Monetary & Financial Economics

Abstract This paper examines central bank interventions in the context of government debt sustainability, focusing on the inflation targeting model. Using quarterly data from 1989 to 2023 for Iran and countries with inflation targeting policies, this study examined the impact of central bank interventions on government debt sustainability in the context of inflation targeting using the Markov regime shift and policy evaluation (PSM) approach. The results of the Markov model showed that monetary interventions, such as increasing liquidity, reduce debt sustainability (debt-to-GDP ratio) and weaken the relationship between government spending and revenues. Gross domestic product had a positive effect (0.61 in the high-volatility regime and 0.98 in the low-volatility regime), and central bank independence (0.96 and 0.54), exchange rate (0.48 and 0.54), oil revenues (0.43 and 0.66), and tax revenues (0.78 and 0.67) had a negative effect on debt. Liquidity (0.53 and 0.65) and inflation (0.67 and 0.54) had positive effects, with a stronger effect in the low-volatility regime. The transition probability matrix also showed that the high-volatility regime (persistence probability 0.98) was more stable than the low-volatility regime (0.91), with an average of 3 quarters of high volatility and 1 quarter of low volatility. The PSM method with the logistic model confirmed that variables such as GDP, inflation, and central bank independence affect the probability of intervention, but interventions reduce debt sustainability.

Abstract In this study, a Dynamic Stochastic General Equilibrium (DSGE) model is developed to investigate the effects of exchange rate shocks on optimal monetary policy and the stock market in Iran. The model captures key features of the Iranian economy and simulates interactions among households, firms, the government, and the central bank. It analyzes the effects of shocks to total factor productivity, exchange rates, government oil revenues, and current government expenditures. Simulation results indicate that exchange rate shocks significantly influence macroeconomic variables, generating fluctuations in stock market returns and adjustments in optimal monetary policy. Strengthening the weight of inflation targeting in the monetary policy rule mitigates the negative impact of exchange rate shocks on output and stock market performance, although it imposes additional pressure on interest rates, potentially affecting capital market dynamics. The findings reveal complex and multidimensional relationships between exchange rates, monetary policy, and the stock market, highlighting the importance of carefully coordinated policy design for economic stability. This study provides both quantitative and qualitative insights into Iran’s economic mechanisms and offers guidance for formulating optimal policies under exchange rate volatility.

Abstract This study examines the dynamics of equity exchange-traded funds (ETFs) returns in the Tehran Stock Exchange in response to shocks arising from economic uncertainty. To identify uncertainty shocks, the proxy SVAR approach is employed within the framework of Koley and Hansen (1989). Gold is used as a proxy for uncertainty shocks, relying on unexpected international events during the period 2013–2023 (1392–1402 in the Iranian calendar), with monthly data. Gold price fluctuations around these events are extracted and introduced into the model as a structural identification instrument. Within this framework, the impact of uncertainty shocks on ETF returns is analyzed alongside other macroeconomic variables, including the exchange rate, inflation, interbank interest rate, risk aversion, and investor sentiment.

The results indicate that uncertainty shocks lead to a significant and substantial decline in ETF returns, with the strongest impact occurring within a one- to three-month horizon. This decline is primarily driven by heightened risk aversion and reduced positive investor sentiment. The effects typically persist for up to six months, after which returns revert to their initial levels. These findings are consistent with international evidence and address the gap in domestic research regarding the behavioral response of ETFs to macroeconomic uncertainties. Accordingly, adopting policies aimed at enhancing information transparency, developing risk-hedging instruments, and strengthening investor confidence is essential to mitigate the adverse effects of uncertainty shocks on the capital market.

