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.