Monetary & Financial Economics

Abstract The necessity of need to reduce dependence on foreign currency income from oil and move towards providing a significant part of the required currency through non-oil exports is felt more. In this regard, paying attention to the mining sector due to its rich and underground reservoirs is a suitable solution to solve this problem. However, the further development of the mining sector in order to achieve economic goals is affected by economic sanctions. Undoubtedly, examining the manner and mechanism of impact of shocks caused by economic sanctions on the country's mining sector can help the officials and economic planners of the country to better face and reduce their negative economic consequences, including reduction in production and added value, reduction in employment, reduction in investment, etc., in the mining sector. Therefore, in this study, for the first time, the effects of shocks caused by economic sanctions (1. the shock of the increase in the exchange rate, 2. the shock of the decrease in the import of raw materials, capital and intermediate goods, 3. the shock of the decrease in crude oil export income and 4. The shock of non-oil export reduction) on foreign trade of Iran's mining sector. For this purpose, the required data was collected from the social accounting matrix and the Recursive Dynamic Computable Equilibrium (RDCGE) model was used to analyze the data. The results showed that among the shocks caused by economic sanctions, in order: the shock caused by the increase in the exchange rate, the shock caused by the decrease in the import of raw materials, capital and intermediate goods, the shock caused by the decrease in crude oil export income and the shock caused by the reduction of non-oil exports have the greatest impact on trade balance of Iran's mining sector.

Abstract The link between stock returns and macroeconomic indicators has been a debatable phenomenon at all times. industry index reflects performance of stock market and state of industry and production of each country. Potential macroeconomic factors may have significant long-term effects on industry index. Therefore, this paper empirically examines dynamics of industrial production and inflation on industry index and total index of Iranian stock market using ARDL model with monthly data during period from 2015 to 2023. empirical results showed that logarithm of consumer price index has a positive and significant long-term effect on industry index and total index. In addition, results indicate a positive and significant long-term relationship between industrial production and industry index. In addition, there is a positive and significant relationship between industrial production and total market index. The results of this study highlight complexity of stock market dynamics influenced by inflation and industrial production. findings of this study can be valuable for investors and policymakers in identifying the response of Iranian stock market to production and inflation.

Abstract Given the banking system's position in the country's economic system and banks' significant contribution to attracting financial resources, we expect the banking system's performance to significantly impact the economy. If the attraction of financial resources by banks leads to the allocation of resources to strengthen industry and production, it can contribute to economic prosperity. Therefore, the purpose of this study is to validate the policy-making model of transparency in the allocation of banking resources in Iranian knowledge-based companies. The research strategy is survey, and the research method is quantitative. The statistical population of the research was 380 managers, vice presidents, and all officials of knowledge-based companies located in the Mashhad Science and Technology Park, of which 190 were selected as a statistical sample using a stratified random method. The research tool includes a researcher-made questionnaire with 130 items. The partial least squares approach was used to analyze the data using PLS software. The research findings in the model in question include 6 categories in 15 components: economic, political, and legal independence; effectiveness in information quality and effectiveness in information access; dynamic monitoring; use of basic and applied systems; transparent allocation based on assets and investments; transparent allocation of resources based on alternative debt; prosperity of the knowledge-based economy; employment development; supportive policies; knowledge-based economy; challenges in implementing policymaking; and challenges of knowledge-based companies. The data results showed that the aforementioned model has the necessary validity. The findings of this study emphasize that identifying factors that enhance transparency in the allocation of Iranian banking resources can be useful in the process of improving the position of knowledge-based companies.

