Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/202870
Authors: 
Hansen, Caspar
Hansen, Christian
Matin, Rastin
Mølgaard, Pia
Year of Publication: 
2018
Series/Report no.: 
Danmarks Nationalbank Working Papers 130
Abstract: 
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.
Subjects: 
Credit risk
Risk management
JEL: 
C45
C55
G17
G33
Document Type: 
Working Paper

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