Abstract Precious metals serve as safe-haven assets during economic crises, playing a pivotal role in the reallocation of capital from stock markets. Influenced by factors such as inflation, economic uncertainty, monetary policies, and geopolitical events, these metals exert either positive or negative effects on stock indices. This study aims to investigate the impact of gold and silver price volatility on the stock indices of selected Islamic countries over the period from 2006 to 2022. Employing a quantitative methodology grounded in econometric models, the research adopts a hybrid approach integrating quantile regression with wavelet transforms to analyze the distributional properties of variables across quantiles and temporal scales (short-term, medium-term, and long-term).
The findings reveal that fluctuations in gold and silver prices have a direct and statistically significant influence on the stock indices of the selected countries. Wavelet coherence analysis indicates an overall in-phase relationship among the variables: in the short term, stock indices exhibit leading behavior; in the medium term, gold and silver take the lead; and in the long term, correlations diminish. Quantile regression results highlight variations across wavelet scales, with D1 showing stronger effects in higher quantiles, D2 demonstrating greater stability, and D3 exhibiting weaker impacts. A similar pattern, albeit with reduced intensity, is observed for silver. Consequently, these volatility dynamics aid policymakers in formulating fiscal policies and assist investors in risk management. Recommendations include multi-scale analyses for forecasting, mitigating dependencies on price fluctuations, and developing novel financial instruments. This research addresses a gap in the literature on Islamic countries and introduces methodological innovations

Abstract Bank profitability is one of the key indicators of the soundness and resilience of the banking system, playing an important role in capital strengthening, shock absorption, and the continuity of financing for the economy. This study examines the non-linear effect of liquidity risk on the profitability of Iranian commercial banks. To this end, panel data for 12 banks listed on the Tehran Stock Exchange over the period 1382–1401 (2003–2022) are compiled, and return on assets (ROA) is used as the profitability measure. Liquidity risk is proxied by the ratio of total loans to total assets. In addition, credit risk, first differences of income diversification, the ratio of administrative expenses to assets, and bank size are included as control variables. After confirming cross-sectional dependence and applying the second-generation CIPS unit root test, the Panel Smooth Transition Regression (PSTR) model is employed to test for non-linearity. Linearity tests indicate that a simple linear specification cannot capture the behavior of liquidity risk, while a PSTR model with two thresholds and three regimes based on bank size provides a better fit. The results show that the effect of liquidity risk on profitability is negative and statistically significant for small and large banks, but positive for medium-sized banks; in other words, there exists an intermediate level of liquidity risk at which a more active use of liquidity capacity translates into higher profitability. Credit risk reduces ROA across all regimes, whereas changes in income diversification play a limited, mainly control role. Overall, the findings imply that prudential policies and liquidity risk management should be tailored to bank size, alongside simultaneous control of credit risk, to sustain bank profitability in Iran.

Abstract The aim of this study is to study the effects of oil price shocks on exchange rates and economic growth using a vector autoregressive model with time-varying coefficients. One of the most important debates and challenges in macroeconomics is the effects of oil price fluctuations on macroeconomic variables. Since oil price fluctuations affect both the demand side of the total economy and the supply side of the economy, studying its effects on the level of production and prices is very important and can provide appropriate policy recommendations for managing the demand side of the country's economy. In this regard, an important and noteworthy point is that the manner and extent of the impact of oil price fluctuations on economic activities depends on the initial conditions of the country's economy and, therefore, can have different effects. In this study, the TVP-VAR model method was used in the period 1972-2022 based on the frequency of seasonal data to analyze the results. The results of this study indicated that the shock from the oil price increase led to an increase in economic growth and the impact coefficient in this model varied over time and led to a fluctuating response of this variable over time. On the other hand, it was observed that the shock related to the oil price increase led to a decrease in the exchange rate and the estimated coefficient in this model was also in a way that expressed the variability of the response between the variables.

Abstract Every organization needs a performance evaluation system to monitor its progress towards achieving its goals. The absence of such a system implies a lack of communication with the internal and external environment, which could lead to the organization's decline and eventual collapse. This study presents a performance evaluation method for banks, aimed at assessing their status and analyzing the relationships among performance evaluation components. First, performance evaluation criteria were identified using content analysis, and data from nine Iranian banks, including 14 performance ratios, were evaluated. After normalizing the data, fuzzy cognitive maps and particle swarm optimization algorithms were used to determine the weights of the performance indicators. The performance indicators were then estimated. Finally, the entropy weighting method was employed to calculate the performance score for each bank. The results showed that Pasargad and Mellat banks had the best performance during this period, while Dey and Eghtesad Novin banks demonstrated the weakest performance. The research also analyzed the interrelations among various indicators and identified the influence and susceptibility of each indicator in evaluating bank performance.

