![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/C25E661D-AAB9-4380-BBA2-ECDD5723E69D | ||
| Year | 2014 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | 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 https://doi.org/10.1016/j.amc.2014.02.028 | ||
| Appears in Collections |
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.
Copyright © DIAS 2013