Abstract This paper examines the effects of exchange rate policies and liquidity creation on key macroeconomic variables in Iran. Using quarterly data from 1991 to 2023, we employ a Dynamic Stochastic General Equilibrium (DSGE) model to analyze the transmission mechanisms of these shocks. The Iranian economy has long grappled with the interdependence of exchange rate and monetary policies, presenting significant challenges for policymakers. A substantial portion of government budget expenditures is financed through oil export revenues and withdrawals from the Foreign Exchange Reserve Account. This dynamic compels the Central Bank to purchase foreign currency, consequently expanding the monetary base. Given this linkage, monetary authorities must carefully assess the impact of oil revenue fluctuations when formulating policies to ensure economic stability. Our study specifically investigates: The exchange rate policy channel (transmitted through exchange rate adjustments). The monetary policy channel (transmitted through liquidity creation). Key findings reveal that: Exchange rate shocks have a more pronounced inflationary effect compared to liquidity creation shocks. These shocks also lead to reductions in both consumption and income, underscoring their contractionary impact on the economy. These results highlight the critical role of exchange rate management in Iran’s macroeconomic stability and suggest that policymakers should prioritize mitigating exchange rate volatility to curb inflation and support economic growth.

Abstract The aim of the current research is to modeling of financial distress prediction using XGBoost ensemble learning algorithm in listed companies on Tehran Stock Exchange. The statistical population of the present research is the listed companies to the Tehran Stock Exchange during the period 2012 to 2023 and the screening sampling method, in this regard, 1800 firm-year (150 firms for 12 years), observations collected from the annual financial reports of the case have been tested. In this research, independent variables according to the theoretical foundations and empirical background of internal and external research include 52 variables in two categories of accounting variables (profitability indicators, obligations fulfillment indicators, activity indicators, cash flow indicators and growth sustainability indicators) and Non-accounting variables (cash flow composition indices, corporate governance indices, macroeconomic indices, management ability index, audit index and competitiveness index) were determined. In order to identify the important variables for developing the model, the average comparison test of two sample was used, and according to the results, 40 variables out of 52 variables were selected as the final variables for developing the model. The results show that the overall accuracy of XGBoost ensemble learning algorithm and logit regression is 97.8% and 92.1%, respectively, which indicates that compared to logit regression, XGBoost algorithm performs better in predicting companies with Has financial distress. In other words, the results show the efficiency of XGBoost algorithm compared to logit regression. Therefore, the XGBoost algorithm provides the most efficient model for predicting financial distress in listed companies on the Tehran Stock Exchange.

Abstract Empirical identification of the role of the life cycle in the fluctuation of stock returns according to economic conditions will improve the performance of investors and will improve the accuracy of company managers' forecasts. Based on this, in this research, the relationship between the stages of the company's life cycle and the fluctuation of stock returns according to the variables of macroeconomic conditions was investigated in the form of two hypotheses. The research is applied in terms of purpose and using the post-event approach, and based on the method, it is of the descriptive-correlation type. The statistical population of companies admitted to the Tehran Stock Exchange and the sample number was 157 companies during the years 1397 to 1401. To test the hypotheses, multivariate regressions were used in the Eviews13 software environment. The results of the research show that the volatility of stock returns in the maturity stage is less than the recession stage and in the growth stage it is more than the recession stage. If the exchange rate is taken into account, the volatility of stock returns in the stages of emergence, maturity, growth and decline is lower than in the recession stage. If the inflation rate is taken into account, the volatility of stock returns in the stages of emergence, maturity and growth is higher than in the stage of recession, and in the stage of decline it is not different from the stage of stagnation. If the bank interest rate is considered, the volatility of stock returns in the stages of emergence, maturity and growth is higher than in the stage of recession, and in the stage of decline, it is not different from the stage of recession. And finally, if the GDP is considered, the fluctuation of stock returns in the stages of emergence, maturity, growth and decline is not significantly different from the recession stage.

