Forecasting of financial data has been a field of research since the efficiency of prediction is essential for strategical decision making. Forecasting exchange rates is not a simple task because it is influenced by many factors and linear models are not able to capture nonlinear relationships in the data. Therefore ANNs have been used in financial forecasting problems since it is capable of handling complex data. The aim of this study is to consider predictive accuracy of ANNs with normalized back propagation using the historical Euro and Turkish Lira (EUR/TRY) exchange rates. The data is obtained from CBRT (Central Bank of the Republic of Turkey) over the period 2010-2013. Several factors affect the accuracy of neural network in the implementation process. Various structures are built by changing the number of neurons, transfer functions and learning algorithms to acquire higher performance. This empirical research has been a comparative study of accuracy in different ANN architectures also in different time horizons. The results are evaluated by MSE (Mean Squared Error) values of each case and it has been found out that ANNs can closely forecast the future EUR/TRY exchange rates.

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

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