Bankruptcy Prediction Studies Across Countries Using Multiple Criteria Linear Programming (MCLP) and Other Data Mining Approaches

In this study, we want to show the usefulness of Multiple Criteria Linear Programming (MCLP) data mining approach to predict bankruptcy across countries such as Japan, Korea, and China compared with those of US studies. Bankruptcy studies are interesting to investors, managers, regulators, and other external users, but traditionally we used logit or probit types of statistical methods to predict firm bankruptcy. Recently, data mining has proven to be a useful tool in accounting and finance for various prediction and classification studies. Many applications have been used in accounting and finance areas where accountants deal with large amounts of computerized operational and financial databases. Eighty-two percent of these studies are predictive studies based on recent survey (Amani, F. A. and A. M. Fadlalla, International Journal of Accounting Information Systems 24:32–58, 2017). Segal (Economics and Business Review 2:45–64, 2016) proposes more applications of data mining in fraud detection and prevention as advanced technologies in the current information environment. There are higher potential payoffs for data mining applications in these areas. The purpose of this study is to propose an MCLP data mining approach as comparable to other data mining methods for bankruptcy prediction. This approach has proven to be robust and powerful even for a large sample size using a huge financial database. The results of the MCLP approach in a bankruptcy prediction study are promising as this approach performs better than traditional multiple discriminant analysis or logit analysis using financial data. Similar approaches can be applied to other accounting areas such as auditor changes, internal control weakness prediction, hedging, forecasting stock performance, detection of tax evasion, and an audit-planning tool using financial variables (see Kwak, W., S. Eldridge, S. Yong, and K. Gang, Journal of Applied Business Research 25:105–118, 2009, Kwak, W., S. Eldridge, S. Yong, and K. Gang, Journal of Applied Business Research 27:73–84, 2011, and Sam, R., J. Osleeb, and K. Si, International Regional Science Review 39:77, 2016 for examples).