@inproceedings{oai:grips.repo.nii.ac.jp:00001414,
 author = {YANG, Xiaopeng and DIMITROV, Stanko},
 month = {Dec},
 note = {The workshop is supported by JSPS (Japan Society for the Promotion of Science), Grant-in-Aid for Scientific Research (B), #25282090, titled “Studies in Theory and Applications of DEA for Forecasting Purpose., 本研究はJSPS科研費 基盤研究(B) 25282090の助成を受けたものです。, Research on corporate failure prediction has drawn numerous scholars’ attention because of its usefulness in corporate risk management, as well as in regulating corporate operational status. Most previous research related to this topic focused on manufacturing companies and relied heavily on corporate assets. The asset size of a manufacturing company plays a vital role in traditional research methods; Altman’s Z score model is one such traditional method. However, very limited number of research studied corporate failure prediction for nonmanufacturing companies as the operational status of such companies is not solely correlated to their assets. In this manuscript we use support vector machines (SVMs) and data envelopment analysis (DEA) to provide a new method for predicting corporate failure of nonmanufacturing firms. We first generate efficiency scores using a slack-based measure (SBM) DEA model, using the recent three years historical data of nonmanufacturing firms; then we used SVMs to classify bankrupt firms and healthy ones. We show that using DEA scores as the only inputs into SVMs predict corporate failure more accurately than using the entire raw data available., Workshop 2015 -Advances in DEA Theory and Applications (December 1-2, 2015)},
 pages = {11--20},
 publisher = {出版社不明},
 title = {Combining Support Vector Machine and Data Envelopment Analysis to Predict Corporate Failure for Nonmanufacturing Firms},
 year = {2015}
}