The application of corporate governance indicators with xbrl technology to financial crisis prediction

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107355

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Title:	The application of corporate governance indicators with xbrl technology to financial crisis prediction
Authors:	梁德容;Li, Chien-Kuo;Liang, Deron;Lin, Fengyi;Chen, Kwo-Liang
Contributors:	資訊電機學院資訊工程學系
Keywords:	Corporate governance;corporate governance indicators;Economic crisis;Extensible Business Reporting Language;extensible business reporting language (XBRL);feature selection;financial crisis prediction;genetic algorithm;Genetic algorithms;Mathematical models;Studies;support vector machine (SVM)
Date:	2015-01-30
Issue Date:	2026-04-23 14:09:58 (UTC+8)
Publisher:	M.E. Sharpe Inc.;Abingdon: Routledge
Abstract:	摘要： The 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. 出版者： Abingdon: Routledge 出版日期： 2015-01-01 出處： Emerging markets finance & trade, 2015-01, Vol.51 (sup1), p.S58-S72 資源來源： Taylor & Francis Journals Auto-Holdings Collection 版權： Copyright © Taylor & Francis Group, LLC 版權： Copyright M. E. Sharpe Inc. 2015 識別號： ISSN: 1540-496X 識別號： EISSN: 1558-0938 識別號： DOI: 10.1080/1540496X.2014.998888
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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