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pro vyhledávání: '"Mølgaard, Pia"'
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' statem
Externí odkaz:
http://arxiv.org/abs/1811.05270
Publikováno v:
In Journal of Financial Intermediation April 2021 46
Publikováno v:
In Expert Systems With Applications 15 October 2019 132:199-208
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' statem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1687::a1dfb50a1561d218997337657949d592
https://hdl.handle.net/10419/202870
https://hdl.handle.net/10419/202870
The collateralized loan obligation, CLO, market withstood the recent financial crisis with minimal losses compared to other structured asset-backed securities. Furthermore, the issuance of new CLOs is now above pre-crisis levels, prompting an underst
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1687::8e5a1c1745f882d678ecd44f17111808
https://hdl.handle.net/10419/202867
https://hdl.handle.net/10419/202867
Accurate probability-of-distress models are central to regulators, firms, and individuals who need to evaluate the default risk of a loan portfolio. A number of papers document that recent machine learning models outperform traditional corporate dist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1687::e9158193e7a396a8d0a37ef10e68df25
https://hdl.handle.net/10419/202868
https://hdl.handle.net/10419/202868