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Öğe A Hybrid Bi-level Metaheuristic for Credit Scoring(Springer, 2020) Sen, Doruk; Donmez, Cem Cagri; Yildirim, Umman MahirThis research aims to propose a framework for evaluating credit applications by assigning a binary score to the applicant. The score is targeted to determine whether the credit application is 'good' or 'bad' in small business purpose loans. Even tiny performance improvements in small businesses may yield a positive impact on the economy as they generate more than 60% of the value. The method presented in this paper hybridizes the Genetic Algorithm (GA) and the Support Vector Machine (SVM) in a bi-level feeding mechanism for increased prediction accuracy. The first level is to determine the parameters of SVM and the second is to find a feature set that increases classification accuracy. To test the proposed approach, we have investigated three different data sets; UCI Australian data set for preliminary works, Lending Club data set for large training and testing, and UCI German and Australian datasets for benchmarking against some other notable methods that use GA. Our computational results show that our proposed method using a feedback mechanism under the hybrid bi-level GA-SVM structure outperforms other classification algorithms in the literature, namely Decision Tree, Random Forests, Logistic Regression, SVM and Artificial Neural Networks, effectively improves the classification accuracy.Öğe An Investigation of Fiat Characterization and Evolutionary Dynamics of the Cryptocurrency Market(Sage Publications Inc, 2021) Donmez, Cem Cagri; Sen, Doruk; Dereli, Ahmet Fatih; Horasan, M. Bilal; Yildiz, Cagri; Donmez, Nergis Feride KaplanRecent developments in global financial markets revealed that cryptocurrencies experienced rapid growth due to the popularity of blockchain technology and its evolving position in the digital finance industry. The rise of cryptocurrencies led economists to question generally accepted financial practices. Particularly the interaction between two different types of financial markets arose as a hot research topic to discover specific relationships and differences between major cryptocurrencies and fiat currencies. Therefore, this article aims to examine analyze by attaching importance to the Bitcoin to investigate significant linkages and analyze critical direct and indirect connections. In this research, Bitcoin-which is known as the most prominent cryptocurrency on the market-and 50 different conventional currencies are taken into consideration by applying cross-correlation, HT (hierarchical tree), and MST (minimum spanning tree) methods. The results of this work can be utilized by academicians and economists for further research related to the subject.