Abstract 1- INTRODUCTION
Recent financial scandals have undermined investor confidence in corporate financial statements, making the efficiency of investment and the qualitative attributes of earnings increasingly critical for financial decision-making. In economic and commercial environments, investment is a key tool to prevent stagnation and promote sustainable growth. Managers can generate maximum returns for shareholders by optimally allocating resources and exploiting profitable opportunities. However, resource constraints and information asymmetry highlight the importance of investment efficiency, which is achieved when overinvestment is prevented, resources are directed to projects with positive net present value, and shareholder wealth is enhanced. In this context, understanding earnings quality and its impact on investment efficiency is essential, as it can guide more effective and sustainable decision-making in capital markets.

2- THEORETICAL FRAMEWORK
According to agency theory, managers may engage in projects with negative net present value for personal gain, leading to overinvestment. Financial reporting quality and earnings quality elements - including earnings persistence, transparency, conservatism, and smoothing - can limit opportunistic behaviors and enhance access to financial resources. Companies with stable, regular, and predictable earnings generally demonstrate higher earnings quality, allowing investors and managers to make better-informed decisions. Earnings persistence helps forecast future cash flows and profits, transparency increases investor confidence and reduces inefficient investment, and conservatism mitigates the risks of overinvestment. Conversely, earnings smoothing and opacity can negatively affect investment efficiency. Empirical studies across various countries and industries show that a combination of persistence, transparency, and conservatism enhances investment efficiency and reduces financial risk. The present study innovatively examines, for the first time in Iran, the comprehensive impact of earnings quality attributes on investment efficiency in companies listed on the Tehran Stock Exchange.


3- METHODOLOGY
This study examines 120 companies listed on the Tehran Stock Exchange over the period 2013-2023. Hypotheses were tested using multiple regression models applied to panel data. The dependent variable is investment efficiency, while the independent variables are earnings quality attributes, including earnings smoothing, persistence, transparency, and accounting conservatism. Descriptive statistics and Pearson correlation coefficients were used to examine relationships between variables. This approach enables a detailed analysis of the effects of each earnings quality element on investment efficiency and the comparison of their relative significance.

4- RESULTS & DISCUSSION
The findings indicate that enhancing earnings quality significantly improves investment efficiency. Earnings persistence and conservatism have a positive and significant effect, ensuring that financial resources are allocated to projects with positive net present value and promoting optimal investment. Earnings transparency similarly enhances investment efficiency by reducing uncertainty and increasing investor confidence. In contrast, earnings smoothing has a negative and statistically non-significant effect, while earnings opacity has a negative and significant effect, reducing the effectiveness of capital allocation and increasing inefficient investment. Overall, companies that improve earnings quality allocate resources more efficiently, and earnings quality serves as a critical indicator for evaluating corporate investment performance. These results underscore the importance of qualitative earnings attributes in guiding investment decisions and achieving sustainable financial outcomes.

5- CONCLUSIONS & SUGGESTIONS
The results suggest that improving earnings quality - particularly by enhancing persistence, transparency, conservatism, and managing smoothing appropriately - can significantly optimize resource allocation, increase investment efficiency, and support sustainable corporate growth. Managers are encouraged to focus on these qualitative aspects, while investors should consider these factors in their decision-making. Regulatory authorities are recommended to strengthen standards for transparency and conservative accounting practices, thereby promoting efficient investment. Additionally, the Tehran Stock Exchange can facilitate informed investment decisions by regularly providing relevant earnings information. Future research could investigate the effects of earnings quality on investment efficiency across different industries, its interaction with corporate governance, its impact on firm valuation, tax planning, and corporate social responsibility.

