2021 Volume 10 Issue 1 Pages 45-57
Numerous studies have been conducted on bankrupt companies, wherein researchers have often employed financial numerical data for analyzing the continuity of such companies. Additionally, several studies have been conducted in which the signs of bankruptcy have been quantitatively identified through textual data from, for example, economic reports, bulletin boards, and securities reports for corporate evaluation and stock price forecasting.
In this study, discriminant analysis was applied for identifying corporate bankruptcy by utilizing both financial numeric data and textual data. The data used in this study from a part of the Japanese annual securities report. Furthermore, a data set was created for the textual data through text mining.
The results indicate that, the macro-average F-score can reach the value of 0.941 uponincorporating both numerical and textual data, which is significantly higher than the macroaverageF-scores obtained while solely utilizing financial numerical data (0.880) or financialtextual data (0.895).
