Data mining in predicting firms failure: a comparative study using artificial neural networks and classification and regression tree / Norashikin Nasaruddin ...[et al.]
Financial Institutions and investors alike are very much interested in the accuracy of predicting the potential failures of firms. These financial institutions believe accurate prediction will lead to a low default rate in servicing their financial loans. The aim of this study is to find a better mo...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/53991/1/53991.pdf https://ir.uitm.edu.my/id/eprint/53991/ |
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| Summary: | Financial Institutions and investors alike are very much interested in the accuracy of predicting the potential failures of firms. These financial institutions believe accurate prediction will lead to a low default rate in servicing their financial loans. The aim of this study is to find a better model to classify firms that is more likely to fail. Bad prediction model will lead to a high default rate. Using financial and non-financial information, this paper illustrates the construction and comparison of two models – artificial neural networks (NN) and classification and regression tree (CART) models to classify the failed from the non-failed firms. This study found that based on the training sample’s result (NN = 94.03% & CART = 94.69%) the overall accuracy result of CART is higher than the NN model. Similar result can be drawn for the validation sample with CART leading at 92.93% overall accuracy rate compared to NN’s 91.92%. |
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