Predicting stock price remains one of the challenges for investors' investment strategies. This study helps with accurate prediction and the main factors affecting variations in stock prices. It applies an adaptive neuro-fuzzy model on 58 listed firms from both the Abu Dhabi Securities Exchange and the Dubai Financial Market for the period 2014-2018 to estimate the predictive power of corporate performance measures and their significance. After examining four performance predictors-return on asset (ROA), return on equity (ROE), earning per share (EPS), and profit margin (PM)-the study finds that ROE is the most significant predictor and ROA is the least. EPS is the most influential profitability measure and PM the least.

This study aims to investigate the determinants of capital structure (CS), how they differ among levels (upstream, midstream, and downstream), and to identify Which CS theory is more relevant to the oil and gas companies in the GCC. It uses secondary data of 22 listed oil and gas companies in the GCC over ten years (2010 and 2019). The study will add to the literature as there is few studies about CS in the petroleum industry and it is the only study about the GCC oil and gas sector. Using pooled ordinary least square (OLS) random effect model, the main findings of this study are; the CS has a positive significant relationship with the size and tangibility, negative with profitability, and insignificant with growth in sales, market to book value, and price to earnings ratio. the research concluded that the GCC oil companies are aligned with both trade-off theory and pecking order theory. The results show that only the determinants of downstream companies are significant, while middle stream and upstream have no significant impact on CS. One of the limitations is unavailability of data of some governmental oil companies and further research is needed to include non-financial determinants and investigate relationships between CS and the value of companies.

The study aims to operationalize financial reporting quality in terms of the qualitative characteristics (QCs) as stated by the Accounting and Auditing Organization of Islamic Financial Institutions (AAOIFI) standards, as well as to investigate their association with earnings quality (EQ) and banking performance. The study uses secondary data extracted from DataStream to operationalize and measure the financial reporting quality in the annual reports of 25 out of the 27 Islamic banks in the Gulf Council Countries (GCC) for a 5-year period (2014–2018), meaning 125 annual reports were used. The study applies a manual content analysis to the annual reports to score all the items of QCs and operationalizes 25 measurement items that represent the six QCs. All items use 5-point Likert-type scales to compute the sub-score and the overall index through the Neural Network System. The findings of the model paths show a significant positive relationship between EQ and most of the QCs. The first hypothesis is partially accepted as there is a positive relationship between EQ and relevancy, reliability, prudence and general quality; however, there is no significant relationship between EQ and understandability and there is a significant negative relationship between EQ and comparability. Moreover, the study finds a significant positive relationship between EQ and ROA on one hand and EQ and ROE on the other hand (p-value = 0.00), meaning the second hypothesis is supported.