Abstract The aim of this study is to conduct a comprehensive and in-depth analysis of the regime dependence between the oil market and financial markets (including gold prices and stock market returns) with an emphasis on the key role of uncertainty arising from high and low oil price fluctuations. This research is conducted using monthly statistical data from 1380 to 1403 and the advanced approach of smooth transition vector autoregression is used to model the dynamic behavior of variables in different economic regimes. The results of this study show that oil prices, as a key transition variable, exert different effects on financial markets in high and low volatility regimes, which highlights the importance of examining the regime-based approach in financial analysis. The model estimation results for stock market returns indicate that macroeconomic variables such as GDP, exchange rate, oil price, liquidity and inflation rate have positive and statistically significant effects (at the 5% error level) on the stock market, while gold price shows a negative and significant effect. These effects are significantly stronger in the nonlinear part, which is consistent with the reduction of the variance of the variables in this regime and reflects economic stability. These findings indicate a greater sensitivity of the stock market to macroeconomic factors in stable conditions, which can be useful for predicting and managing investment risk. In the case of gold price, the model confirms that macroeconomic variables such as GDP, exchange rate, oil price, liquidity and inflation rate have positive and significant effects, while capital market returns exert a negative and significant effect on gold price. These effects are also more prominent in the nonlinear part and are consistent with the lower variance in this regime, indicating the role of gold as a safe haven asset in stable conditions.

Abstract The aim of this study is to investigate the net spillover effects of oil price and exchange rate on economic growth using a quantile vector autoregression model with time-varying coefficients. In order to analyze the results, the TVP-QVAR model method was used in the period 2000-2022 based on the frequency of monthly data. The main factor in the formation of fluctuations in economic growth is the exchange rate and oil price, especially if oil price fluctuations are long-term, the transmission of fluctuations and the net effect of oil price on the variable itself and economic growth will increase. If short-term fluctuations in oil prices continue and lead to fluctuations in the exchange rate and economic growth, in the medium term, fluctuations in economic growth will create the basis for the transmission of fluctuations to the oil price, and with increasing fluctuations in oil prices, the exchange rate will be highly volatile in the long term. Therefore, controlling oil price fluctuations in the short term will prevent the increase in exchange rate fluctuations and economic growth, and if policymakers do not consider this, in the medium term, the oil price will again fluctuate through the economic growth channel, and subsequently, fluctuations will be transmitted to the exchange rate with greater intensity in the long term. Considering the results obtained, it is suggested to economic policymakers that the degree of transmission and reception of effects in different time periods is proportional to different economic conditions, so all the aforementioned dimensions should be considered when making decisions about the exchange rate system and setting goals for economic growth.

Abstract This research examines how to improve supervisory effectiveness through AI functions in Iranian banking institutions using MAXQDA software. With the increasing complexity of banking operations and regulatory requirements, AI has emerged as an essential transformative technology that has great potential to enhance supervisory functions along with providing services and security measures. The research findings show that AI technologies including machine learning and natural language processing are able to accurately detect anomalies and fraud by monitoring large sets of financial data in real-time operations. This technology provides effective results through intelligent systems in three important areas including anti-money laundering (AML), financial advisory services, and improving customer experience with intelligent chatbots. Through these technologies, banks achieve higher operational efficiency, reduce human errors, and accelerate response times, which leads to increased customer trust and security. However, implementing AI in banking operations requires solving three fundamental challenges: transparency of system performance, data protection measures, and the development of appropriate legal frameworks to ensure privacy and security. This study, using qualitative methods and analyzing data collected in the software environment, examines the key variables affecting the supervisory performance of banks and provides operational solutions for the optimal and beneficial use of AI. The results of this study indicate that integrating AI into the Iranian banking system can lead to improved financial supervision, reduced risks, customer satisfaction, and the realization of smart and resilient banking.

Abstract The significance of fintech’s presence in the economy, particularly in the financial sector, is no longer a secret, and is underscored by the proliferation of recent studies examining the interplay between the performance of fintechs and macroeconomics on a global scale. According to international experiences, fintechs have significantly contributed to bolstering economic growth and controlling inflation by increasing access to financial services. Iran is host to over fifty operational fintech companies that have penetrated diverse facets of the business models employed by banks. Their presence may thus have an effect on the efficacy of the macro economy. A primary focus of economic policymakers in recent years has been the regulation of inflation. The fundamental inquiry is that how fintechs’ presence and emergence affect the inflation. Furthermore, what are the immediate and delayed consequences of fintechs’ existence on inflation? With the aid of time series data spanning 1991 to 2022 and the ARDL model, this paper examines the short- and long-term effects of fintechs on inflation. It categorizes fintechs into two distinct groups: those that engage in competition with banks and those that do not. By separating fintechs into competing and non-competing categories, the impact of each on inflation can be evaluated independently. The research findings indicate that the impact of different types of fintechs (competitor and non-competitor) on inflation differs, as does this impact during economic expansion and recession.

