URI | http://purl.tuc.gr/dl/dias/C25E661D-AAB9-4380-BBA2-ECDD5723E69D | - |
Αναγνωριστικό | http://www.sciencedirect.com/science/article/pii/S0096300314002677 | - |
Αναγνωριστικό | https://doi.org/10.1016/j.amc.2014.02.028 | - |
Γλώσσα | en | - |
Μέγεθος | 13 pages | en |
Τίτλος | Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines | en |
Δημιουργός | Niklis Dimitrios | en |
Δημιουργός | Νικλης Δημητριος | el |
Δημιουργός | Michael Doumpos | en |
Δημιουργός | Δουμπος Μιχαλης | el |
Δημιουργός | Zopounidis Konstantinos | en |
Δημιουργός | Ζοπουνιδης Κωνσταντινος | el |
Εκδότης | Elsevier | en |
Περίληψη | Credit risk rating is an important issue for both financial institutions and companies, especially in periods of economic recession. There are many different approaches and methods which have been developed over the years. The aim of this paper is to create a credit risk rating model, using a machine learning methodology that combines accounting data with the option-based approach of Black, Scholes, and Merton. The model is built on data for companies listed in the Greek stock exchange, but it is also shown to provide accurate results for non-listed firms as well. Linear and nonlinear support vector machines are used for model building, as well as an innovative additive modeling approach, which enables the construction of comprehensible and accurate credit scoring models. | en |
Τύπος | Peer-Reviewed Journal Publication | en |
Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
Ημερομηνία | 2015-11-05 | - |
Ημερομηνία Δημοσίευσης | 2014 | - |
Θεματική Κατηγορία | Credit risk | en |
Θεματική Κατηγορία | Black–Scholes–Merton model | en |
Θεματική Κατηγορία | Credit rating | en |
Θεματική Κατηγορία | Support vector machines | en |
Βιβλιογραφική Αναφορά | D. Niklis, M. Doumpos and C. Zopounidis, "Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines," Appl. Math. Computat., vol. 234, pp. 69-81, May 2014. doi:10.1016/j.amc.2014.02.028 | en |