The Application of Corporate Governance Indicators With XBRL Technology to Financial Crisis Prediction |
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| Authors: | Chien-Kuo Li Deron Liang Fengyi Lin Kwo-Liang Chen |
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| Affiliation: | 1. Department of Information Technology and Management, Shih Chien University, Taipei, Taiwanckli@g2.usc.edu.tw;3. Department of Computer Science and Information Engineering, and Software Research Center, National Central University, Taoyuan, Taiwan;4. Department of Business Management, National Taipei University of Technology, Taipei, Taiwan;5. Information Technology Division, Small &6. Medium Enterprise Administration, Ministry of Economic Affairs, Taipei, R.O.C. |
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| Abstract: | ABSTRACTThe widespread adoption of eXtensible Business Reporting Language (XBRL) suggests that intelligent software agents can now use financial information disseminated on the Web with high accuracy. Financial data have been widely used by researchers to predict financial crises; however, few studies have considered corporate governance indicators in building prediction models. This article presents a financial crisis prediction model that involves using a genetic algorithm for determining the optimal feature set and support vector machines (SVMs) to be used with XBRL. The experimental results show that the proposed model outperforms models based on only one type of information, either financial or corporate governance. Compared with conventional statistical methods, the proposed SVM model forecasts financial crises more accurately. |
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| Keywords: | corporate governance indicators extensible business reporting language (XBRL) feature selection financial crisis prediction genetic algorithm support vector machine (SVM) |
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