Abstract This study aims to investigate the impact of electronic banking infrastructure and performance on the cost of funds in 57 branches of Tejarat Bank in East Azerbaijan Province. For this purpose, monthly data for the year 2023 (1402 in the Iranian calendar) were analyzed using dynamic panel regression and the Generalized Method of Moments (GMM).
The results indicate that the expansion of electronic banking alone does not necessarily guarantee a reduction in the cost of funds, and its effect depends on the type of tools used and the quality of their utilization. Increasing the number of ATMs, as a key component of electronic banking infrastructure, has a significant negative effect on the cost of funds. However, an increase in the volume and especially the average amount of ATM transactions—mostly cash withdrawals—can lead to a higher cost of funds.
Moreover, merely increasing the number of registered mobile banking users and expanding point-of-sale (POS) terminals has a neutral or even increasing effect on the cost of funds. This is mainly because a portion of mobile banking accounts remain inactive, and POS terminals generate relatively high settlement and maintenance costs without attracting stable and low-cost deposits. In terms of performance indicators, the only electronic banking tool that shows a significant and stable cost-reducing effect—both in the number and the average amount of transactions—is active mobile banking. Active users tend to maintain more stable balances in current and Qard-al-Hasanah accounts and help reduce branch operational costs.
Overall, the ratio of current accounts to total deposits and the loans-to-resources ratio are among the most important factors in reducing the cost of funds.

Abstract This study investigates the impact of public debt and foreign direct investment (FDI) on the economic growth of four selected Islamic countries (Iran, Turkey, Malaysia, Egypt) during the period 1960-2023. To achieve the research objectives, the panel autoregressive distributed lag (Panel ARDL) econometric method was employed to simultaneously analyze the short-term and long-term relationships among the variables. In the empirical model, in addition to the key research variables (public debt and FDI), the role of control variables including national savings, inflation rate, real exchange rate, human capital, political stability, and the degree of trade openness was also considered. Findings from the model estimation indicate that in the long run, both public debt and inward FDI have a positive and significant effect on the economic growth of the studied countries. Furthermore, the results show a positive and significant effect of inflation and domestic savings, and a negative effect of the degree of trade openness on economic growth. In the short term, the error correction coefficient was negative and significant, indicating the stability of the model and its convergence towards long-term equilibrium. Based on these findings, efficient and productive management of public debt, improving the business environment and macroeconomic stability to effectively attract foreign investment, along with policies supporting national savings and managed trade openness, are presented as the main policy recommendations of this research.

Abstract The aim of this study is to investigate the role of the US withdrawal from the JCPOA on the spillovers of the foreign exchange market, stock exchange and cryptocurrency in Iran during the period from 16/11/1390 to 20/10/1401. For this purpose, the dynamic and advanced TVP-VAR model was used to examine the pairwise and triple spillovers between the three markets. The results show that the total correlation between the three markets increased by 0.58 percent after the US withdrawal from the JCPOA. Also, a net analysis of each market's spillovers showed that Bitcoin spillovers decreased by 123%, while the stock index and dollar spillovers increased by 117% and 58%, respectively. The innovation of this research is in using the TVP-VAR model to analyze long-term and pairwise-triple spillovers between three important Iranian markets, focusing on specific economic and political conditions before and after the US withdrawal from the JCPOA, and simultaneously analyzing the role of cryptocurrencies alongside the stock market and foreign exchange, which has received less attention in Iranian financial literature.

