Elsayed A. H. Elamir

The study assesses corporate forward-looking disclosure by measuring four attributes, namely disclosure quantity, disclosure coverage, disclosure concentration and disclosure quality, through a sample of 34 listed firms in the Bahrain Bourse from 2014 to 2017. The study also investigates the relationship between these attributes and stock return volatility. Regression analysis has been employed with five different models to examine the relationship between the four attributes of corporate forward-looking disclosure and stock return volatility. The main finding of this study agrees with the results of Bravo et al. (2009) who found that the selection of a specific disclosure index could influence crucially the results of the analysis. In addition, stock return volatility has a statistically significant negative association with the three attributes of forward-looking disclosure, namely disclosure quantity, disclosure coverage and disclosure quality. In contrast, it has a non-significant association with the fourth attribute of forward-looking disclosure, disclosure concentration. This study provides a novel contribution to disclosure quality studies by being the first study to examine forward-looking disclosure quality attributes in the Kingdom of Bahrain.

This study is of an exploratory nature as it seeks to explore the extent to which the language of emotions in the banks’ annual reports is affected by the global financial crisis (GFC). The language of emotions was analyzed using eight categories (trust, anticipation, sadness, anger, fear, disgust, surprise and joy) in annual reports of 12 listed banks from six countries in the Middle East area (namely, Jordan, Kingdom of Bahrain, United Arab Emirates, Sultanate of Oman, Kuwait, Kingdom of Saudi Arabia) from 2002 to 2017. The final data set consists of 192 bank-year observations. The study time was divided into three periods (pre, during and post GFC). In addition, the study enriches accounting literature by being the first study to test Pollyanna hypothesis using emotion analysis. The results of the study show that the percentage of emotional words in banks’ annual reports (2002–2017) represents almost 22% on average. The trust, anticipation and fear categories were the most affected than other emotional categories during GFC. While the trust category decreased, both the fear and anticipation categories increased. Other findings of the study show that regardless of GFC, emotional words of trust and anticipation categories in banks’ annual reports have dominated the emotional words of the disgust and surprise categories. Therefore, Pollyanna hypothesis is supported. In contrast to the emotional words of the joy category in banks’ annual reports which has not dominated the sadness category. In this case, Pollyanna hypothesis is rejected.

This study aims to explore the determinant indicators for the labor market efficiency and the higher education and training factors that can help in increasing the productivity in labor market and the quality in higher education and training, as well as pays attention to important relative indicators to improve the relationship between them. To achieve these aims the canonical correlation analysis is used as a bidirectional technique that allows studying the mutual relationship between two factors by taking advantage of available reports from 2012 to 2018 published by World Economic Forum (WEF).
The results indicate that the extent of staff training, internet access, quality of education, and quality of management schools are the most important indicators in higher education and training and most correlated with labor market efficiency factor. The capacity to attract talent, pay and productivity, cooperation in labor-employer relations, and reliance on professional management are the most important indicators in labor market efficiency and the most correlated with higher education and training factor. The commonality analysis gives interesting results and shows that the explained variance in labor market efficiency and higher education and training depends on common indicators rather than a unique indicator.

The regression tree approach is an effective and easy to interpret technique where it utilizes a recursive binary partitioning algorithm that divides the sample into partitioning variables with the strongest correlation to the response variable. Earnings per share can be considered as one of the main factors in making the investment decision. This study aims to build a predictive model for earnings per share in the context of the Middle East and North African countries (MENA) . The sample of the study consists of sixty-three banks, which were chosen from eight countries, with a total of six-hundred thirty observations. The simple regression, regression tree, and its pruned regression tree, conditional inference tree, and cubist regression are used to build the predictive model for earnings per share that depends on total assets, total liability, bank book value, stock volatility, age of the bank, and net cash. The results show that the cubist regression is outperforming other approaches where it improves root mean square error for the predictive model by approximately double in comparison with other methods. More interesting results are obtained from the important scores, where it shows that the total assets of the bank, bank book value, and total liability have the biggest impact on the prediction of earnings per share. Also, the cubist regression gives an improvement in R-squared over other methods by at least 30% and 23% using training and testing data, respectively.