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Corporate distress models typically only employ the numerical financialvariables in the firms' annual reports. We develop a model that employs theunstructured textual data in the reports as well, namely the auditors' reportsand managements' statements. Our model consists of a convolutional recurrentneural network which, when concatenated with the numerical financial variables,learns a descriptive representation of the text that is suited for corporatedistress prediction. We find that the unstructured data provides astatistically significant enhancement of the distress prediction performance,in particular for large firms where accurate predictions are of the utmostimportance. Furthermore, we find that auditors' reports are more informativethan managements' statements and that a joint model including both managements'statements and auditors' reports displays no enhancement relative to a modelincluding only auditors' reports. Our model demonstrates a direct improvementover existing state-of-the-art models.

Predicting Distresses using Deep Learning of Text Segments in Annual Reports