Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 Examining and Prioritizing the Impacts of Artificial Intelligence Usage: The Golden Key to Successful Marketing in the Banking System Examining and Prioritizing the Impacts of Artificial Intelligence Usage: The Golden Key to Successful Marketing in the Banking System 1 26 106759 10.22059/frj.2025.387854.1007688 FA Dariush Tahmasebi Assistant Prof., Department of Management, Faculty of Commerce and Trade, College of Management. University of Tehran, Tehran, Iran. Journal Article 2024 12 30 <strong>Objective</strong> In today’s rapidly globalizing and digitalizing world, various industries face numerous challenges, including swift changes in demand, intense competition, and cultural diversity. Similarly, the financial and banking services sector has experienced fundamental transformations within these complex environments. In the meantime, artificial intelligence, as a new and transformative technology, has played an important role in smart financial institutions and banking. Therefore, the purpose of the study is to investigate the impact of using artificial intelligence in banking services marketing trends.   <strong>Methods</strong> This study is applied in purpose and employs a mixed-methods approach (qualitative and quantitative) in nature. In the qualitative phase, data were collected through semi-structured interviews with 20 experts in private banking and artificial intelligence. Thematic analysis was used to analyze the data, resulting in the identification of 8 main themes and 54 sub-themes. In the quantitative phase, the Analytic Hierarchy Process (AHP) method was applied to prioritize the main themes.   <strong>Results</strong> The results of this study showed that the most important impacts identified include reducing operating and advertising costs, minimizing human error, improving decision-making accuracy, increasing the speed of banking operations, and enhancing the customer experience. In the ranking of the main themes, minimizing human error was identified as the most significant impact, while improving resource management had the least impact.   <strong>Conclusion</strong> Artificial intelligence technology can currently pave the way for a dramatic transformation in this industry and create a different future for this field. Artificial intelligence can facilitate customer credit assessment based on customer behavior. It can help institutions by learning and remembering applicable laws in the field of customer identification and anti-money laundering measures. Early identification and prevention of cybersecurity threats that can threaten the country's banks today is another application of this technology in the financial field. By increasing automation in various banking processes, this technology can increase productivity and speed of work. Chatbots based on artificial intelligence can also easily interact with customers, answer their questions, and guide them in using banking services. It can be said that the banking system is competing with each other to use artificial intelligence and also to provide practical services based on artificial intelligence technology. Undoubtedly, this technology can be considered one of the important drivers of the banking industry in the coming years, and serious steps should be taken towards its development and application. Finally, The banking system operates in a competitive arena for the application of artificial intelligence and the delivery of functional services based on it. As one of the key drivers of the banking industry in the coming years, this technology demands serious attention to its development and implementation. <strong>Objective</strong> In today’s rapidly globalizing and digitalizing world, various industries face numerous challenges, including swift changes in demand, intense competition, and cultural diversity. Similarly, the financial and banking services sector has experienced fundamental transformations within these complex environments. In the meantime, artificial intelligence, as a new and transformative technology, has played an important role in smart financial institutions and banking. Therefore, the purpose of the study is to investigate the impact of using artificial intelligence in banking services marketing trends.   <strong>Methods</strong> This study is applied in purpose and employs a mixed-methods approach (qualitative and quantitative) in nature. In the qualitative phase, data were collected through semi-structured interviews with 20 experts in private banking and artificial intelligence. Thematic analysis was used to analyze the data, resulting in the identification of 8 main themes and 54 sub-themes. In the quantitative phase, the Analytic Hierarchy Process (AHP) method was applied to prioritize the main themes.   <strong>Results</strong> The results of this study showed that the most important impacts identified include reducing operating and advertising costs, minimizing human error, improving decision-making accuracy, increasing the speed of banking operations, and enhancing the customer experience. In the ranking of the main themes, minimizing human error was identified as the most significant impact, while improving resource management had the least impact.   <strong>Conclusion</strong> Artificial intelligence technology can currently pave the way for a dramatic transformation in this industry and create a different future for this field. Artificial intelligence can facilitate customer credit assessment based on customer behavior. It can help institutions by learning and remembering applicable laws in the field of customer identification and anti-money laundering measures. Early identification and prevention of cybersecurity threats that can threaten the country's banks today is another application of this technology in the financial field. By increasing automation in various banking processes, this technology can increase productivity and speed of work. Chatbots based on artificial intelligence can also easily interact with customers, answer their questions, and guide them in using banking services. It can be said that the banking system is competing with each other to use artificial intelligence and also to provide practical services based on artificial intelligence technology. Undoubtedly, this technology can be considered one of the important drivers of the banking industry in the coming years, and serious steps should be taken towards its development and application. Finally, The banking system operates in a competitive arena for the application of artificial intelligence and the delivery of functional services based on it. As one of the key drivers of the banking industry in the coming years, this technology demands serious attention to its development and implementation. https://jfr.ut.ac.ir/article_106759_d1a94a9c08c2efbe3971ec03ae7f6f0b.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 Examining the Impact of Islamic Treasury Bill Yields on Iran’s Capital Market Returns Using the Quantile on Quantile Connectedness Model Examining the Impact of Islamic Treasury Bill Yields on Iran’s Capital Market Returns Using the Quantile on Quantile Connectedness Model 27 55 106760 10.22059/frj.2025.388616.1007693 FA Iman Dadashi Assistant Prof., Department of Accounting, Faculty of Economics Sciences and Administrative, University of Qom, Qom, Iran. Vahid Omidi Assistant Prof., Department of Economics, Faculty of Economics Sciences and Administrative, University of Qom, Qom, Iran. Journal Article 2025 01 11 <strong>Objective</strong> The capital market is one of the main pillars of a country’s economy, playing a key role in pooling funds and optimizing resource allocation. This market provides a platform for financing economic enterprises and contributes to the country’s economic growth and development. Among the factors influencing the capital market, changes in the yield to maturity (YTM) of Islamic Treasury Bonds (ITBs)—a risk-free financial instrument—can significantly impact investment flows. This study aims to investigate the relationship between changes in the YTM of ITBs and the returns of both the total and equal-weighted indices of the Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB). The central research question is whether an increase in the YTM of ITBs leads to a decline in the returns of capital market indices, and how this relationship varies under different economic conditions.   <strong>Methods</strong> To address the research question, the Quantile-on-Quantile Connectedness model was employed, allowing for the examination of asymmetric and varying relationships across different quantiles of the variables' distributions. The data used in this study include the YTM of ITBs issued during three periods: the Iranian calendar years 1393 (2014/15), 1397 (2018/19), and 1400 (2021/22), along with the returns of the TSE total index, TSE equal-weighted index, IFB total index, and IFB equal-weighted index. The analysis was conducted using monthly data, focusing on different quantiles of changes in YTM and index returns. Additionally, the impact of crisis periods, such as the economic fluctuations observed in late 1402 (2023/24), was incorporated into the analysis.   <strong>Results</strong> The results indicate that the relationship between changes in the YTM of ITBs and the returns of the TSE and IFB indices is significantly asymmetric across the upper and lower quantiles of the distribution. During the Iranian calendar year of 1400 (2021/22), this relationship predominantly flowed from changes in the YTM of ITBs to the index returns. Furthermore, with the upward trend in the YTM of ITBs in late 1402 (2023/24), coinciding with declining index returns, the intensity of this asymmetric relationship increased. These findings suggest that a rise in the YTM of ITBs can attract investors to these bonds and exacerbate selling pressure in the capital market. Conversely, during the years of 1393 (2014/15) and 1397 (2018/19), the direction of the relationship was reversed, with the returns of the TSE and IFB indices exerting greater influence on the YTM of ITBs. This indicates that under normal or non-crisis conditions, the YTM of ITBs has less impact on the capital market and is more influenced by fluctuations in market indices.   <strong>Conclusion</strong> The findings demonstrate that during periods of crisis and declining capital markets, an increase in the YTM of ITBs can intensify the downward trend in the capital market. Conversely, under normal economic conditions, the impact of the YTM on the capital market is more limited. Based on these results, it is recommended that the Central Bank and other economic policymakers adjust policies related to interest rates and treasury bonds to prevent extensive capital shifts to risk-free bonds and the subsequent reduction in capital market investments. Additionally, policies aimed at enhancing the attractiveness of the capital market by facilitating financing processes and reducing costs for enterprises are essential. Finally, continuous monitoring of the relationship between the YTM of ITBs and capital market returns using advanced predictive and analytical models can help identify and prevent potential crises. These measures can maintain equilibrium in the capital market and contribute to sustainable economic growth. <strong>Objective</strong> The capital market is one of the main pillars of a country’s economy, playing a key role in pooling funds and optimizing resource allocation. This market provides a platform for financing economic enterprises and contributes to the country’s economic growth and development. Among the factors influencing the capital market, changes in the yield to maturity (YTM) of Islamic Treasury Bonds (ITBs)—a risk-free financial instrument—can significantly impact investment flows. This study aims to investigate the relationship between changes in the YTM of ITBs and the returns of both the total and equal-weighted indices of the Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB). The central research question is whether an increase in the YTM of ITBs leads to a decline in the returns of capital market indices, and how this relationship varies under different economic conditions.   <strong>Methods</strong> To address the research question, the Quantile-on-Quantile Connectedness model was employed, allowing for the examination of asymmetric and varying relationships across different quantiles of the variables' distributions. The data used in this study include the YTM of ITBs issued during three periods: the Iranian calendar years 1393 (2014/15), 1397 (2018/19), and 1400 (2021/22), along with the returns of the TSE total index, TSE equal-weighted index, IFB total index, and IFB equal-weighted index. The analysis was conducted using monthly data, focusing on different quantiles of changes in YTM and index returns. Additionally, the impact of crisis periods, such as the economic fluctuations observed in late 1402 (2023/24), was incorporated into the analysis.   <strong>Results</strong> The results indicate that the relationship between changes in the YTM of ITBs and the returns of the TSE and IFB indices is significantly asymmetric across the upper and lower quantiles of the distribution. During the Iranian calendar year of 1400 (2021/22), this relationship predominantly flowed from changes in the YTM of ITBs to the index returns. Furthermore, with the upward trend in the YTM of ITBs in late 1402 (2023/24), coinciding with declining index returns, the intensity of this asymmetric relationship increased. These findings suggest that a rise in the YTM of ITBs can attract investors to these bonds and exacerbate selling pressure in the capital market. Conversely, during the years of 1393 (2014/15) and 1397 (2018/19), the direction of the relationship was reversed, with the returns of the TSE and IFB indices exerting greater influence on the YTM of ITBs. This indicates that under normal or non-crisis conditions, the YTM of ITBs has less impact on the capital market and is more influenced by fluctuations in market indices.   <strong>Conclusion</strong> The findings demonstrate that during periods of crisis and declining capital markets, an increase in the YTM of ITBs can intensify the downward trend in the capital market. Conversely, under normal economic conditions, the impact of the YTM on the capital market is more limited. Based on these results, it is recommended that the Central Bank and other economic policymakers adjust policies related to interest rates and treasury bonds to prevent extensive capital shifts to risk-free bonds and the subsequent reduction in capital market investments. Additionally, policies aimed at enhancing the attractiveness of the capital market by facilitating financing processes and reducing costs for enterprises are essential. Finally, continuous monitoring of the relationship between the YTM of ITBs and capital market returns using advanced predictive and analytical models can help identify and prevent potential crises. These measures can maintain equilibrium in the capital market and contribute to sustainable economic growth. https://jfr.ut.ac.ir/article_106760_0ffcbf61bc4eac5c2c17b636fa8362ff.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 An Ontological Framework of Blockchain Capabilities Amid Emerging Tokenization Development Contexts in Future Perspectives An Ontological Framework of Blockchain Capabilities Amid Emerging Tokenization Development Contexts in Future Perspectives 56 92 106761 10.22059/frj.2025.388892.1007696 FA Amir Hajizadeh Amini PhD Candidate, Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran. Seyed Abbas Borhani Assistant Prof., Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran. Mojgan Safa Assistant Prof., Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran. Journal Article 2025 01 16 <strong>Objective</strong> The emergence of blockchains as a new generation of technology and information has brought about new developments in the field of data organization and trading transactions, which, by creating a new data authentication system, transform all types of data into encrypted tokens and facilitate exchange between users. Such developments in developing countries, such as Iran, face legal and, of course, cognitive limitations in terms of the capabilities and contexts for converting assets into tokens. For this reason, by focusing on the phenomenological process, this study seeks to identify the prerequisites for the development of tokenization and explain the capabilities of blockchain in a structural model based on the structures of strategic reference points.    <strong>Methods</strong> By systematically reviewing the existing literature, the study most closely related to the two phenomena of this research was evaluated to determine the initial model structures of strategic reference points, based on the two central dimensions of the underlying prerequisites for tokenization development. These two identified dimensions were then placed on the vertical and horizontal axes to develop interview questions aimed at identifying open codes and emergent propositional themes during 16 interviews with practitioners. Next, by adapting the propositional themes into synonymous concepts and eliminating redundant ones, the final themes for the structural categorization of the final strategic reference model were determined through hierarchical scoring evaluation checklists.   <strong>Results</strong> Reviewing nine similar studies, the prerequisites for developing tokenization mechanisms were identified as the main axes of the initial strategic reference model by creating a two-dimensional matrix. The results of the study, based on the identification of two areas—"cyber support" and "institutional support"—through a systematic review of similar research, indicate 261 open codes derived from 16 interviews, which led to the emergence of 25 propositional themes. Then, by performing score scaling of these 25 propositional themes across four categories titled "Staking Token Capability," "Binance Coin Token Capability," "Stable Coin Token Capability," and "Securities Token Capability," the foundation was established for the final model of the Strategic Reference Points Matrix.   <strong>Conclusion</strong> This study may help policymakers and developers of digital financial transaction norms move away from a one-dimensional view that simply ignores this capability in today’s technologically advanced world and instead consider the potential of blockchain’s discrete capabilities to enhance the dynamics of the economic system, enabling less troublesome financial transactions and greater freedom for traders. In fact, the results recommend that policymakers and developers of norms for digital-based financial transactions shift from ignoring this capability and recognize the blockchain’s discrete capabilities identified in this study, which can contribute to a more dynamic economic system by facilitating smoother financial transactions—even in the presence of financial sanctions—without intermediaries. This freedom allows traders to achieve lower-risk returns by selecting an appropriate portfolio of financial transactions.   <strong>Objective</strong> The emergence of blockchains as a new generation of technology and information has brought about new developments in the field of data organization and trading transactions, which, by creating a new data authentication system, transform all types of data into encrypted tokens and facilitate exchange between users. Such developments in developing countries, such as Iran, face legal and, of course, cognitive limitations in terms of the capabilities and contexts for converting assets into tokens. For this reason, by focusing on the phenomenological process, this study seeks to identify the prerequisites for the development of tokenization and explain the capabilities of blockchain in a structural model based on the structures of strategic reference points.    <strong>Methods</strong> By systematically reviewing the existing literature, the study most closely related to the two phenomena of this research was evaluated to determine the initial model structures of strategic reference points, based on the two central dimensions of the underlying prerequisites for tokenization development. These two identified dimensions were then placed on the vertical and horizontal axes to develop interview questions aimed at identifying open codes and emergent propositional themes during 16 interviews with practitioners. Next, by adapting the propositional themes into synonymous concepts and eliminating redundant ones, the final themes for the structural categorization of the final strategic reference model were determined through hierarchical scoring evaluation checklists.   <strong>Results</strong> Reviewing nine similar studies, the prerequisites for developing tokenization mechanisms were identified as the main axes of the initial strategic reference model by creating a two-dimensional matrix. The results of the study, based on the identification of two areas—"cyber support" and "institutional support"—through a systematic review of similar research, indicate 261 open codes derived from 16 interviews, which led to the emergence of 25 propositional themes. Then, by performing score scaling of these 25 propositional themes across four categories titled "Staking Token Capability," "Binance Coin Token Capability," "Stable Coin Token Capability," and "Securities Token Capability," the foundation was established for the final model of the Strategic Reference Points Matrix.   <strong>Conclusion</strong> This study may help policymakers and developers of digital financial transaction norms move away from a one-dimensional view that simply ignores this capability in today’s technologically advanced world and instead consider the potential of blockchain’s discrete capabilities to enhance the dynamics of the economic system, enabling less troublesome financial transactions and greater freedom for traders. In fact, the results recommend that policymakers and developers of norms for digital-based financial transactions shift from ignoring this capability and recognize the blockchain’s discrete capabilities identified in this study, which can contribute to a more dynamic economic system by facilitating smoother financial transactions—even in the presence of financial sanctions—without intermediaries. This freedom allows traders to achieve lower-risk returns by selecting an appropriate portfolio of financial transactions.   https://jfr.ut.ac.ir/article_106761_a2d5d62485edd9277650009a13f34e20.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 Design and Validation of a Service Marketing Model for the Adoption of Social Security Retirement Funds with a Financial Literacy Approach Design and Validation of a Service Marketing Model for the Adoption of Social Security Retirement Funds with a Financial Literacy Approach 93 127 104072 10.22059/frj.2024.380672.1007631 FA Hamid Sheikhpoodeh Ph.D. Candidate, Department of Business Management, South Tehran Branch, Islamic Azad University, Tehran, Iran. Mohammad Nasrollahniya Assistant Prof., Department of Business Management, South Tehran Branch, Islamic Azad University, Tehran, Iran. Naser Azad Assistant Prof., Department of Business Management, South Tehran Branch, Islamic Azad University, Tehran, Iran. Abdoul Rasoul Mostajeran Assistant Prof., Department of Financial Mathematics, Khwarazmi University, Tehran, Iran. Journal Article 2024 08 10 <strong>Objective</strong><br />The marketing of retirement fund services is inherently linked to the future security and welfare of individuals; therefore, it must be conducted in a specialized manner. Marketing strategies for retirement fund services must be designed with careful consideration of the financial literacy and knowledge levels of the target audience to ensure maximum effectiveness. As a result, this issue has become a focal point for policymakers, managers, and practitioners within the Iranian Social Security Organization. This study is particularly important because, without continuous evaluation, monitoring, and enhancement of individuals’ financial literacy, the marketing objectives of these funds cannot be realized, leading to a potential waste of substantial budgets and resources. From a theoretical standpoint, this topic holds significant value; although numerous studies have addressed retirement funds, none have specifically explored them from a marketing perspective with an emphasis on financial literacy. Previous studies have addressed these components separately, without any integration or alignment between them. The development and evolution of the role of service marketing in the domain of retirement funds, emphasizing financial literacy, has been overlooked by researchers. A review of the literature indicates a profound research gap in the area under investigation. Therefore, the present study was conducted with an applied-developmental approach to service marketing for the adoption of retirement funds with a financial literacy perspective within the Social Security Organization. The theoretical contribution and knowledge enhancement of this study lie in linking the concepts of service marketing and financial literacy in the context of retirement funds. Moreover, since the Iranian Social Security Organization has its unique conditions and requirements, this research employs an exploratory mixed-methods design to identify the relevant factors. Accordingly, the present study addresses the key question: What is the service marketing model for the adoption of Iranian Social Security retirement funds from the financial literacy perspective?<br /> <br /><strong>Methods</strong><br />This study is applied-developmental in its objective and utilizes a cross-sectional survey design regarding method and data collection period. To achieve the research objectives, an exploratory mixed-methods approach was employed. The qualitative sample included 20 experienced managers of the Social Security retirement fund, selected purposively until theoretical saturation was reached. For the quantitative phase, the views of 384 compulsory and self-employed insured individuals were surveyed. Sampling for the quantitative phase was conducted via cluster-random sampling. Data collection tools included semi-structured interviews and a researcher-made questionnaire, which were validated through construct validity, convergent validity, and discriminant validity. Reliability was assessed using Cronbach’s alpha and composite reliability, confirming the questionnaire’s adequacy. To identify the dimensions and components of service marketing for the adoption of Social Security retirement funds with a financial literacy approach, qualitative thematic analysis was used; the relationships between elements were determined by Interpretive Structural Modeling (ISM), and the model was validated using Partial Least Squares (PLS).<br /> <br /><strong>Results</strong><br />Results revealed that 302 codes were identified during the open coding phase. Ultimately, four overarching categories, 10 organizing categories, and 59 basic themes were extracted through axial coding.<br /> <br /><strong>Conclusion</strong><br />The findings indicate that service marketing strategy, users’ financial literacy, and physical equipment and facilities impact reliability and responsiveness. Reliability and responsiveness affect the improvement of customer experience, which leads to customer engagement, customer loyalty, and customer satisfaction. Through effective customer engagement, the adoption of retirement funds is ultimately promoted. <strong>Objective</strong><br />The marketing of retirement fund services is inherently linked to the future security and welfare of individuals; therefore, it must be conducted in a specialized manner. Marketing strategies for retirement fund services must be designed with careful consideration of the financial literacy and knowledge levels of the target audience to ensure maximum effectiveness. As a result, this issue has become a focal point for policymakers, managers, and practitioners within the Iranian Social Security Organization. This study is particularly important because, without continuous evaluation, monitoring, and enhancement of individuals’ financial literacy, the marketing objectives of these funds cannot be realized, leading to a potential waste of substantial budgets and resources. From a theoretical standpoint, this topic holds significant value; although numerous studies have addressed retirement funds, none have specifically explored them from a marketing perspective with an emphasis on financial literacy. Previous studies have addressed these components separately, without any integration or alignment between them. The development and evolution of the role of service marketing in the domain of retirement funds, emphasizing financial literacy, has been overlooked by researchers. A review of the literature indicates a profound research gap in the area under investigation. Therefore, the present study was conducted with an applied-developmental approach to service marketing for the adoption of retirement funds with a financial literacy perspective within the Social Security Organization. The theoretical contribution and knowledge enhancement of this study lie in linking the concepts of service marketing and financial literacy in the context of retirement funds. Moreover, since the Iranian Social Security Organization has its unique conditions and requirements, this research employs an exploratory mixed-methods design to identify the relevant factors. Accordingly, the present study addresses the key question: What is the service marketing model for the adoption of Iranian Social Security retirement funds from the financial literacy perspective?<br /> <br /><strong>Methods</strong><br />This study is applied-developmental in its objective and utilizes a cross-sectional survey design regarding method and data collection period. To achieve the research objectives, an exploratory mixed-methods approach was employed. The qualitative sample included 20 experienced managers of the Social Security retirement fund, selected purposively until theoretical saturation was reached. For the quantitative phase, the views of 384 compulsory and self-employed insured individuals were surveyed. Sampling for the quantitative phase was conducted via cluster-random sampling. Data collection tools included semi-structured interviews and a researcher-made questionnaire, which were validated through construct validity, convergent validity, and discriminant validity. Reliability was assessed using Cronbach’s alpha and composite reliability, confirming the questionnaire’s adequacy. To identify the dimensions and components of service marketing for the adoption of Social Security retirement funds with a financial literacy approach, qualitative thematic analysis was used; the relationships between elements were determined by Interpretive Structural Modeling (ISM), and the model was validated using Partial Least Squares (PLS).<br /> <br /><strong>Results</strong><br />Results revealed that 302 codes were identified during the open coding phase. Ultimately, four overarching categories, 10 organizing categories, and 59 basic themes were extracted through axial coding.