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An Application of Support Vector Machine to Companies’ Financial Distress Prediction

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Modeling Decisions for Artificial Intelligence (MDAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3885))

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Abstract

Because of the importance of companies’ financial distress prediction, this paper applies support vector machine (SVM) to the early-warning of financial distress. Taking listed companies’ three-year data before special treatment (ST) as sample data, adopting cross-validation and grid-search technique to find SVM model’s good parameters, an empirical study is carried out. By comparing the experiment result of SVM with Fisher discriminant analysis, Logistic regression and back propagation neural networks (BP-NNs), it is concluded that financial distress early-warning model based on SVM obtains a better balance among fitting ability, generalization ability and model stability than the other models.

Sponsored by National Natural Science Foundation of China (No. 70573030).

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© 2006 Springer-Verlag Berlin Heidelberg

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Hui, XF., Sun, J. (2006). An Application of Support Vector Machine to Companies’ Financial Distress Prediction. In: Torra, V., Narukawa, Y., Valls, A., Domingo-Ferrer, J. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2006. Lecture Notes in Computer Science(), vol 3885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681960_27

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  • DOI: https://doi.org/10.1007/11681960_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32780-6

  • Online ISBN: 978-3-540-32781-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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