Abstract Aim and Introduction:
The precautionary demand for money is usually influenced by uncertain factors and changes in cash flows, and is dependent on assets with high liquidity. This study aims to identify the role of qarz al-hasanah (interest-free loans) in the precautionary demand for money using the Canian and Tarka model.
Methodology:
The specified equations were estimated using quarterly time series data from the years 1377 to 1402 using the ARDL method, and the coefficient obtained from the ECM estimation is used to identify long-term relationships.
Findings:
The results show that the expansion of the capital market has a greater impact on the allocation of money demand than on liquidity preferences, and gold strengthens the precautionary demand for money during periods of economic instability, especially when monetary policies face challenges in dealing with crises. Furthermore, inflation significantly affects adaptive behaviors and changes in money demand, and qarz al-hasanah plays a key role in enhancing liquidity and managing short-term financial gaps. Additionally, a long-term relationship exists between the qarz al-hasanah variable, as an independent variable, and the precautionary demand for money.
Discussion and Conclusion:
The precautionary demand for money is influenced by several factors, including capital markets, loans, inflation, and asset preferences. The expansion of the capital market has a greater impact on the allocation of money demand than on liquidity preferences. Gold, as an asset, strengthens the precautionary demand for money during periods of economic instability and challenges the effectiveness of monetary policy. Inflation affects adaptive behaviors, and currency also creates an immediate precautionary response in money demand. Qarz al-hasanah is confirmed in smoothing liquidity cycles and managing both short-term and long-term money demand, and i t effectively contributes to increasing liquidity and filling short-term financial gaps.

Abstract The Bitcoin market, characterized by its high volatility, consistently attracts the attention of investors and researchers. However, two primary challenges persist within this market: assessing its efficiency and identifying periods of price bubbles. These bubbles can lead to erroneous financial decisions. Despite extensive research, ambiguities surrounding non-random behavior and the formation of price bubbles in Bitcoin remain. This study investigates the efficiency of the Bitcoin market and identifies potential bubbles using the DS LPPLS (Discrete Singularity Log-Periodic Power Law Singularity) model, alongside confidence and sentiment indices. Daily closing prices of Bitcoin from the beginning of 2025 to the end of 2024 were utilized for this analysis. Through the examination of price charts and relevant indicators, it was determined that the Bitcoin market generally does not exhibit weak-form efficiency, and its behavior does not fully align with a purely random walk model. The confidence and sentiment indices, which reflect investor emotions towards the market, experienced significant fluctuations and showed a high correlation with price changes. Sudden and simultaneous increases in positive sentiment indicators and price suggest the formation of price bubbles during certain periods. The findings of this research indicate that the DS LPPLS model, in conjunction with confidence and sentiment indices, serves as a robust tool for analyzing Bitcoin market behavior and detecting price bubbles. Nevertheless, for more precise results, further in-depth investigation into factors influencing the market and the application of more complex models are warranted.

Abstract The main objective of this research is to validate and fit the comprehensive risk management development model. This research is applied in terms of purpose and descriptive-analytical in terms of data collection method based on the structural modeling approach. To understand the relationship between the structures of the extracted model from the context-based approach, a phenomenographic strategy was used. The data collection tool was a researcher-made questionnaire, and for this purpose, data were collected from 87 experts in the field of investment who were selected using the convenience sampling method. The research data were analyzed using Smart-PLS software. The results of the research indicate that the majority of the paths are confirmed, such that the path of causal conditions through the phases of risk management planning, risk identification, implementation of qualitative risk analysis, implementation of quantitative risk analysis, risk response planning, and risk control has a significant relationship with the category of comprehensive risk management. Also, the path of the underlying conditions has a significant relationship with the comprehensive risk management strategies, and finally, the path of the strategies has a significant relationship with the consequences of implementing the comprehensive risk management model.

Abstract The aim of the present study is to investigate the effect of investment in fixed assets on the speed of cash holding adjustment. The present study is applied and from the methodological perspective, the correlation is of a causal (post-event) type. The statistical population of the study is all companies listed on the Tehran Stock Exchange, and using the criterion-based screening sampling method, 144 companies were selected as the research sample and were studied over a 10-year period between 2014 and 2023. The results of the research hypothesis test showed that the speed of cash holding adjustment among the sample companies is about 77 percent. Companies cover the gap between actual and optimal cash holdings on average at the same speed annually. Also, investment in fixed assets affects the speed of cash holding adjustment and reduces the speed of adjustment. In fact, given the ability to collateralize fixed assets, companies with higher fixed asset investments move towards optimal cash holdings at a slower rate and do not need to hold as much cash. Based on the results, it can be stated that companies that allocate a higher share of their investment to fixed assets have a lower need to hold large amounts of cash to manage liquidity risk or potential investment opportunities due to the ability to use these assets as collateral for financing. As a result, these companies move towards optimal cash holdings at a slower rate, because fixed assets play an alternative or complementary role in managing financial risk and securing resources and reduce the immediate pressure to quickly adjust cash reserves.

