Open AccessCorporate Credit Risk Assessment of BIST Companies
Author(s) -
Olcay Erdogan,
Zafer Konakli
Publication year - 2018
Publication title -
european scientific journal
Language(s) - English
Resource type - Journals
eISSN - 1857-7881
pISSN - 1857-7431
DOI - 10.19044/esj.2018.v14n1p122
Subject(s) - profitability index , decision tree , credit risk , support vector machine , artificial neural network , adaptive neuro fuzzy inference system , default , computer science , sample (material) , linear discriminant analysis , investment (military) , financial risk , machine learning , artificial intelligence , risk management , business , fuzzy logic , finance , data mining , fuzzy control system , chemistry , chromatography , politics , political science , law
Assessing credit risk allows financial institutions to plan future loans freely, to achieve targeted risk management and gain maximum profitability. In this study, the constructed risk assessment models are on a sample data which consists of financial ratios of enterprises listed in the Bourse Istanbul (BIST). 356 enterprises are classified into three levels as the investment, speculative and below investment groups by ten parameters. The applied methods are discriminant analysis, k nearest neighbor (k-NN), support vector machines (SVM), decision trees (DT) and a new hybrid model, namely Artificial Neural Networks with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This study will provide a comparison of models to build better mechanisms for preventing risk to minimize the loss arising from defaults. The results indicated that the decision tree models achieve a superior accuracy for the prediction of failure. The model we proposed as an innovation has an adequate performance among the applied models