Soufiane Benbachir

This paper investigates the multifractal behavior of the six largest African stock markets, including the Johannesburg, Casablanca, Botswana, Nigerian, Egyptian, and Regional Stock Exchanges. Despite the growing significance of these markets in the global economy, there is limited understanding of their underlying dynamics, particularly regarding their multifractal properties. This lack of knowledge raises concerns about the informational efficiency of these markets, as traditional models may not adequately capture the complexities of price movements. To achieve the goals of the study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method is applied to capture the multifractal dynamics, and shuffling and phase randomization techniques are performed to identify the sources of the multifractality of the six African stock markets. The empirical results, derived from the generalized Hurst exponents, Rényi exponents, and Singularity spectrum, show that all six stock markets display multifractal behavior, characterized by irregular and complex price movements that vary across different scales and timeframes. Additionally, the study finds that both long-term correlations and heavy-tailed distributions contribute to the observed multifractality. Long-term correlations lead to persistent price trends, challenging the Efficient Market Hypothesis (EMH), while heavy tails increase market unpredictability by raising the likelihood of extreme events like crashes or booms. The findings have significant practical implications for stakeholders in African stock markets, enabling investors and portfolio managers to enhance risk assessment and develop effective trading strategies while helping market regulators improve efficiency and stability through appropriate policies. Financial institutions can also refine risk management frameworks to better account for extreme events.

In today’s volatile financial environment, banks encounter various risks, including political instability, regulatory changes, and global market fluctuations, which can undermine efficiency and threaten systemic stability. This study focuses on banking efficiency in the MENA region, highlighting its crucial role in economic growth and financial stability. This paper addresses the gap in banking efficiency research in the MENA region by evaluating the technical and pure technical efficiency of 59 conventional banks from 11 MENA countries between 2019 and 2023 and identifying the internal and external factors affecting their efficiency. Using a Data Envelopment Analysis, the study evaluates efficiency based on three inputs and two outputs. A panel Tobit regression model is then applied to analyze the impact of eight internal factors and four external factors on efficiency. The findings indicate that just 16% of the MENA banks were technically efficient, with Qatari banks outperforming and banks in Morocco and Jordan underperforming. The Tobit regression model results indicate that both return on assets and capital adequacy positively influence technical efficiency (TE) and pure technical efficiency (PTE). In contrast, Liquidity and operational costs negatively affect PTE and TE. Non-performing loans negatively impact TE but not PTE, and macroeconomic factors positively influence both TE and PTE. In conclusion, banks in the MENA region must prioritize improving their efficiency to stay competitive. The findings offer valuable insights into operational best practices and provide practical guidance for policymakers, regulators, and banking institutions to enhance the performance of the region’s financial systems.