Abstract Predicting Stock Price Movement Using Subjective Cash Flow and Discount Rate Expectations Modeling in Iran's Sock Market

Abstract
One of the ambiguations in assets pricing which has been ignored in assets pricing literature is this question that stock price movement is based on revision in expected cash flows or revision in discount rates? Therefore, in this research stock price movement in Iran’s stock market was studied for the first time using subjective cash flow and discount rate expectations modeling. For this purpose, required data was gathered form Tehran stock exchange, RahAvard Novin data bank and analysists consensus system during 2011-2023. Also, firstly subjective expectations of cash flow growth (SCF) and subjective expectations of discount rate (SDR) were predicted using Vector Auto-Regressive (VAR), Implied Cost of Capital (ICC) and Revisions in Analysts' Forecasts (RAF) models. Then, the performance of mentioned models was evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). Results showed that ICC model is more efficient from VAR model and RAF model is more efficient that both VAR and ICC models in predicting SCF and SDR. Also, against the expectations, the covariance between P/E and SCF is negative and covariance between P/E and SDR is positive which indicate the inefficient algorithm in stock pricing in Iran’s stock market due to factors such as high inflation, economic sanctions and investors’ behavioral biases.
Keywords: subjective expectations of cash flow growth, VAR model, ICC model, RAF model.

Abstract The need to design new securities to deepen and diversify the country's financial markets due to the lack of use of bonds has led to the discussion of sukuk being pursued more seriously in the country. Sukuk is a type of security that is designed based on accepted Islamic contracts. One of the fourteen types of sukuk is the Istisna'a sukuk or made-to-order securities, which has recently been approved for use in the country's financial markets by the country's Securities and Exchange Organization. This article seeks to examine the operational models of these securities for use in the country's financial markets. The results of the study show that the models of direct Istisna, indirect Istisna, and Istisna and lease with possession have been previously discussed in other articles and have been approved by the Securities and Exchange Organization for use in the country's financial markets. However, the two models of Istisna and ordinary Istisna and lease and Istisna and lease with option to sell are considered new models of build-to-order bonds.

Abstract The increase in non-performing loans and the growing complexity of the economic environment have posed serious challenges for banks in reducing credit risk. In this context, Bank Melli Iran, as the largest state-owned bank in the country, is more exposed than other banks to the consequences of credit risk due to the breadth of its operations and its strategic role in the monetary system. The purpose of this study is to propose a comprehensive model for identifying and explaining strategies to reduce credit risk in Bank Melli Iran.

This research is applied in terms of purpose and adopts a mixed-methods (qualitative–quantitative) approach. In the qualitative phase, effective components and strategies for credit risk reduction were identified and refined using qualitative content analysis and the fuzzy Delphi method. Subsequently, in the quantitative phase, interpretive structural modeling (ISM) was employed to level and structure the relationships among the components, and finally, the proposed model was tested and validated using structural equation modeling (SEM).

The findings indicate that economic factors, governance-related factors, and credit and managerial policies play the most significant causal role in reducing credit risk, while other factors such as human resources, process management, and information systems are positioned at the more influenced levels of the model. Overall, the results suggest that focusing on effective credit policies and the efficient management of economic variables plays a key role in improving asset quality and controlling credit risk in Bank Melli Iran.

Abstract Although various studies have examined the economic effects of money laundering, there is a lack of studies, especially in the country, on the impact of shocks resulting from economic sanctions on money laundering. Therefore, in this study, the impact of negative shocks of economic sanctions on the volume of money laundering in Iran was investigated using a structural vector autoregression (SVAR) model during the period 1994-1999.
The results showed that shocks of -5, -10 and -20 percent in crude oil export revenue increase the volume of money laundering in Iran by a maximum of 3.66, 4.67 and 7.63 percent, respectively. Also, shocks of -5, -10 and -20 percent in non-oil exports increase the volume of money laundering in Iran by a maximum of 2.24, 3.26 and 6.24 percent, respectively. In addition, shocks of -5, -10 and -20 percent in capital and intermediate goods increase the volume of money laundering in Iran by a maximum of 1.11, 2.12 and 5.22 percent, respectively. On the other hand, shocks of +5, +10 and +20 percent in exchange rate increase the volume of money laundering in Iran by a maximum of 3.01, 5.72 and 12.97 percent, respectively. Due to economic sanctions, economic growth, decline, exchange rate fluctuations, increases, inflation rates, increases and income inequality also increases, money laundering grows as a natural reaction of the informal market.
Therefore, solutions must be simultaneously economic (controlling exchange rates and inflation), social (reducing income inequality), institutional (regulations and supervision), and technological (artificial intelligence and blockchain). Only with this combination can the volume of money laundering be reduced and public trust and sound investment be strengthened.