Abstract The purpose of this study is to investigate the effect of comparability of financial information and earnings management and smoothing on the level of shareholder loyalty. The present study is applied and methodologically it is a causal (post-event) correlation.The statistical population of the study was all companies listed in the Tehran Stock Exchange and using the systematic elimination sampling method, 163 companies were selected as the research sample and were studied in a 10-year period between 2014 and 2023. Three levels of loyalty for investors‌(total, majority, and minority) were considered to measure the dependent variable. Three hypotheses were proposed for the present study, and the results showed that at the level of total shareholder loyalty, earnings management does not affect the loyalty‌ of the company's shareholders. Gaining shareholder loyalty has the opposite effect. Earnings smoothing does not affect the loyalty of the company's shareholders‌. Disclosure of high-comparability information has a direct effect on gaining the loyalty of the company's shareholders. Earnings management has an inverse effect on gaining the loyalty of the company'‌s shareholders.

Abstract Given the numerous challenges facing the Iranian economy, globalization and financial development are considered as two key factors in the country's economic growth process. This study examines the effects of globalization and financial development on Iran's economic growth. The main objective of this research is to create an analytical framework and model to calculate total factor productivity (TFP) and examine the effects of these two variables on Iran's gross domestic product (GDP). The data used were collected from 2000 to 2023 and include key variables such as physical capital, financial development, globalization index, government spending, and inflation rate. Advanced econometric methods such as the ARDL model and static and cointegration tests were used to analyze the data, which allows for a detailed examination of the long-term and short-term relationships between the variables. The results of the research show that globalization and financial development have a positive and significant effect on Iran's economic growth and can act as the main drivers in this process. In contrast, government spending and inflation rate have negative effects on economic growth, and these results indicate the need for optimal management of government spending and control of inflation rate to maintain economic stability. These findings can help policymakers and economic decision-makers in designing effective and efficient strategies and lead to improvement of the country's economic situation.

Abstract Abstract
Introduction: Selecting an appropriate computerized accounting information system (CAIS) is a pivotal strategic decision that directly influences financial transparency, decision quality, and operational efficiency in today’s digital business environment. Despite a growing array of CAIS solutions, Iranian firms lack a systematic, evidence based framework to guide their choice under rapidly evolving technology and regulatory landscapes.
Theoretical Framework: Drawing on DeLone & McLean’s Information Systems Success Model and Davis’s Technology Acceptance Model (TAM), this study conceptualizes CAIS selection as a multi criteria decision problem, encompassing two broad dimensions—software capabilities and vendor attributes. Prior research highlights the critical roles of technological infrastructure, service quality, and user support in driving successful system adoption.
Methodology: A purposive snowball sample of ten CAIS experts (each with ≥10 years’ relevant experience) was assembled. We constructed a three level AHP hierarchy containing nine criteria and 35 subcriteria identified from the literature. Pairwise comparisons were conducted via Expert Choice 11, ensuring all consistency ratios (CR) remained below 0.10.
Results & Discussion:
• Top ranked criteria: Technology infrastructure (weight = 0.293), Maintenance & Upgrades (0.198), and Training & Documentation (0.175).
• Quantitative ranking model: Based on the final AHP weights, we developed a scoring model that systematically combines the nine criteria into a single CAIS suitability index, enabling organizations to numerically compare alternatives.
• Key technology subcriteria: Web based architecture & e commerce support (0.323), Flexibility for future needs (0.171), and Data interoperability (0.169). These findings underscore the necessity of cloud readiness, API integration, and seamless data exchange to enable advanced analytics and real time reporting.
• Service oriented dimensions: Technical support responsiveness (0.406) and comprehensive user documentation (0.462) highlight the indispensable roles of vendor support and end user empowerment in achieving rapid, risk mitigated CAIS deployment.
Conclusions & Suggestions:
Organizations should prioritize CAIS solutions with robust web centric and cloud capabilities, backed by formalized upgrade and support agreements. Continuous, blended training programs (self learning modules, user guides, and instructor led workshops) are essential to maximize user adoption and minimize operational errors. Future research is encouraged to:
1. Undertake large scale, non Delphi survey studies across diverse industries to validate and generalize AHP derived weightings;
2. Employ Fuzzy AHP or integrate AHP with DEA to capture environmental uncertainty and criterion interdependencies;

