African Journal of
Business Management

  • Abbreviation: Afr. J. Bus. Manage.
  • Language: English
  • ISSN: 1993-8233
  • DOI: 10.5897/AJBM
  • Start Year: 2007
  • Published Articles: 4092

Full Length Research Paper

Information disclosure prediction using a combined rough set theory and random forests approach

  Der-Jang Chi1 and Ching-Chiang Yeh2*    
  1Department of Accounting, Chinese Culture University, Taipei, Taiwan. 2Department of Business Administration, National Taipei College of Business, Taipei, Taiwan.  
Email: [email protected]

  •  Accepted: 17 August 2011
  •  Published: 23 November 2011



In recent years, corporate disclosure and transparency analysis has been of interest in the academic and business community. The objective of this study is to increase the accuracy of information disclosure prediction by combining rough set theory (RST) and random forests (RF) technique, while adopting corporate governance as predictive variables. The effectiveness of this methodology has been verified by experiments comparing RF model. The sample is based on 580 Taiwan information technology (IT) firm in 2007. The results show that the proposed model provides better prediction results and corporate governance does provide valuable information in information disclosure prediction model.


Key words: Information disclosure, rough set theory, random forests.