Earticle

초록

영어

Traditional corporate bankruptcy prediction methodology basically relies on financial accounting data to objectively reflect the status of companies. However, since financial accounting data is difficult to immediately reflect changes in the status of companies, real-time financial data such as stock and bond prices are also used in order to make up for the shortcomings. In this study, we use news text information which is a typical real-time information to study the corporate bankruptcy prediction models. In the past, news text information was difficult to use in quantitative analysis but not any more due to the recent advances of information processing technology and text-mining techniques. For bankruptcy prediction using news information, we collect news text for six months before the bankruptcy events of companies actually occur and study the possibility of bankruptcy prediction based on the data by utilizing text-mining techniques. Results indicate that we can not get such a high level of predictability as that of existing corporate bankruptcy prediction models, but that there exists a high potential of this approach enough to increase the predictability of bankruptcy models. Further research on bankruptcy prediction model using news text information will be promising.

목차

Ⅰ. 서론
 Ⅱ. 연구방법론
 Ⅲ. 실증분석
 Ⅳ. 결론 및 제언
 참고문헌
 Abstract

키워드

저자

• 최정원 [ Jung-won Choi | 딜로이트안진, 건국대학교 경영학과 박사과정 ] 주저자
• 한호선 [ Ho-sun Han | 유세스파트너스, 건국대학교 경영정보학과 박사과정 ] 공동저자
• 이미영 [ Mi-young Lee | 건국대학교 경영정보학과 교수 ] 교신저자
• 안준모 [ Jun-mo Ahn | 건국대학교 경영정보학과 교수 ] 공동저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  생산성연구: 국제융합학술지 [Productivity Research: An International Interdisciplinary Journal]

• 간기

  격월간

• pISSN

  1225-3553

• 수록기간

  1987~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 325 DDC 330