Abstract This study aims to investigate the impact of CAMELS indicators on bank non-performing loans (NPLs). The present research is applied in terms of purpose and correlational-causal (ex-post facto) in terms of methodology. The statistical population includes banks listed on the Tehran Stock Exchange, from which 10 banks were selected as the sample. The data were collected and analyzed over a 10-year period (2014-2023). The CAMELS index comprises six components: Capital Adequacy, Asset Quality, Management Quality, Earnings, Liquidity, and Sensitivity to Market Risk. The results showed that Asset Quality, Earnings Status, and Liquidity Quality have a significant and inverse impact on NPLs. However, Capital Adequacy, Management Quality, and Sensitivity to Market Risk do not have a significant impact on NPLs. This study, by analyzing the CAMELS components separately and using up-to-date data, can assist policymakers and bank managers in identifying factors affecting NPLs, thereby enhancing the financial health of banks and preserving stakeholder confidence.

Abstract Objective: The purpose of this study is to investigate the effect of financial constraint on the speed of adjustment of business credit by considering the role of social responsibility of economic firms.
Methodology: This study employs an applied approach with a focus on causal and ex-post facto correlation. It examines the relationships among financial constraints, social responsibility, and trade credit adjustment speed. The statistical population comprises companies listed on the Tehran Stock Exchange, with data spanning the period from 2011 to 2023. Following a systematic elimination process for final sample selection, data from 146 companies were utilized for analysis. The hypotheses were tested using EViews 12 and Stata 14 software, employing the Generalized Method of Moments (GMM) approach.
Findings: Statistical results indicate that financial constraints have a direct and significant impact on trade credit adjustment speed. Specifically, when financial constraints increase, managers tend to rely more on credit financing, thereby accelerating the trade credit adjustment process. Furthermore, social responsibility influences this relationship; the interaction between financial constraints and social responsibility leads to an increase in trade credit adjustment speed.
Conclusion: Social responsibility is not merely an isolated ethical or strategic dimension; rather, it acts as a powerful moderating factor, influencing how financial constraints affect the speed of trade credit adjustment. The key finding is that companies with high levels of social responsibility are capable of overcoming, to some extent, the challenges posed by financial constraints, and can even maintain or enhance their trade credit adjustment speed.

Abstract This study aims to conduct an institutional mapping of the crowdfunding system in Iran by identifying key actors, mapping the network of institutional relations, evaluating the main functions of the system, and discovering its structural gaps. The research adopts a sequential exploratory mixed-methods design. In the qualitative phase, thematic analysis was applied to the responses of 14 key experts—including managers of crowdfunding platforms, investors, fundraisers, bank managers, and Farabourse experts—collected through semi-structured questionnaires administered in two stages. In the quantitative phase, the system’s functions were scored using a 5-point Likert scale, and institutional mapping matrices (actor–function, roles, and relations) were developed. Official documents and statistical reports on the Iranian crowdfunding market up to the end of 1404 were also used as secondary data. The results indicate that the Iranian crowdfunding ecosystem has a core-periphery structure, with three key actors—crowdfunding platforms, Farabourse Iran, and banks—holding the greatest power and influence. The system performs relatively well in connecting retail and professional investors with founders (mean score: 4.5 out of 5) and financing projects (mean score: 4.2), but shows serious weaknesses in risk management and sharing (mean score: 2.5) and in supporting innovation and entrepreneurship (mean score: 2.0). Five major institutional challenges and gaps were identified, the most important of which are the absence of a secondary market, the high cost of bank guarantees, weak post-financing monitoring, excessive focus on working capital rather than innovative projects, and the absence of key actors such as professional associations and universities. A comparison with advanced global ecosystems revealed that Iran is still in the early stages of developing this market.

