Gaussian case-based reasoning for business failure prediction with empirical data in China |
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| Authors: | Hui Li Jie Sun |
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| Affiliation: | 1. School of Business, Pusan National University, 63 Beon-gil 2, Busandaehag-ro, Geumjeong-gu, Busan 609-735, Republic of Korea;2. Department of Computer and Information Engineering, Dongseo University, 47, Churye-Ro, Sasang-Gu, Busan 617-716, Republic of Korea;3. Division of Business, Dongseo University, 47, Churye-Ro, Sasang-Gu, Busan 617-716, Republic of Korea;1. School of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China;2. School of Materials Science and Engineering, Shanghai University, Shanghai 200072, China;1. Department of Business Administration, National Taipei College of Business, No. 321, Sec. 1, Ji-Nan Rd., Zhongzheng District, Taipei 10051, Taiwan, ROC;2. Department of Accounting, Chinese Culture University, No. 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei City 11114, Taiwan, ROC;1. School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China;2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui, PR China;3. Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong |
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| Abstract: | Case-based reasoning (CBR) is an easily understandable concept. Business failure prediction (BFP) is a valuable tool that can assist businesses take appropriate action when faced with the knowledge of the possibility of business failure. This study aims to improve the performance of a CBR system for BFP in terms of accuracy and reliability by constructing a new similarity measure, an area seldom researched in the domain of BFP. In order to fulfill this objective, we present a hybrid Gaussian CBR (GCBR) system and use it in BFP with empirical data in China. The heart of GCBR is similarity measure using Gaussian indicators. Feature distances between a pair of cases on each feature are transferred to Gaussian indicators by Gaussian transformations. A combiner is used to generate case similarity on the basis of the Gaussian indicators. Consensus of nearest neighbors is used to generate forecasting on the basis of case similarity. The new hybrid CBR system was empirically tested with data collected from the Shanghai Stock Exchange and Shenzhen Stock Exchange in China. We statistically validated our results by comparing them with multiple discriminant analysis, logistic regression, and two classical CBR systems. The results indicated that GCBR produces superior performance in short-term BFP of Chinese listed companies in terms of both predictive accuracy and coefficient of variation. |
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