  Corporate distress models typically only employ the numerical financial
variables in the firms' annual reports. We develop a model that employs the
unstructured textual data in the reports as well, namely the auditors' reports
and managements' statements. Our model consists of a convolutional recurrent
neural network which, when concatenated with the numerical financial variables,
learns a descriptive representation of the text that is suited for corporate
distress prediction. We find that the unstructured data provides a
statistically significant enhancement of the distress prediction performance,
in particular for large firms where accurate predictions are of the utmost
importance. Furthermore, we find that auditors' reports are more informative
than managements' statements and that a joint model including both managements'
statements and auditors' reports displays no enhancement relative to a model
including only auditors' reports. Our model demonstrates a direct improvement
over existing state-of-the-art models.
