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Αξιολόγηση πιστωτικού κινδύνου επιχειρήσεων μέσω λογιστικών στοιχείων και υποδειγμάτων αποτίμησης δικαιωμάτων προαίρεσης

Niklis Dimitrios

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Summary

Credit risk evaluation is a very challenging and important problem in the domain of financial risk management. It 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 for constructing credit risk assessment rating systems. The aim of this thesis is twofold. First, an empirical comparison of different popular techniques (logistic regression, support vector machines, and the UTADIS multicriteria method) using a data set of Greek companies from the commercial sector is executed. The results show that even with a considerable imbalanced data set with a small number of defaults, all methods provide good results. The second goal is to create a credit risk rating model, using a machine learning methodology and a multicriteria method that combines accounting data with the option-based approach of Black, Scholes, and Merton and the extension to non-listed firms. The model is built on data for companies listed in the Greek stock exchange, but it is also shown that the predictive performance is similar to accounting-based models developed using (non- publicly available) historical default data.

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