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The primary focus of this paper is to address the determinants of profitability on commercial banks in Jordan and to examine to what extent the performance of commercial banks operating in Jordan are affected by internal and external factors of companies listed on the Amman Stock Exchange for Jordanian Banks between 2007 and 2012. Previous studies focused only on internal factors, namely, the banks' specific characteristics in Jordan. This study includes not only the internal factors, but also external factors, namely, macroeconomic and financial market structures. The question is whether there are significant impacts that can be gained from internal and external factors on ROAA. The internal factors of capital adequacy, liquidity ratio, and size are found to be significant as well as all the external factors in these models. A third multivariate model which includes both internal and external factors is included in this study but not in previous studies. This model is found to be significant.As a result, this research gives deeper insights into determinants influencing the profitability of Jordanian commercial banks within the Jordanian environment.

Corporate governance is the way of governing a firm in order to increase its accountability and to avoid any massive damage before it occurs. The aim of this paper is to investigate the impact of capital structure, firms’ size, and competitive advantages of firms as control variables on credit ratings. We investigate the role of corporate governance in improving the firms’ credit rating using a sample of Jordanian listed firms. We split firms into four categories according to WVB credit rating. We use both the binary logistic regression (LR) and the ordinal logistic regression (OLR) to model credit ratings in Jordanian environment. The empirical results show that the control variables are strong determinants of credit ratings. When we evaluate the relationship between the governance variables and credit ratings, we found interesting results. The board stockholders and board expertise are moderately significant. The board independence and role duality are weakly significant, while board size is insignificant.

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Credit scoring is a crucial problem in both finance and banking. In this paper, we tackle credit scoring as a classification problem where three local search-based methods are studied for feature selection. The feature selection is an interesting technique that can be launched before the data classification task. It permits to keep only the relevant variables and eliminate the redundant ones which enhances the classification accuracy. We study the local search method (LS), the stochastic local search method (SLS) and the variable neighborhood search method (VNS) for feature selection. Then, we combine these methods with the support vector machine (SVM) classifier to find the best described model from a dataset with the correct class variable. The proposed methods (LS+SVM, SLS+SVM and VNS+SVM) are evaluated on both German and Australian credit datasets and compared with some well-known classifiers. The numerical results are promising and show a good performance in favor of our methods.