Abstract این تحقیق به بررسی نقش شکست‌های بازار مالی در تضعیف اثربخشی سیاست پولی و کارایی هدف‌گذاری تورم در اقتصاد ایران می‌پردازد. در اقتصادهای در حال‌توسعه‌ای مانند ایران، توسعه مالی ناقص و وجود شکست‌هایی مانند بازارهای مالی کم‌عمق، سلطه مالی دولت، بخش غیررسمی گسترده و ناکارایی نظام بانکی، کانال‌های انتقال سیاست پولی (مانند کانال نرخ بهره و اعتبار) را مختل می‌کند. در نتیجه، سیاست‌های پولی انقباضی ممکن است نتوانند تورم را به‌طور مؤثر کنترل کنند و حتی گاه با انحراف منابع به سمت بخش‌های غیرمولد، خود به تداوم تورم دامن زنند. با وجود تلاش‌های بانک مرکزی برای هدف‌گذاری تورم، نرخ تورم در ایران به‌دلیل این شکست‌های ساختاری در بازار مالی، مزمن و نوسانی باقی مانده است. شواهد تجربی در ایران (مانند نرخ سود حقیقی منفی و انبساط نامؤثر نقدینگی) نشان می‌دهد که بازار مالی در ایران نه تنها به عنوان یک کانال تسهیل‌کننده، بلکه به عنوان یک مانع در برابر سیاست‌های پولی عمل می‌کند. این پژوهش با روش تحلیلی-تجربی و با استفاده از مدل تعادل عمومی تصادفی پویا (DSGE) برای داده‌های فصلی دوره 1403-1370 به تحلیل این تعاملات می‌پردازد. پارامترهای مدل با روش بیزین برآورد خواهند شد. هدف اصلی، تحلیل ارتباط بین توسعه مالی و هدف‌گذاری تورم با درنظرگرفتن نقش شکست‌های بازار مالی است. نتایج نشان می‌دهد که توسعه مالی نقش محوری و تعیین‌کننده در تقویت کانال‌های انتقال سیاست پولی ایفا می‌کند. در چارچوب رژیم هدفگذاری تورم، شوک مثبت به شاخص توسعه مالی نه تنها متغیرهای واقعی اقتصاد را به شکل معنادار و پایداری افزایش می‌دهد، بلکه با فعال‌سازی مکانیسم شتاب‌دهنده مالی، تخصیص منابع را کارآمدتر کرده و بهره‌وری سرمایه‌گذاری را ارتقا می‌بخشد.

Abstract Exports play a significant role in the development of third world countries due to increased employment and income. In addition, exports can create huge opportunities such as access to innovation and advanced technologies and the mobility of capital goods. For this reason, it is essential for governments and economic policymakers to identify the factors affecting exports. The aim of the present study is to examine the effect of monetary policies on the volume of non-oil exports of Iran during the period 1990-2024. For this purpose, the fuzzy regression method was used. The findings of this study showed that money supply policies with a fuzzy coefficient (-0.0417, 0.0019) have a negative effect on the volume of non-oil exports of Iran. This coefficient confirms the point that the increase in the volume of money in the Iranian economy has led to higher inflation and increased production costs and, consequently, reduced the competitiveness of exported goods. Other findings also showed that the increase in the average per capita income level of other countries in the world and the increase in the real effective exchange rate have a positive effect, and on the other hand, the inflation rate and economic sanctions against Iran have a negative effect on the volume of non-oil exports of Iran.

Abstract The main objective of this study is to examine the relationship between abnormal accruals,
earnings smoothness, earnings persistence, and earnings predictability as measures of earnings
quality and the financial sustainability of companies operating in the basic metals industry. In
this research, panel data and multiple regression analysis were employed using EViews 13
software to test the study’s hypotheses. In addition, the study incorporates a comprehensive
quantitative approach to ensure the robustness and reliability of the obtained results. The data
analysis was conducted on 30 listed metal companies in the Iranian capital market over a 13-year
period from 2011 to 2023 (1390–1402), aiming to precisely investigate potential relationships
among the variables. Furthermore, special attention was given to the consistency of results across
different estimation techniques and model specifications.
The results of the analysis indicate a significant negative relationship between financial
sustainability and abnormal accruals. Moreover, there is a significant but negative relationship
between financial sustainability (as the dependent variable) and both earnings persistence and
earnings predictability. However, no significant relationship was found between financial
sustainability and earnings smoothness.