Abstract The aim of this article was to investigate the effect of economic policy uncertainty on the performance of the insurance industry in Iran. For this purpose, data from the period 1370-1402 were used based on the frequency of annual data and the autocorrelation method with nonlinear distribution lags. The results of this study indicated a significant and asymmetric effect of economic policy uncertainty on the performance of the insurance industry in the two sectors of life and non-life insurance. Based on data analysis, positive and negative uncertainty shocks have different effects on premiums received. In the life insurance sector, the impact coefficients of positive shocks are reported to be 0.22 and negative shocks are -0.15, and in the non-life insurance sector, they are 0.28 and -0.19. These results indicate that increased uncertainty in economic policies leads to growth in demand and, consequently, an increase in premiums received, while a decrease in uncertainty is associated with a decrease in insurance demand. This asymmetric relationship means that the intensity of the impact of positive shocks (increased uncertainty) on premiums received is greater than that of negative shocks (decreased uncertainty). This asymmetry is more pronounced in the non-life insurance sector than in the life insurance sector due to the coverage of more diverse and shorter-term risks and higher sensitivity to economic fluctuations.

Abstract This study investigates the impact of fiscal policy shocks on financial stability and household behavior within the framework of a small open economy. Given the structural fragility of Iran's economy in the face of fiscal volatility, the primary aim is to analyze the dynamic direct and indirect effects of increased taxation on output, consumption, exchange rates, inflation, and the budget deficit. For this purpose, a calibrated Dynamic Stochastic General Equilibrium (DSGE) model tailored to the Iranian economy is employed. Oil revenues and sanctions are incorporated into the model as exogenous shocks. The model, comprising 41 parameters, was simulated over 100 periods, and both impulse responses and impulse response functions were generated. The results indicate that monetary shocks lead to an increase in the interest rate and a decrease in inflation, whereas tax shocks increase consumption and reduce labor supply. Oil and sanction shocks have limited effects on consumption but exacerbate financial instability. A diagnostic test reveals negligible estimation error; however, issues related to data scaling are notable. It is recommended that empirical validation be conducted using Iranian data, alongside more precise modeling of exchange rate dynamics, to strengthen policy recommendations.

Abstract Development policies in Iran can be examined and analyzed from several important aspects, but what can have the most impact in this area are financial and economic issues, which have been proven to have an impact on development trends and related policies in many countries. Accordingly, the aim of this research is to identify and analyze the financial and economic drivers of Iran's development policy. The method of conducting this research is a qualitative futures study that was conducted with a trend analysis strategy and all financial and economic trends governing the development policy situation in Iran were studied by studying domestic and foreign reports, in-depth interviews, and brainstorming sessions. The population of experts and development policy activists in Iran, 25 of whom participated in the research process through interviews and participation in brainstorming sessions. Data analysis was based on trend analysis and was based on driver analysis. The research findings showed that the macro-trend of economic changes is driven by; Monitoring and measuring development, abolishing economic protectionism, foreign financing, financing through tourism, developing trade routes, the emergence of multinational corporations, decoupling the economy from the environment, supporting green economy projects and social returns, global convergence for development, changing labor markets, and good corporate governance have been identified. Then, through quantitative analysis of these drivers, it was determined that three drivers; abolishing economic protectionism with an average of (4.32), foreign financing with an average of (4.14), and the emergence of multinational corporations with an average of (4.06) are among the most important drivers for development policymaking, and the drivers of foreign financing with an average of (4.67), financing through tourism with an average of (4.35), and the emergence of multinational corporations with an average of (4.19) are the most influential drivers in development policymaking.

