International Journal of Finance & Banking Studies

Location selection problem in banking is an important issue for the commercial success in competitive environment. There is a strategic fit between the location selection decision and overall performance of a new branch. Providing physical service in requested location as well as alternative distribution channels to meet profitable client needs is the current problematic to achieve the competitive advantage over the rivalry in financial system. In this paper, an integrated model has been developed to support in the decision of branch location selection for a new bank branch. Analytic Hierarchy Process (AHP) technique has been conductedto prioritize of evaluation criteria, and multi-objective optimization on the basis of ratio analysis (MOORA) method has been applied to rank location alternatives of bank branch.

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

<p>Nowadays, almost all sectors especially service ones are aware of the fact that the presence of employees who have high level of emotional intelligence and innovative work behaviors is crucial so as to perform efficiently and productively. Since customer satisfaction and customer loyalty are the main primacy for the banking sector, they need to keep up with all kinds of changes and developments to resist and gain advantages over their competitors subject to the highly increase of competition worldwide.  The main reason of applying this study on the banking sector is its dynamic structure that has been changed both positively and negatively owing to the economic crisis. In connection with this, it is aimed to present the importance of emotional intelligence and innovative work behaviors on the success of banking sector through the analysis of two variables in terms of their relationship between each other. Additionally, it is intended for setting forth the differences of state-owned and private banks in terms of the level of emotional intelligence and innovative work behaviors. As a data collection tool, questionnaire technique has been used. It has been benefited from the emotional intelligence scale developed by Wong and Law to obtain the data related to emotional intelligence level; and the innovative work behaviors scale developed by Janssen to obtain the data related to innovative work behaviors. The findings obtained from the questionnaire were applied to 332 employees in Turkish banking sector.</p>

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

Location selection problem in banking is an important issue for the commercial success in competitive environment. There is a strategic fit between the location selection decision and overall performance of a new branch. Providing physical service in requested location as well as alternative distribution channels to meet profitable client needs is the current problematic to achieve the competitive advantage over the rivalry in financial system. In this paper, an integrated model has been developed to support in the decision of branch location selection for a new bank branch. Analytic Hierarchy Process (AHP) technique has been conductedto prioritize of evaluation criteria, and multi-objective optimization on the basis of ratio analysis (MOORA) method has been applied to rank location alternatives of bank branch.

<p>Nowadays, almost all sectors especially service ones are aware of the fact that the presence of employees who have high level of emotional intelligence and innovative work behaviors is crucial so as to perform efficiently and productively. Since customer satisfaction and customer loyalty are the main primacy for the banking sector, they need to keep up with all kinds of changes and developments to resist and gain advantages over their competitors subject to the highly increase of competition worldwide.  The main reason of applying this study on the banking sector is its dynamic structure that has been changed both positively and negatively owing to the economic crisis. In connection with this, it is aimed to present the importance of emotional intelligence and innovative work behaviors on the success of banking sector through the analysis of two variables in terms of their relationship between each other. Additionally, it is intended for setting forth the differences of state-owned and private banks in terms of the level of emotional intelligence and innovative work behaviors. As a data collection tool, questionnaire technique has been used. It has been benefited from the emotional intelligence scale developed by Wong and Law to obtain the data related to emotional intelligence level; and the innovative work behaviors scale developed by Janssen to obtain the data related to innovative work behaviors. The findings obtained from the questionnaire were applied to 332 employees in Turkish banking sector.</p>