Working Paper: Predicting distresses using deep learning of text segments in annual reports
Working Paper - November 2018 - No. 130
We develop a probability-of-default model for Danish corporate firms based on deep learning that employs the managements' statements and auditors' reports of the annual reports in addition to the numerical financial variables. Our results show that the text segments provide a statistically significant enhancement of the prediction accuracy compared to models that do not employ the text segments, in particular for large firms. Our results furthermore show that the auditors' reports contain more relevant information than the managements' statements.