<br /> <br /><strong>Conclusion</strong><br />The findings indicate that service marketing strategy, users’ financial literacy, and physical equipment and facilities impact reliability and responsiveness. Reliability and responsiveness affect the improvement of customer experience, which leads to customer engagement, customer loyalty, and customer satisfaction. Through effective customer engagement, the adoption of retirement funds is ultimately promoted. https://jfr.ut.ac.ir/article_104072_5d82564c28006580499e2042bc486412.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 The Impact of Macroeconomic Variables on the Systematic Risk of the Top 50 Companies on the Tehran Stock Exchange: A Bayesian Model Averaging Approach The Impact of Macroeconomic Variables on the Systematic Risk of the Top 50 Companies on the Tehran Stock Exchange: A Bayesian Model Averaging Approach 128 160 103546 10.22059/frj.2025.382691.1007647 FA Leila Farvizi Ph.D Candidate, Department of Financial Economics, Aras International Campus, University of Tabriz, Tabriz, Iran. Sakineh Sojoodi Associate Prof., Department of Economics, Faculty of Management and Accounting, University of Tabriz, Tabriz, Iran. Hossein Asgharpour Prof., Department of Economics, Faculty of Management and Accounting, University of Tabriz, Tabriz, Iran. Jafar Haghighat Prof., Department of Economics, Faculty of Management and Accounting, University of Tabriz, Tabriz, Iran. Journal Article 2024 09 22 <strong>Objective</strong> Since unsystematic risk can be mitigated through diversification of the asset portfolio, the focus of researchers and investors has increasingly shifted towards systematic risk and its determinants. The global financial crisis of 2007 brought significant economic turmoil and triggered a substantial chain reaction within the financial sector, which further amplified the emphasis on systematic risk as a critical factor related to financial stability. Consequently, due to the growing significance of systematic risk and the necessity for companies to respond appropriately, it becomes imperative to investigate the factors that influence systematic risk. In fact, an understanding of the factors impacting the level of systematic risk is a prerequisite for the implementation of effective risk management measures. Moreover, beta, as a measure of systematic risk, cannot be directly assessed through stock price movements for unlisted firms, presenting challenges in estimating the cost of capital and the relative risk profiles of these entities. Therefore, the development of a model capable of predicting systematic risk using macroeconomic variables is a research priority. The multitude of variables that potentially affect systematic risk necessitates experimental research to identify the most salient among them. Therefore, the main goal of this research is to investigate and identify the most important macroeconomic variables that determine the systematic risk of shares in the Iranian Stock Exchange.   <strong>Methods</strong> Despite the extensive body of research examining the determinants of systematic risk in corporate stock, there exists a paucity of theoretical modeling addressing the macroeconomic determinants of this variable. Furthermore, existing studies often rely on an arbitrary selection of independent variables without comprehensive theoretical foundations<strong><em>.</em></strong> There is considerable debate regarding the variables influencing systematic risk and their inclusion in the model. These differing viewpoints have resulted in disparate outcomes across various studies. Given the importance of systematic risk and the lack of comprehensiveness in prior research, the present study employs the Bayesian Model Averaging (BMA) approach to analyze the effects of macroeconomic variables on the systematic risk of companies listed on the Tehran Stock Exchange during the period from 2015 to 2015, encompassing a total of 72 periods. Recognizing that the values of macroeconomic variables are consistent for all companies within a given year, cross-sectional analysis is deemed inadequate. Thus, this research utilizes the systematic risk of a sample portfolio of stocks over time, specifically selecting a common portfolio comprising 50 actively listed companies in the Iranian stock market<strong><em>.</em></strong> Accordingly, the dependent variable in this study is the beta of the portfolio comprising the 50 most active stocks, with 14 macroeconomic variables identified as potential explanatory variables.   <strong>Results</strong> The findings indicate that among the variables examined, a total of eight variables exert the most significant influence on systematic risk. Notably, the housing rental price index and the consumer price index rank first and second, respectively, in their impact. The average coefficient for the housing rent variable is positive, while the average coefficient for the consumer price index is negative. Following these, the unemployment rate and the amount of foreign assets held by the banking system rank third and fourth, respectively; the average coefficient for the unemployment rate is positive, whereas foreign assets exhibit a negative average coefficient. Liquidity is positioned fifth with a positive coefficient. Lastly, government expenditures and the price of gold coins serve as the sixth and seventh explanatory variables, respectively, with PIP values exceeding 0.5, demonstrating negative and positive average effects on the systematic risk of the stock portfolio comprising the top 50 companies.   <strong>Conclusion</strong> According to the findings of this study, it is possible to predict the systematic risk of stocks and stock portfolios using macroeconomic variables such as housing rental prices, consumer price index, unemployment rate, volume of foreign assets of the banking system, volume of liquidity, government spending, and gold price. This prediction helps investors to manage the risk of their portfolio and provides economic policymakers and company managers with the possibility to consider the necessary measures to deal with and manage the risk in the stock market.   <strong>Objective</strong> Since unsystematic risk can be mitigated through diversification of the asset portfolio, the focus of researchers and investors has increasingly shifted towards systematic risk and its determinants. The global financial crisis of 2007 brought significant economic turmoil and triggered a substantial chain reaction within the financial sector, which further amplified the emphasis on systematic risk as a critical factor related to financial stability. Consequently, due to the growing significance of systematic risk and the necessity for companies to respond appropriately, it becomes imperative to investigate the factors that influence systematic risk. In fact, an understanding of the factors impacting the level of systematic risk is a prerequisite for the implementation of effective risk management measures. Moreover, beta, as a measure of systematic risk, cannot be directly assessed through stock price movements for unlisted firms, presenting challenges in estimating the cost of capital and the relative risk profiles of these entities. Therefore, the development of a model capable of predicting systematic risk using macroeconomic variables is a research priority. The multitude of variables that potentially affect systematic risk necessitates experimental research to identify the most salient among them. Therefore, the main goal of this research is to investigate and identify the most important macroeconomic variables that determine the systematic risk of shares in the Iranian Stock Exchange.   <strong>Methods</strong> Despite the extensive body of research examining the determinants of systematic risk in corporate stock, there exists a paucity of theoretical modeling addressing the macroeconomic determinants of this variable. Furthermore, existing studies often rely on an arbitrary selection of independent variables without comprehensive theoretical foundations<strong><em>.</em></strong> There is considerable debate regarding the variables influencing systematic risk and their inclusion in the model. These differing viewpoints have resulted in disparate outcomes across various studies. Given the importance of systematic risk and the lack of comprehensiveness in prior research, the present study employs the Bayesian Model Averaging (BMA) approach to analyze the effects of macroeconomic variables on the systematic risk of companies listed on the Tehran Stock Exchange during the period from 2015 to 2015, encompassing a total of 72 periods. Recognizing that the values of macroeconomic variables are consistent for all companies within a given year, cross-sectional analysis is deemed inadequate. Thus, this research utilizes the systematic risk of a sample portfolio of stocks over time, specifically selecting a common portfolio comprising 50 actively listed companies in the Iranian stock market<strong><em>.</em></strong> Accordingly, the dependent variable in this study is the beta of the portfolio comprising the 50 most active stocks, with 14 macroeconomic variables identified as potential explanatory variables.   <strong>Results</strong> The findings indicate that among the variables examined, a total of eight variables exert the most significant influence on systematic risk. Notably, the housing rental price index and the consumer price index rank first and second, respectively, in their impact. The average coefficient for the housing rent variable is positive, while the average coefficient for the consumer price index is negative. Following these, the unemployment rate and the amount of foreign assets held by the banking system rank third and fourth, respectively; the average coefficient for the unemployment rate is positive, whereas foreign assets exhibit a negative average coefficient. Liquidity is positioned fifth with a positive coefficient. Lastly, government expenditures and the price of gold coins serve as the sixth and seventh explanatory variables, respectively, with PIP values exceeding 0.5, demonstrating negative and positive average effects on the systematic risk of the stock portfolio comprising the top 50 companies.   <strong>Conclusion</strong> According to the findings of this study, it is possible to predict the systematic risk of stocks and stock portfolios using macroeconomic variables such as housing rental prices, consumer price index, unemployment rate, volume of foreign assets of the banking system, volume of liquidity, government spending, and gold price. This prediction helps investors to manage the risk of their portfolio and provides economic policymakers and company managers with the possibility to consider the necessary measures to deal with and manage the risk in the stock market.   