텍스트마이닝 방법론을 활용한 기업 부도 예측 연구

Traditional corporate bankruptcy prediction methodology basically relies on financialaccounting data to objectively reflect the status of companies. However, since financialaccounting 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 upfor the shortcomings. In this study, we use news text information which is a typical real-time information tostudy the corporate bankruptcy prediction models. In the past, news text information wasdifficult to use in quantitative analysis but not any more due to the recent advances ofinformation processing technology and text-mining techniques. For bankruptcy prediction using news information, we collect news text for six monthsbefore the bankruptcy events of companies actually occur and study the possibility ofbankruptcy 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 ofexisting corporate bankruptcy prediction models, but that there exists a high potential ofthis approach enough to increase the predictability of bankruptcy models. Further researchon bankruptcy prediction model using news text information will be promising.