Journal of Money and Economy

This paper examines whether sukuk issuance can always affect the economic growth of Islamic countries and what is the role of institutional quality in this regard. A case study is conducted on selected MENA countries including Bahrain, Qatar, Saudi Arabia, Oman, and the United Arab Emirates. Macroeconomic data are collected from the World Bank and sukuk data are collected from the International Islamic Financial Market. Analyses are conducted using a balanced panel data model and weighted least squares method, and principal component analysis is used for the governance quality index. The results show that sukuk issuance, governance quality, inflation rate, and degree of economic openness all have a positive and significant effect on economic growth. These findings confirm the importance of institutional factors in enhancing the economic growth of Islamic countries.

Transparency can be defined as the provision of sufficient, relevant, and reliable information for stakeholders. Due to numerous limitations, transparency and disclosure remain a challenge in the Iranian banking sector. Therefore, the aim of this study is to examine the impact of ownership structure on the transparency and disclosure of banks. To assess ownership structure, three variables are used: the percentage of shares held by the largest shareholder, the percentage of shares held by institutional shareholders, and the percentage of shares held by individual investors. The research method is of a correlational type in terms of content and nature. This study is applied in terms of its goal and is quantitative in nature based on the data. Statistical analysis was conducted on the information of 13 banks listed on the Tehran Stock Exchange during the years 2014 to 2022. An ordinary least squares (OLS) estimator with fixed-year effects and Stata software version 17 were used to test the research hypotheses. The results of this study show that the variables of individual investors' shares, the largest shareholder's shares, and institutional shareholders' shares have the most significant impact on the transparency and disclosure of banks, respectively. These findings indicate that the presence and influence of individual investors, large shareholders, and institutional shareholders each play a crucial role in improving the transparency and disclosure of banks.

Iranian stock market, as a reflection of the real sector of the country's economy, has experienced many uncertainties and challenges in recent years. One of the macroeconomic factors that vaguely affects stock market is exchange rate, which has a significant volatile pattern with several overshoots during recent years. As a result, analyzing the impact of the exchange rate on stock price has become important as always. Bearing the restrictions on Iran’s trade and spectacular role of exchange rate in Iranian economy along with monetary expansion and fiscal dominance in mind, stock market reacts to exchange rate fluctuations in an asymmetric way. In order to prevent the impact of outliers and parametrical failures, this study seeks to examine the impact of exchange rate fluctuations on the stock price using quantile regression. Based on daily data between 2020/10/12 till 2024/12/20 and using first difference of both stock price logarithm and exchange rate logarithm, it became vivid that the impact of the exchange rate on the stock price is different in estimated quantiles and it is U shaped. Meaning when Stock market is stable and there are no absolute pattern of growth or crash around the median, exchange rate could not explain the stock price significantly. However, if the stock market goes bearish or bullish, quantiles where market is on its lowest or in top two quantiles, exchange rate has a crucial positive impact on the market which complies with evidence in the sample period.

This study seeks to rigorously assess the relationship between Iran’s stock market index and monetary policy within the framework of the Markov Switching Vector Autoregressive (MS-VAR) model. The MS-VAR methodology is particularly well-suited for capturing regime-dependent dynamics and structural shifts in macroeconomic and financial time series. For this purpose, quarterly data spanning from Spring 2009 to Fall 2023 have been employed. All estimations were conducted using EViews 12 and OX Metrics 7 software. As a preliminary step, the Hodrick-Prescott filter was applied to differentiate between two distinct market regimes. Combined with a univariate Markov Switching model, this approach enabled the identification of cyclical fluctuations in the stock market, distinguishing bull from bear market phases. The results indicate that Regime 1 (bear market) demonstrates greater persistence and stability relative to Regime 2 (bull market), suggesting asymmetric market dynamics. Subsequently, the study investigates the effects of monetary policy—proxied by the interbank market rate and liquidity growth—on the growth of the stock market index within the MS-VAR framework. The findings suggest that monetary policy has different effects during bull and bear market phases. The stock index exhibits a prompt and asymmetric response to changes in both the interbank market rate and liquidity growth. Specifically, in both bull and bear market regimes, an increase in the interbank interest rate exerts a contractionary effect on stock index growth, with a more pronounced negative impact observed during periods of market recession. Moreover, liquidity growth consistently contributes positively to stock index growth across both regimes, with a more pronounced effect under bull market conditions. Variance decomposition analysis further reveals that, in both regimes, shocks to the stock index itself account for the largest proportion of its fluctuations. Nonetheless, the relative importance of monetary policy instruments varies by regime: In both expansionary and recessionary phases of the stock market, shocks stemming from the interbank market rate play a more prominent role in explaining stock index volatility compared to liquidity shocks. Finally, the presence of nonlinear interactions among the variables is statistically validated based on the Likelihood Ratio (LR) test.

This study investigates the role of sentiment analysis in improving exchange rate prediction models, providing empirical evidence for narrative economics; the idea that economic outcomes are shaped by prevailing beliefs and popular narratives. By integrating sentiment-based features into predictive frameworks, we demonstrate that exchange rate movements are influenced by subjective factors beyond traditional economic variables. Our findings suggest that market sentiment systematically impacts currency fluctuations. To assess the effectiveness of sentiment-enhanced models, we compare various forecasting approaches. Notably, a generalized linear model (GLM) outperforms more complex deep learning architectures, including long short-term memory (LSTM) networks and hybrid CNN-LSTM models. Additionally, even an optimized multilayer perceptron (MLP) fails to surpass GLM performance, highlighting the potential linearity of the relationship between predictors and exchange rates. These results underscore the importance of aligning model complexity with the statistical properties of the target variable. Beyond exchange rate forecasting, our study underscores the broader significance of incorporating sentiment and narratives into economic models. By acknowledging the role of subjective beliefs, researchers and policymakers can enhance predictive accuracy and improve decision-making processes in financial markets.