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In this study, we aimed to explain the influencing factors of interest rate risk in Turkish banking sector. Within this scope, 20 deposit banks in Turkey were evaluated. Furthermore, annual data for the periods between 2006 and 2015 was analyzed by using panel logit method. According to the results of this analysis, it was identified that 2 explanatory variables affect interest rate risk of Turkish banks. First of all, it was determined that banks, which have higher amount of deposits, are exposed to higher amount of interest rate risk. In addition to this situation, it was also defined that there is a negative relationship between the amount of capital and interest rate risk. As a result of this issue, it was recommended that Turkish banks should increase capital amount in order to manage interest rate risk more effectively.

The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection) has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection.