classifier model for data mining
International Journal of Business Intelligence and Data Mining
An empirical study of classification algorithm evaluation for financial risk prediction
Applied Soft Computing
Regularized multiple-criteria linear programming via second order cone programming formulations
DM-IKM '12 Proceedings of the Data Mining and Intelligent Knowledge Management Workshop
Hi-index | 0.00 |
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.