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Data depth based support vector machines for predicting corporate bankruptcy

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Abstract

In financial distress analysis, the diagnosis of firms at risk for bankruptcy is crucial in preparing to hedge against any financial damage the at-risk firms stand to inflict. Some pre-alarm signals that indicate a potential financial crisis exist when a firm faces a default risk. Early studies on corporate bankruptcy prediction include parametric and nonparametric approaches, such as artificial intelligence (AI), for detecting pre-alarm signals. Among nonparametric techniques, the methods involving support vector machine (SVM) have shown potential in predicting corporate bankruptcy. We propose a hybrid method that combines data depths and nonlinear SVM for the prediction of corporate bankruptcy. We employed data depth functions to condense multivariate financial data with nonlinear and non-normal characteristics into one-dimensional space. The SVM method was introduced to classify the data points on a depth versus depth plot (DD-plot). Based on data set that records failed and non-failed manufacturing firms in Korea over 10 years, the empirical results demonstrated that the proposed method offers a higher level of accuracy in corporate bankruptcy prediction than existing methods. The proposed method is expected to provide a guidance in corporate investing for investors or other interested parties.

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Acknowledgements

This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20154030200900), and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (No. 201500000002571).

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Authors and Affiliations

  1. Department of Industrial Engineering, Hanyang University, Seoul, Korea

    Sungdo Kim, Byeong Min Mun & Suk Joo Bae

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  1. Sungdo Kim
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  3. Suk Joo Bae
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Correspondence to Suk Joo Bae.

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Kim, S., Mun, B. & Bae, S. Data depth based support vector machines for predicting corporate bankruptcy. Appl Intell 48, 791–804 (2018). https://doi.org/10.1007/s10489-017-1011-3

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