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 The shadow economy, defined as a set of economic activities that remain hidden from formal institutions, represents a fundamental and complex phenomenon in Iran's economy. The objective of this study is to assess the impact of economic policy uncertainty on the size of the shadow economy, with a specific focus on the moderating role of financial development. Utilizing data from 1978 to 2022 and employing the Autoregressive Distributed Lag (ARDL) model, this research was conducted. The obtained results indicate that economic policy uncertainty has a positive and significant effect on the shadow economy in the short run, but no direct impact is observed in the long run. However, financial development positively moderates this relationship, meaning it amplifies the effects of uncertainty. Furthermore, the positive relationship between GDP and the shadow economy and the inverse effect of interest rate uncertainty on it are unconventional phenomena that can be explained by the unique structure of Iran's economy. These findings emphasize the necessity for precise and targeted policymaking, taking into account the unique institutional and structural characteristics of the Iranian economy.

Abstract The interaction between the money and capital markets is a crucial topic within Iran's financial system. Despite numerous studies in financial markets, the pathology of this interaction remains largely unexplored. This research aims to identify and systematically analyze the impediments to the interaction between these two markets.Given the highly specialized nature of the research topic and the limited scholarly attention it has received, the study's population is restricted to experts and academics in the field. These individuals possess both accessibility and a deep understanding of the interaction between money and capital markets and its related pathologies in Iran.This research employs a mixed-methods approach. After a systematic literature review and interviews with eight experts, 75 impediments (25 from literature and 50 from interviews) were identified and coded using thematic analysis. Subsequently, these impediments were assessed using a fuzzy Delphi questionnaire distributed among 12 experts, leading to the confirmation of 65 impediments. These were categorized into five dimensions: policymaking, financial institutions, laws and regulations, financial instruments, and structural factors.Finally, an ISM (Interpretive Structural Modeling) questionnaire was distributed to 12 experts. Using the Group ISM method in MATLAB R2021a software, the internal relationships among these impediments were analyzed. The results revealed that policymaking is the most significant hindering factor to market interaction in Iran, followed by financial institutions, laws and regulations, financial instruments, and structural factors, in order of importance. Furthermore, policymaking and laws and regulations were identified as the most influential criteria, while financial institutions, financial instruments, and structural factors were deemed the most dependent criteria. This analysis was also performed at the component level within each dimension.

Abstract This study aims to investigate the effect of inflation uncertainty on the efficiency of financial institutions in Iran. To conduct this study, statistical data from the period 1980 to 2022 related to the Iranian economy and a regression analysis using the Autoregressive distributed lag (ARDL) method were used. In the model, the variables of inflation uncertainty and inflation rate, exchange rate uncertainty and exchange rate growth, corruption control, government efficiency, and economic growth were used as explanatory variables to explain the behavior of efficiency financial institutions. The results obtained in this study show that during the period under study, inflation and inflation uncertainty have increased the efficiency of financial institutions, but exchange rate growth and exchange rate uncertainty have decreased the efficiency of financial institutions. The results also indicate a positive effect of institutional quality indicators and no significant effect of economic growth on the efficiency of financial institutions. These findings reaffirm the importance of paying attention to the stability of the foreign exchange market and improving institutional quality in the current conditions of the Iranian economy.

Abstract Nowadays, supply chain management has become more important due to the globalization of business markets. As complexity increases, the level of uncertainty and risk in the chain also increases. Therefore, supply chain risk management is one of the issues that has received attention from organizations. Digital transformation in the supply chain is associated with benefits such as improved efficiency, increased transparency, and accelerated processes, but at the same time it also brings with it numerous risks such as cyber threats, technology dependency, and organizational resilience. This research presents an empirical model for assessing the risks of digital transformation in a sustainable supply chain using backpropagation neural network (BPNN). Research data is collected from 120 companies active in manufacturing and logistics industries, and the model is evaluated with RMSE, MAE, and classification accuracy criteria. The results show that the proposed model is able to predict the level of digital risk with 93% accuracy. Sensitivity analysis also shows that cybersecurity has the greatest impact (coefficient 0.45) on the overall risk.