This paper examines the impact of mergers on the financial performance of the Jordanian banking sector. This paper applies the financial approaches in analysing the effects of mergers on Jordanian banks' performance for two the periods: four years pre-merger and four years' post-merger for the period from 2001 to 2009. The sample of the study solely contains the case of the merger of the Jordan Ahli Bank (AHLI bank) with Philadelphia Bank in 2005. Data are tested for normality using the Shapiro-Wilk Test and Kolmogorov Smirnov test. The financial ratios and a statistical technique as a Mann-Whitney U test were used to assess the significant differences in the financial performance of the selected banks pre-and post-merger by investigating the performance-related financial ratio groups that are expressed by leverage, liquidity, efficiency, and cash flow ratio. The results show that there is an insignificant improvement in the ratios of AHLI bank in the period after the merger, except for the superior result provided by this study indicating that the leverage ratios improved significantly. The reason for the insignificant improvement in financial ratios may be that the post-merger period corresponds to the period of the global financial crisis that began in 2007.

This study attempts to develop bankruptcy prediction model for the Jordanian industrial sector with a recent approach—neural networks. The multilayer perceptron neural network (MPNN) approach was used to develop the bankruptcy prediction model for the Jordanian industrial companies for the period from 2000 to 2015. The samples have been divided into two subsets: the first set for developing or building the model, made up of 14 companies, of which 7 are bankrupt and 7 are non-bankrupt; while the second is a hold-out sample for testing the model, made up of 18 companies, of which 9 are bankrupt and 9 are non-bankrupt. The main variables in predicting bankruptcy were ten financial ratios. The results show that the accuracy rate of final prediction model is found to be 100 percent. While the hold-out sample testing provides that the model correctly predicted all 18 test cases.

This paper intends to investigate whether the financial crisis (2008) exerted an impact on the level of accounting conservatism in the case of Jordanian commercial banks before and during the financial crisis. The sample of this study includes 78 observations; these observations are based on the financial statements of all commercial banks in Jordan and may be referred to as cross-sectional data, whereas the period from 2005 to 2011 represents a range of years characterized by time series data. The appropriate regression model to measure the relationship between cross-sectional data and time series data is in this case the pooled data regression (PDR) using the ordinary least squares (OLS) method. The results indicate that the level of accounting conservatism had been steadily increasing over a period of three years from 2005 to 2007. The results also indicate that the level of accounting conservatism was subjected to an increase during crisis period between 2009 and 2011 compared with the level of accounting conservatism for the period 2005-2007 preceding the global financial crisis. The F-test was used in order to test the significant differences between the regression coefficients for the period before and during the global financial crisis. The results indicate a positive impact on the accounting conservatism during the global financial crisis compared with the period before the global financial crisis. The p-value is 0.040 which indicates that there are statistically significant differences between the two periods; these results are consistent with the results in Sampaio (2015).

The main purpose of this study is to develop and compare the classification accuracy of bankruptcy prediction models using the multilayer perceptron neural network, and discriminant analysis, for the industrial sector in Jordan. The models were developed using the ten popular financial ratios found to be useful in earlier studies and expected to predict bankruptcy. The study sample was divided into two samples; the original sample (n=14) for developing the two models and a hold-out sample (n=18) for testing the prediction of models for three years prior to bankruptcy during the period from 2000 to 2014.The results indicated that there was a difference in prediction accuracy between models in two and three years prior to failure. The results indicated that the multilayer perceptron neural network model achieved a higher overall classification accuracy rate for all three years prior to bankruptcy than the discriminant analysis model. Furthermore, the prediction rate was 94.44% two years prior to bankruptcy using multilayer perceptron neural network model and 72.22% using the discriminant analysis model. This is a significant difference of 22.22%. On the other side, the prediction rate of 83.34% three years prior to bankruptcy using multilayer perceptron neural network model and 61.11% using discriminant analysis model. We indicate there was a difference exists of 22.23%. In addition, the multilayer perceptron neural network model provides in the first two years prior to bankruptcy the lowest percentage of type I error.