Abstract
Financial distress prediction is an important research topic in both academic and practical world. This paper proposed a financial distress prediction model based on similarity weighted voting case-based reasoning (CBR), which consists of case representation, similar case retrieval and combination of target class. An empirical study was designed and carried out by using Chinese listed companies’ three-year data before special treatment (ST) and adopting leave-one-out and grid-search technique to find the model’s good parameters. The experiment result of this model was compared with multi discriminant analysis (MDA), Logit, neural networks (NNs) and support vector machine (SVM), and it was concluded that similarity weighted voting CBR model has very good predictive ability for enterprises which will probably run into financial distress in less than two years, and it is more suitable for short-term financial distress prediction.
This paper is supported by the National Natural Science Foundation of China (No. 70573030) and the National Center of Technology, Policy, and Management, Harbin Institute of Technology.
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Sun, J., Hui, XF. (2006). Financial Distress Prediction Based on Similarity Weighted Voting CBR. In: Li, X., ZaĂŻane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_103
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DOI: https://doi.org/10.1007/11811305_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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