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

Chien Kuo Li, Deron Liang, Fengyi Lin, Kwo Liang Chen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)S58-S72
JournalEmerging Markets Finance and Trade
Volume51
DOIs
StatePublished - 30 Jan 2015

Keywords

  • Extensible business reporting language (XBRL)
  • Feature selection
  • Financial crisis prediction
  • Genetic algorithm
  • Support vector machine (SVM)
  • corporate governance indicators

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