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Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach

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Abstract

The main purpose of this paper is to evaluate the data mining applications, such as classification, which have been used in previous bankruptcy prediction studies and credit rating studies. Our study proposes a multiple criteria linear programming (MCLP) method to predict bankruptcy using Korean bankruptcy data after the 1997 financial crisis. The results, of the MCLP approach in our Korean bankruptcy prediction study, show that our method performs as well as traditional multiple discriminant analysis or logit analysis using only financial data. In addition, our model’s overall prediction accuracy is comparable to those of decision tree or support vector machine approaches. However, our results are not generalizable because our data are from a special situation in Korea.

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Acknowledgments

The authors would like to thank an anonymous reviewer for the constructive comments. This work has been partially supported by grants from Nebraska EPSCoR Program, Nebraska Furniture Market Co., BHP Billiton Co, National Natural Science Foundation of China (#70901015, #70921061, #70621001), and Overseas Collaboration Group of Chinese Academy of Sciences.

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Authors and Affiliations

  1. Department of Accounting, College of Business Administration, University of Nebraska at Omaha, Omaha, NE, 68182, USA

    Wikil Kwak

  2. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, 68182, USA

    Yong Shi

  3. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, 100190, Beijing, China

    Yong Shi

  4. School of Management and Economics, University of Electronic Science and Technology of China, 610054, Chengdu, People’s Republic of China

    Gang Kou

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  1. Wikil Kwak
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  2. Yong Shi
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  3. Gang Kou
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Correspondence to Wikil Kwak.

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Kwak, W., Shi, Y. & Kou, G. Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach. Rev Quant Finan Acc 38, 441–453 (2012). https://doi.org/10.1007/s11156-011-0238-z

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