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Data depth based support vector machines for predicting corporate bankruptcy

Title
Data depth based support vector machines for predicting corporate bankruptcy
Author
배석주
Keywords
Artificial neural network; Bankruptcy prediction; Classification model; Data depth; DD-plot; Support vector machine
Issue Date
2018-03
Publisher
SPRINGER
Citation
APPLIED INTELLIGENCE, v. 48, no. 3, page. 791-804
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.
URI
https://link.springer.com/article/10.1007%2Fs10489-017-1011-3http://repository.hanyang.ac.kr/handle/20.500.11754/117760
ISSN
0924-669X; 1573-7497
DOI
10.1007/s10489-017-1011-3
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
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