Purpose: This research aims to identify the factors influencing stability reporting in the Islamic financial services industry, with a specific focus on financial performance, efficiency, asset quality, and capital adequacy.Design/Methodology: Utilizing panel data from Islamic financial institutions across ten countries (Iran, Saudi Arabia, Malaysia, UAE, Kuwait, Qatar, Turkey, Bangladesh, Indonesia, and Bahrain), the research employs the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) approach to address cross-sectional dependence and heterogeneity in panel data. The model's validity and consistency were confirmed through robust diagnostic tests, including cointegration and cross-sectional dependence tests.Findings: This study finds that financial performance (ROA) and capital adequacy (CAR) are key drivers of stability in the Islamic financial services industry. Institutions with higher profitability and stronger capital buffers are better able to withstand economic pressures and maintain stable operations, both in the short and long term. In contrast, inefficiency (high Cost to Income Ratio) and poor asset quality (high NPF) weaken stability. When institutions operate inefficiently, their costs erode profits that could otherwise serve as a cushion during difficult times. Likewise, when financing defaults increase, asset quality deteriorates, directly threatening institutional soundness. These findings indicate that achieving stability requires Islamic financial institutions to simultaneously strengthen profitability, maintain adequate capital, control operational costs, and manage credit risk effectively.Practical Implications: This study offers practical implications for regulators and financial institutions to enhance stability monitoring and risk management frameworks, thereby improving systemic resilience within the Islamic financial services industry.Originality/Value: This study contributes to the literature on Islamic financial stability by providing robust empirical evidence on the critical determinants of stability reporting, using a sophisticated methodology to account for data complexities.
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