https://jfr.ut.ac.ir/article_103546_39b7dddeb1751091d7456cd382d9d438.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 Decisions on Leverage Adjustment and Stock Price Crash Risk Decisions on Leverage Adjustment and Stock Price Crash Risk 161 186 103309 10.22059/frj.2025.387484.1007682 FA Azam Pouryousof Assistant Prof., Department of Accounting, Payame Noor University, Tehran, Iran. Mahdi Saghafi Assistant Prof., Department of Accounting, Payame Noor University, Tehran, Iran. Mohsen Moodi hD., Department of Economics, Esfahan Baranch, Islamic Azad University, Esfahan, Iran. Journal Article 2024 12 23 <strong>Objective</strong><br />Existing theories of capital structure indicate that information asymmetry is an important factor in adjusting target leverage. The signaling theory of capital structure shows that the stock market reacts positively (negatively) to the announcement of debt (equity). Furthermore, the dynamic trade-off theory allows firms to weigh the benefits of maintaining a financial structure below the target leverage level against the costs of adjusting leverage. Accordingly, this theory suggests that firms with higher adjustment costs tend to adjust their leverage ratios toward their targets at a slower speed. In this article, we examine whether stock price crash risk can affect the decision-making process regarding financial leverage adjustment. Thus, it is expected that as stock price crash risk increases, firms’ tendency to adjust their leverage decreases.<br /> <br /><strong>Methods</strong><br />The study population was selected from the Tehran Stock Exchange based on four criteria. Data from 143 companies were collected for the period from 2013 to 2024, and the research models were estimated using multivariate regression, controlling for year and industry fixed effects.<br /> <br /> <br /><strong>Results</strong><br />Stock price crash risk has a negative effect on the speed of leverage adjustment, and a firm’s leverage level does not moderate this relationship.<br /> <br /><strong>Conclusion</strong><br />Two interesting findings may shed light on how dynamic capital structure decisions are made. First, the empirical results show that firms more exposed to stock price crash risk adjust their leverage ratios more slowly toward their target leverage ratio. This result can be explained by the fact that firms facing higher stock price crash risk encounter greater transaction costs when adjusting their financial leverage. Recent evidence on stock price crash risk indicates its association with information asymmetry. Therefore, in line with dynamic trade-off theory, firms with higher adjustment costs show greater tolerance for operating below optimal leverage and adjust more slowly toward their target leverage. Second, the empirical results reveal that the effect of stock price crash risk on the speed of financial leverage adjustment does not depend on the actual level of financial leverage. According to capital structure signaling theory, stock prices are predicted to increase (decrease) following the announcement of debt (equity) issuance. Thus, in firms with lower leverage, an increase in stock price crash risk reduces the speed of leverage adjustment, as they typically need to issue equity. On the other hand, for firms with higher leverage, this effect is weaker because issuing debt can help conceal bad news. Consequently, the researchers’ expectations regarding the moderating role of firms’ financial leverage on the relationship between stock price crash risk and the speed of leverage adjustment were not confirmed. Possible reasons for this result include the weak efficiency of the Iranian capital market, the substantial trading volume of new investors entering the market, and the generally low level of financial leverage among the sample firms. <strong>Objective</strong><br />Existing theories of capital structure indicate that information asymmetry is an important factor in adjusting target leverage. The signaling theory of capital structure shows that the stock market reacts positively (negatively) to the announcement of debt (equity). Furthermore, the dynamic trade-off theory allows firms to weigh the benefits of maintaining a financial structure below the target leverage level against the costs of adjusting leverage. Accordingly, this theory suggests that firms with higher adjustment costs tend to adjust their leverage ratios toward their targets at a slower speed. In this article, we examine whether stock price crash risk can affect the decision-making process regarding financial leverage adjustment. Thus, it is expected that as stock price crash risk increases, firms’ tendency to adjust their leverage decreases.<br /> <br /><strong>Methods</strong><br />The study population was selected from the Tehran Stock Exchange based on four criteria. Data from 143 companies were collected for the period from 2013 to 2024, and the research models were estimated using multivariate regression, controlling for year and industry fixed effects.<br /> <br /> <br /><strong>Results</strong><br />Stock price crash risk has a negative effect on the speed of leverage adjustment, and a firm’s leverage level does not moderate this relationship.<br /> <br /><strong>Conclusion</strong><br />Two interesting findings may shed light on how dynamic capital structure decisions are made. First, the empirical results show that firms more exposed to stock price crash risk adjust their leverage ratios more slowly toward their target leverage ratio. This result can be explained by the fact that firms facing higher stock price crash risk encounter greater transaction costs when adjusting their financial leverage. Recent evidence on stock price crash risk indicates its association with information asymmetry. Therefore, in line with dynamic trade-off theory, firms with higher adjustment costs show greater tolerance for operating below optimal leverage and adjust more slowly toward their target leverage. Second, the empirical results reveal that the effect of stock price crash risk on the speed of financial leverage adjustment does not depend on the actual level of financial leverage. According to capital structure signaling theory, stock prices are predicted to increase (decrease) following the announcement of debt (equity) issuance. Thus, in firms with lower leverage, an increase in stock price crash risk reduces the speed of leverage adjustment, as they typically need to issue equity. On the other hand, for firms with higher leverage, this effect is weaker because issuing debt can help conceal bad news. Consequently, the researchers’ expectations regarding the moderating role of firms’ financial leverage on the relationship between stock price crash risk and the speed of leverage adjustment were not confirmed. Possible reasons for this result include the weak efficiency of the Iranian capital market, the substantial trading volume of new investors entering the market, and the generally low level of financial leverage among the sample firms. https://jfr.ut.ac.ir/article_103309_8c360cb9222929dafbff0c366befda79.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 Predicting Mutual Fund Returns in Member Countries of the Federation of Euro-Asian Stock Exchanges: A Spatial and Artificial Intelligence Approach Predicting Mutual Fund Returns in Member Countries of the Federation of Euro-Asian Stock Exchanges: A Spatial and Artificial Intelligence Approach 187 235 106762 10.22059/frj.2025.385026.1007665 FA Nashmil Esmaily PhD Candidate, Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran. Parviz Piri Associate Prof., Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran. Ali Ashtab Assistant Prof., Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran. Mehdi Heydari Associate Prof., Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran. Akbar Zavarirezaei Assistant Prof., Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran. 0009-0005-1999-3676 Journal Article 2024 11 07 <strong>Objective</strong> The primary objective of this study is to forecast the returns of investment funds in developed and developing countries that are members of the Federation of Euro-Asian Stock Exchanges (FEAS).   <strong>Methods</strong> This applied research study analyzes financial data from investment funds in FEAS countries over the period 2015–2023. The modeling framework employs artificial intelligence techniques, spatial econometrics, and a hybrid approach combining both within a spatial hybrid panel structure. The study examines the effects of key variables—including the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors—on fund returns. The central aim is to assess the predictive efficiency of these models under different economic conditions.   <strong>Results</strong> The findings demonstrate that artificial intelligence models outperform alternative approaches in forecasting returns for both developed and developing country groups. The neoclassical neural network showed the strongest performance across both groups, while the multilayer perceptron also proved effective in developed markets; in contrast, decision tree models exhibited weaker predictive capability. Moreover, integrating artificial intelligence methods with spatial techniques led to significant improvements in forecasting accuracy. The results indicate that market returns had a positive effect in both groups, with a more pronounced impact in developed countries. The analysis identified the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors as significant determinants of fund returns. Notably, the Sharpe ratio had a significant positive effect in both groups, reflecting the greater sensitivity of investors in developed markets to risk-adjusted returns. Finally, forecasting mutual fund returns using the combined artificial intelligence and spatial hybrid panel approach revealed substantial differences in model performance between developed and developing countries. In developed countries, models such as the multilayer perceptron and decision tree achieved superior performance, whereas in developing countries, deep learning and support vector machine models demonstrated greater effectiveness.   <strong>Conclusion</strong> This study demonstrates that developed markets substantially outperform developing markets in terms of predictability and performance stability. Advanced artificial intelligence models proved effective in forecasting fund returns in both developed and developing country groups. Ultimately, the design of hybrid models that integrate artificial intelligence with spatial analysis and spatial hybrid panels can enhance the accuracy of fund return forecasts, thereby enabling investors and fund managers to make more informed financial decisions. Accordingly, the implementation of hybrid artificial intelligence and spatial hybrid panel models in both groups improved efficiency and increased forecasting precision, exerting a considerable influence on the quality of financial decision-making. This research also revealed that variables affecting fund returns—including the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors—together with countries’ economic conditions, influence model performance, underscoring the importance of incorporating macroeconomic factors into financial and accounting analyses. For market practitioners and financial analysts, these findings can contribute to enhanced financial reporting processes, more rigorous risk assessment, and improved investment decision-making. Specifically, the results can facilitate greater transparency of financial information within investment funds. <strong>Objective</strong> The primary objective of this study is to forecast the returns of investment funds in developed and developing countries that are members of the Federation of Euro-Asian Stock Exchanges (FEAS).   <strong>Methods</strong> This applied research study analyzes financial data from investment funds in FEAS countries over the period 2015–2023. The modeling framework employs artificial intelligence techniques, spatial econometrics, and a hybrid approach combining both within a spatial hybrid panel structure. The study examines the effects of key variables—including the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors—on fund returns. The central aim is to assess the predictive efficiency of these models under different economic conditions.   <strong>Results</strong> The findings demonstrate that artificial intelligence models outperform alternative approaches in forecasting returns for both developed and developing country groups. The neoclassical neural network showed the strongest performance across both groups, while the multilayer perceptron also proved effective in developed markets; in contrast, decision tree models exhibited weaker predictive capability. Moreover, integrating artificial intelligence methods with spatial techniques led to significant improvements in forecasting accuracy. The results indicate that market returns had a positive effect in both groups, with a more pronounced impact in developed countries. The analysis identified the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors as significant determinants of fund returns. Notably, the Sharpe ratio had a significant positive effect in both groups, reflecting the greater sensitivity of investors in developed markets to risk-adjusted returns. Finally, forecasting mutual fund returns using the combined artificial intelligence and spatial hybrid panel approach revealed substantial differences in model performance between developed and developing countries. In developed countries, models such as the multilayer perceptron and decision tree achieved superior performance, whereas in developing countries, deep learning and support vector machine models demonstrated greater effectiveness.   <strong>Conclusion</strong> This study demonstrates that developed markets substantially outperform developing markets in terms of predictability and performance stability. Advanced artificial intelligence models proved effective in forecasting fund returns in both developed and developing country groups. Ultimately, the design of hybrid models that integrate artificial intelligence with spatial analysis and spatial hybrid panels can enhance the accuracy of fund return forecasts, thereby enabling investors and fund managers to make more informed financial decisions. Accordingly, the implementation of hybrid artificial intelligence and spatial hybrid panel models in both groups improved efficiency and increased forecasting precision, exerting a considerable influence on the quality of financial decision-making. This research also revealed that variables affecting fund returns—including the Sharpe ratio, Jensen’s alpha, asset growth rate, and the proportion of retail investors—together with countries’ economic conditions, influence model performance, underscoring the importance of incorporating macroeconomic factors into financial and accounting analyses. For market practitioners and financial analysts, these findings can contribute to enhanced financial reporting processes, more rigorous risk assessment, and improved investment decision-making. Specifically, the results can facilitate greater transparency of financial information within investment funds. https://jfr.ut.ac.ir/article_106762_77ec87f1d9bdc1ccfa25593c5b40b92f.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 Value Creation Framework in Capital Market Financing: A Thematic Analysis Approach Value Creation Framework in Capital Market Financing: A Thematic Analysis Approach 236 260 106764 10.22059/frj.2025.375344.1007596 FA Zahra Saleh Jalali Ph.D. Candidate, Department of Project Management and Construction, Faculty of Arts, Tarbiat Modares University, Tehran, Iran. Mohammad Hossein Sobhiyah Associate Prof., Department of Project Management and Construction, Faculty of Arts, Tarbiat Modares University, Tehran, Iran. Journal Article 2024 05 02 <strong>Objective</strong> Value and value creation constitute the core elements of any business strategy, and corporate success largely depends on the ability to create value that is perceived as meaningful by customers and other stakeholders. Firm value is directly associated with the strategic decisions undertaken by organizations, among which financing decisions represent one of the most critical. In this regard, capital markets are recognized as one of the main channels of financing within an economy and play a pivotal role in the efficient allocation of financial resources. Capital markets possess the capacity to reduce financing gaps that the banking system alone has been unable to fully address and provide firms with a wide range of alternative financing instruments. Consequently, more than a decade after the global financial crisis, firms have increasingly shown a preference for utilizing diversified financing instruments available through capital markets. Alongside the banking network, capital markets can substantially contribute to alleviating financing constraints and play a significant role in enhancing macroeconomic dynamism. Moreover, capital markets exhibit unique characteristics that, beyond merely addressing financing needs, can facilitate value creation at both the firm level and within the broader economic environment. Accordingly, this study aims to examine the concept of value creation in the context of financing through capital markets and to propose an integrated framework for value creation in capital market financing.   <strong>Methods</strong> A thematic analysis approach was employed within the framework of a systematic literature review. Relevant factors influencing value creation in capital market financing were identified, and their underlying themes were extracted and categorized. The identified open codes and themes were examined, refined, and prioritized. This process ended in the development of a comprehensive and structured framework that reflects both theoretical insights and expert perspectives on value creation in capital market financing.   <strong>Results</strong> The findings indicate that the key themes constituting the value creation framework in capital market financing, in order of priority, include targeted, high-quality, and integrated disclosure and reporting, financial leverage, corporate governance, managerial performance, macroeconomic impact, risk and return considerations, investor protection, and drivers of intangible asset creation. These themes collectively illustrate the multidimensional nature of value creation in capital markets and emphasize the interplay between firm-level practices and broader economic factors.   <strong>Conclusion</strong> The results suggest that, in examining value creation frameworks for capital market financing, even when profitability is considered as the primary focus, stakeholder theory demonstrates a superior capacity for integrating the diverse factors and themes associated with value creation. Therefore, financial managers and decision-makers are encouraged to adopt a multidimensional perspective on value creation and to consider the interests of all stakeholders in order to achieve more sustainable value outcomes. Furthermore, the findings indicate that capital markets possess the potential to realize value creation from this perspective. A comparison between the results of the systematic literature review and the prioritization derived from the focus group reveals that themes such as disclosure, reporting quality, and corporate governance are consistently emphasized and validated in both approaches. In contrast, themes related to drivers of intangible asset creation, despite their frequent appearance and growing prominence in recent literature, were assigned lower priority by the focus group participants. This discrepancy highlights the necessity for empirical and field-based studies that take into account the institutional and economic characteristics specific to Iran.   <strong>Objective</strong> Value and value creation constitute the core elements of any business strategy, and corporate success largely depends on the ability to create value that is perceived as meaningful by customers and other stakeholders. Firm value is directly associated with the strategic decisions undertaken by organizations, among which financing decisions represent one of the most critical. In this regard, capital markets are recognized as one of the main channels of financing within an economy and play a pivotal role in the efficient allocation of financial resources. Capital markets possess the capacity to reduce financing gaps that the banking system alone has been unable to fully address and provide firms with a wide range of alternative financing instruments. Consequently, more than a decade after the global financial crisis, firms have increasingly shown a preference for utilizing diversified financing instruments available through capital markets. Alongside the banking network, capital markets can substantially contribute to alleviating financing constraints and play a significant role in enhancing macroeconomic dynamism. Moreover, capital markets exhibit unique characteristics that, beyond merely addressing financing needs, can facilitate value creation at both the firm level and within the broader economic environment. Accordingly, this study aims to examine the concept of value creation in the context of financing through capital markets and to propose an integrated framework for value creation in capital market financing.   <strong>Methods</strong> A thematic analysis approach was employed within the framework of a systematic literature review. Relevant factors influencing value creation in capital market financing were identified, and their underlying themes were extracted and categorized. The identified open codes and themes were examined, refined, and prioritized. This process ended in the development of a comprehensive and structured framework that reflects both theoretical insights and expert perspectives on value creation in capital market financing.   <strong>Results</strong> The findings indicate that the key themes constituting the value creation framework in capital market financing, in order of priority, include targeted, high-quality, and integrated disclosure and reporting, financial leverage, corporate governance, managerial performance, macroeconomic impact, risk and return considerations, investor protection, and drivers of intangible asset creation. These themes collectively illustrate the multidimensional nature of value creation in capital markets and emphasize the interplay between firm-level practices and broader economic factors.   <strong>Conclusion</strong> The results suggest that, in examining value creation frameworks for capital market financing, even when profitability is considered as the primary focus, stakeholder theory demonstrates a superior capacity for integrating the diverse factors and themes associated with value creation. Therefore, financial managers and decision-makers are encouraged to adopt a multidimensional perspective on value creation and to consider the interests of all stakeholders in order to achieve more sustainable value outcomes. Furthermore, the findings indicate that capital markets possess the potential to realize value creation from this perspective. A comparison between the results of the systematic literature review and the prioritization derived from the focus group reveals that themes such as disclosure, reporting quality, and corporate governance are consistently emphasized and validated in both approaches. In contrast, themes related to drivers of intangible asset creation, despite their frequent appearance and growing prominence in recent literature, were assigned lower priority by the focus group participants. This discrepancy highlights the necessity for empirical and field-based studies that take into account the institutional and economic characteristics specific to Iran.   https://jfr.ut.ac.ir/article_106764_b9668d3792a03f17d68cc55e4b95f453.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 The Impact of FinTech on Financial Inclusion and Financial Stability in Selected Developing Countries The Impact of FinTech on Financial Inclusion and Financial Stability in Selected Developing Countries 261 299 106765 10.22059/frj.2025.387002.1007676 FA Sohad Mohsen Kazem Ph.D. Candidate, Department of Financial Economics, Faculty of Economics and Management, Urmia University, Urmia, Iran. Ali Rezazadeh Associate Prof., Department of Economics, Faculty of Economics and Management, Urmia University, Urmia, Iran. Yousef Mohammadzadeh Associate Prof., Department of Economics, Faculty of Economics and Management, Urmia University, Urmia, Iran. SeyedJamaleddin Mohseni Zonouzi Associate Prof., Department of Economics, Faculty of Economics and Management, Urmia University, Urmia, Iran. Journal Article 2024 12 14 <strong>Objective</strong> With the rapid expansion of digital financial services, FinTech has emerged as one of the key instruments for increasing access to formal financial services among different segments of society. However, alongside the positive effects of FinTech on the expansion of financial inclusion, concerns have been raised regarding its potential implications for financial stability, systemic risk, and the sustainability of the banking system. Accordingly, this study seeks to simultaneously investigate the dynamic and causal relationships among FinTech development, financial inclusion, and financial stability within a dynamic econometric framework. Its main focus is to identify the direction and magnitude of the effects of FinTech on financial inclusion and financial stability, to determine whether the development of digital financial services can improve the performance of the financial system in developing countries without generating structural risks.  <strong>Methods</strong> This study employs panel data from 38 developing countries over the period 2004–2023. To capture the dynamic and endogenous relationships among the variables, a Panel Vector Autoregression (PVAR) model is utilized. Prior to model estimation, panel unit root tests are conducted, and the results confirm that all variables are stationary at the 5% significance level. Based on standard information criteria, the optimal lag length of the PVAR model is selected as one period. The financial stability is measured using the banking Z-score, which is a widely accepted indicator of banking system resilience. Financial inclusion is constructed through Principal Component Analysis (PCA) using variables such as the number of bank branches, automated teller machines (ATMs), bank loans, and bank deposits. The FinTech index was measured using the extent of mobile-based digital payment utilization, reflecting the diffusion of digital financial technologies. To analyze the dynamic interactions and causal linkages among the variables, Granger causality tests, impulse response functions, and forecast error variance decomposition are employed.   <strong>Results</strong> The results of the stability tests confirm that the estimated PVAR model is structurally stable. The Granger causality tests reveal a unidirectional causal relationship running from FinTech development to financial inclusion, indicating that the expansion of digital financial services plays a significant role in enhancing access to financial services. Moreover, a bidirectional causal relationship is identified between FinTech and financial stability, suggesting a mutual interaction between digital financial innovation and the resilience of the financial system. Additionally, a unidirectional causal relationship from financial stability to financial inclusion is observed, highlighting the importance of a stable financial environment in facilitating broader financial access. The impulse response analysis shows that positive shocks to FinTech development and financial stability have a positive and statistically significant effect on financial inclusion over a ten-period horizon. In contrast, inflation and exchange rate shocks exert a negative impact on financial inclusion in the long run. Furthermore, the response of financial stability to shocks in FinTech development and financial inclusion is positive in the short run but turns negative in the long run, reflecting the dynamic and potentially nonlinear nature of these relationships.   <strong>Conclusion</strong> The findings of this study indicate that FinTech development can serve as an effective instrument for enhancing financial inclusion in developing countries without constituting a serious long-term threat to financial stability. Nevertheless, the impact of FinTech on financial stability is dynamic and requires continuous monitoring, as potential risks may emerge over time. Therefore, appropriate regulatory frameworks and prudent supervisory policies are essential to mitigate financial risks while maximizing the benefits of FinTech. Overall, the results emphasize the importance of designing smart and adaptive regulatory policies to harness the advantages of FinTech in promoting financial inclusion while safeguarding financial stability in developing economies. <strong>Objective</strong> With the rapid expansion of digital financial services, FinTech has emerged as one of the key instruments for increasing access to formal financial services among different segments of society. However, alongside the positive effects of FinTech on the expansion of financial inclusion, concerns have been raised regarding its potential implications for financial stability, systemic risk, and the sustainability of the banking system. Accordingly, this study seeks to simultaneously investigate the dynamic and causal relationships among FinTech development, financial inclusion, and financial stability within a dynamic econometric framework. Its main focus is to identify the direction and magnitude of the effects of FinTech on financial inclusion and financial stability, to determine whether the development of digital financial services can improve the performance of the financial system in developing countries without generating structural risks.  <strong>Methods</strong> This study employs panel data from 38 developing countries over the period 2004–2023. To capture the dynamic and endogenous relationships among the variables, a Panel Vector Autoregression (PVAR) model is utilized. Prior to model estimation, panel unit root tests are conducted, and the results confirm that all variables are stationary at the 5% significance level. Based on standard information criteria, the optimal lag length of the PVAR model is selected as one period. The financial stability is measured using the banking Z-score, which is a widely accepted indicator of banking system resilience. Financial inclusion is constructed through Principal Component Analysis (PCA) using variables such as the number of bank branches, automated teller machines (ATMs), bank loans, and bank deposits. The FinTech index was measured using the extent of mobile-based digital payment utilization, reflecting the diffusion of digital financial technologies. To analyze the dynamic interactions and causal linkages among the variables, Granger causality tests, impulse response functions, and forecast error variance decomposition are employed.   <strong>Results</strong> The results of the stability tests confirm that the estimated PVAR model is structurally stable. The Granger causality tests reveal a unidirectional causal relationship running from FinTech development to financial inclusion, indicating that the expansion of digital financial services plays a significant role in enhancing access to financial services. Moreover, a bidirectional causal relationship is identified between FinTech and financial stability, suggesting a mutual interaction between digital financial innovation and the resilience of the financial system. Additionally, a unidirectional causal relationship from financial stability to financial inclusion is observed, highlighting the importance of a stable financial environment in facilitating broader financial access. The impulse response analysis shows that positive shocks to FinTech development and financial stability have a positive and statistically significant effect on financial inclusion over a ten-period horizon. In contrast, inflation and exchange rate shocks exert a negative impact on financial inclusion in the long run. Furthermore, the response of financial stability to shocks in FinTech development and financial inclusion is positive in the short run but turns negative in the long run, reflecting the dynamic and potentially nonlinear nature of these relationships.   <strong>Conclusion</strong> The findings of this study indicate that FinTech development can serve as an effective instrument for enhancing financial inclusion in developing countries without constituting a serious long-term threat to financial stability. Nevertheless, the impact of FinTech on financial stability is dynamic and requires continuous monitoring, as potential risks may emerge over time. Therefore, appropriate regulatory frameworks and prudent supervisory policies are essential to mitigate financial risks while maximizing the benefits of FinTech. Overall, the results emphasize the importance of designing smart and adaptive regulatory policies to harness the advantages of FinTech in promoting financial inclusion while safeguarding financial stability in developing economies. https://jfr.ut.ac.ir/article_106765_2304c7c8f94b866535e0d5af2c610408.pdf

Univrsity Of Tehran Press Financial Research Journal 1024-8153 28 1 2026 03 21 Informed Trading Probability in Exchange-traded Funds on the Tehran Stock Exchange: A Market Microstructure Approach Informed Trading Probability in Exchange-traded Funds on the Tehran Stock Exchange: A Market Microstructure Approach 300 326 105502 10.22059/frj.2026.403269.1007796 FA Ali Namaki Associate Prof., Department of Financial Engineering, Faculty of Accounting and Financial Sciences, College of Management, University of Tehran, Tehran, Iran. 0000-0003-4167-6472 Reza Eyvazlou Assistant Prof., Faculty of Accounting and Financial Sciences, College of Management, University of Tehran, Tehran, Iran Mohsen Sayar Ph.D. Candidate, Department of Financial Engineering, Kish International Campus, University of Tehran, Tehran, Iran. Journal Article 2025 09 28 <strong>Objective</strong><br />Informed trading, as a key factor undermining market transparency and efficiency, plays a pivotal role in investor decision-making and risk management. Accordingly, identifying and quantifying such trading activities at the microstructure level of the market is of critical importance. Given the growing presence of exchange-traded equity funds (ETFs) in Iran’s capital market and the need for enhanced transparency and regulatory oversight, this study aims to propose a robust and reliable index for measuring information asymmetry within these financial institutions.<br /> <br /><strong>Methods</strong><br />The foundational model for estimating the extent of informed trading is the Probability of Informed Trading (PIN) model. This well-established measure in financial economics captures the likelihood of informed traders’ participation in the market and reflects the degree of information asymmetry in trading activity. Over time, the PIN model has undergone refinements to address computational inefficiencies and the complexity of multi-parameter estimation. A prominent advancement is the Volume-Synchronized Probability of Informed Trading (VPIN) model, which offers superior speed and accuracy, requires fewer parameter estimations, and incorporates a volume-based framework that enables continuous updating. By addressing the primary limitation of the original model—its disregard for trading volume—VPIN facilitates a more precise assessment of informed trading probabilities. The dataset used in this study consists of intraday price, time, and volume data for 92 equity exchange-traded funds listed on the Tehran Stock Exchange, covering the period from April 2019 to March 2025. To compute the VPIN index, real-time data on trade time, volume, and price were collected and processed, yielding VPIN values for each fund across the specified timeframe. Additionally, the funds were categorized based on variables such as assets under management and the industry sector of investment (sectoral ETFs), allowing for an examination of structural factors influencing informed trading levels.<br /> <br /><strong>Results</strong><br />The results reveal significant variation in the probability of informed trading across the studied funds. Specifically, funds with larger assets under management (AUM) exhibited lower average VPIN values compared to smaller funds. Furthermore, sector-specific funds investing in different industries display varying VPIN levels. Moreover, funds investing in less transparent and specialized industries demonstrated higher average VPIN scores. These differences suggest that structural factors—including fund size, the nature of the target industry, and investor trading behavior— significantly influence the extent of informed trading.<br /> <br /><strong>Conclusion</strong><br />This study contributes to the literature by introducing an analytical framework grounded in the VPIN methodology to identify and quantify information asymmetry among ETFs operating in Iran’s capital market. The findings—encompassing VPIN monitoring across sectoral, small, medium, and large equity funds—underscore the necessity for regulatory authorities and policymakers to consider variables such as fund structure, industry focus, and trading volume in efforts to enhance market transparency and mitigate informational inequality. Furthermore, the VPIN index proves to be an effective analytical tool for modeling informational risk in ETFs and offers a foundation for developing intelligent regulatory monitoring systems. Ultimately, the insights derived from this research hold practical relevance for investor decision-making, strategic trading design, and the advancement of capital market oversight policies. <strong>Objective</strong><br />Informed trading, as a key factor undermining market transparency and efficiency, plays a pivotal role in investor decision-making and risk management. Accordingly, identifying and quantifying such trading activities at the microstructure level of the market is of critical importance. Given the growing presence of exchange-traded equity funds (ETFs) in Iran’s capital market and the need for enhanced transparency and regulatory oversight, this study aims to propose a robust and reliable index for measuring information asymmetry within these financial institutions.<br /> <br /><strong>Methods</strong><br />The foundational model for estimating the extent of informed trading is the Probability of Informed Trading (PIN) model. This well-established measure in financial economics captures the likelihood of informed traders’ participation in the market and reflects the degree of information asymmetry in trading activity. Over time, the PIN model has undergone refinements to address computational inefficiencies and the complexity of multi-parameter estimation. A prominent advancement is the Volume-Synchronized Probability of Informed Trading (VPIN) model, which offers superior speed and accuracy, requires fewer parameter estimations, and incorporates a volume-based framework that enables continuous updating. By addressing the primary limitation of the original model—its disregard for trading volume—VPIN facilitates a more precise assessment of informed trading probabilities. The dataset used in this study consists of intraday price, time, and volume data for 92 equity exchange-traded funds listed on the Tehran Stock Exchange, covering the period from April 2019 to March 2025. To compute the VPIN index, real-time data on trade time, volume, and price were collected and processed, yielding VPIN values for each fund across the specified timeframe. Additionally, the funds were categorized based on variables such as assets under management and the industry sector of investment (sectoral ETFs), allowing for an examination of structural factors influencing informed trading levels.<br /> <br /><strong>Results</strong><br />The results reveal significant variation in the probability of informed trading across the studied funds. Specifically, funds with larger assets under management (AUM) exhibited lower average VPIN values compared to smaller funds. Furthermore, sector-specific funds investing in different industries display varying VPIN levels. Moreover, funds investing in less transparent and specialized industries demonstrated higher average VPIN scores. These differences suggest that structural factors—including fund size, the nature of the target industry, and investor trading behavior— significantly influence the extent of informed trading.<br /> <br /><strong>Conclusion</strong><br />This study contributes to the literature by introducing an analytical framework grounded in the VPIN methodology to identify and quantify information asymmetry among ETFs operating in Iran’s capital market. The findings—encompassing VPIN monitoring across sectoral, small, medium, and large equity funds—underscore the necessity for regulatory authorities and policymakers to consider variables such as fund structure, industry focus, and trading volume in efforts to enhance market transparency and mitigate informational inequality. Furthermore, the VPIN index proves to be an effective analytical tool for modeling informational risk in ETFs and offers a foundation for developing intelligent regulatory monitoring systems. Ultimately, the insights derived from this research hold practical relevance for investor decision-making, strategic trading design, and the advancement of capital market oversight policies. https://jfr.ut.ac.ir/article_105502_ce0a8723c4e4a0fba874945aa5adc3ae.pdf