A novel semisupervised learning method with textual information for financial distress prediction

Abstract

Financial distress prediction (FDP) has attracted high attention from many financial institutions. Utilizing supervised learning-based methods in FDP, however, is time consuming and labor intensive. Therefore, in this paper, we exploit active-pSVM method, which combines potential data distribution information and existing expert experience to solve FDP problem. Moreover, with the increasingly popular textual information, we construct several features on our protocol that are based on the Management Discussion and Analysis (MD&A) text information. Using datasets that are collected in different time windows from the listed Chinese companies, we conducted an extensive experiment and were able to confirm a better efficiency of our active-pSVM, when compared with some common supervised learning-based methods. Our study also covers the application of MD&A text information on weakly supervised learning model in FDP.