https://jfr.ut.ac.ir/ Financial Research Journal en daily 1 Sat, 22 Nov 2025 00:00:00 +0330 Sat, 22 Nov 2025 00:00:00 +0330 https://jfr.ut.ac.ir/article_105133.html Objective In the field of stock portfolio optimization, various risk measures have been used under the title of "classic risk measures" in previous studies. The correct estimation of portfolio risk and its management plays a crucial role in preserving investors' wealth. Failure to accurately estimate portfolio risk has been one of the major causes of financial crises in recent decades. According to prospect theory, investors exhibit a heightened sensitivity to losses and thus assign more weight to them. This leads to the underestimation of risk by common risk measures, such as Value at Risk, Expected Shortfall, Variance, and others. This study examines stock portfolio optimization with a behavioral finance approach under loss aversion conditions in the Tehran Stock Exchange, comparing the performance of this approach with 11 other risk measures in the classic financial field by considering different time horizons (short-term, medium-term, and long-term) and portfolios of varying sizes (small, medium, and large).   Methods The performance of the risk measure based on loss aversion, developed from Fulga's (2016) model, is evaluated alongside 11 other classic risk criteria in portfolio optimization, using daily data from 178 stock companies. Non-parametric tests (Kruskal-Wallis test and one-sided Wilcoxon test) are applied for evaluation. Instead of limiting the comparison to portfolio returns and risk, this study utilizes 26 different criteria for a comprehensive performance comparison. Historical simulation methods are used to calculate the risk measures. The reason for using this method is twofold: first, all the risk measures used in this research can be calculated using this approach; and second, since this method makes no assumptions about data distribution, it is superior to other risk calculation methods. Fulga's model, based on the downside of portfolio return distributions, specifically simulates loss-averse behavior and offers a more accurate risk evaluation, incorporating criteria like Conditional Value at Risk (CVaR) and the level of negative deviation from a reference point (LPM).   Results Depending on the threshold value or reference point in prospect theory, the performance of the loss aversion-based risk measure can vary. When the threshold is zero or greater (e.g., 0%, +2%, and +4%), the loss aversion-based criterion demonstrates significantly better performance in portfolio optimization compared to other risk measures. However, when the threshold value is negative (e.g., -2% and -4%), no particular advantage is observed over other criteria.   Conclusion In situations where it is possible to estimate parameters for the loss aversion coefficient and the threshold value, the performance of the behaviorally-based risk measure (under conditions of loss aversion) in portfolio optimization proves to be superior to other classical financial measures in real market conditions (where the threshold value is zero or higher). Additionally, as the threshold value increases above zero, the results of this study indicate that the "standard deviation" measure demonstrates better performance compared to other classical risk measures. https://jfr.ut.ac.ir/article_105167.html Objective Sovereign debt default episodes have long been an integral part of economic history and have consistently attracted attention in the literature on financial crises, particularly in analyses of the determinants of such events. A review of past sovereign default cases across economies reveals that these episodes have often coincided with banking or currency crises; indeed, in some instances, all three crises have occurred simultaneously within the same period. This study examines the phenomenon of triple crises in Iran’s economy, providing empirical evidence on the relationship between government debt crises and currency and banking crises. It further explores the impact of these three crises—currency, banking, and sovereign debt—on the government’s budget deficit. The present research contributes to the crisis literature in several ways. First, it is the first domestic study to investigate the relationship between the triple crises and the government budget deficit. Second, it seeks to identify which type of crisis exacerbates the budget deficit at different levels of crisis intensity. Third, it aims to determine the mode and direction of causality between the triple crises and the budget deficit across varying degrees of severity. Identifying the effects and causal direction of the triple crises on the budget deficit will help enhance the government budget’s flexibility and responsiveness to changing economic conditions. In other words, establishing the causal pathway from the triple crises toward the budget deficit can serve as an early warning system for policymakers. Accordingly, the primary aim of this study is to model the relationship between the triple crises and the budget deficit across different levels of crisis intensity over time.   Methods This study is applied in nature and exploratory in purpose. Data were collected through documentary and library methods. The research covers 32 years, from 1991 to 2022 (1370–1401 in the Iranian calendar). MATLAB and R software were employed for model estimation. Using the TVP-FAVAR approach, the study examines the effects of the triple crises on the budget deficit across three time horizons—short-term, medium-term, and long-term. In addition, the TVP-Quantile VAR method was applied to analyze the causal relationships between the triple crises and the budget deficit. The research variables and their measurements are as follows: budget deficit (the difference between government expenditures and revenues), sovereign debt crisis (measured by the ratio of total public debt to gross domestic product), currency crisis (measured by the exchange market pressure index), and banking crisis (measured by the money market pressure index).   Results Using the estimation of the TVP-FAVAR model in MATLAB, the results of the impulse response analysis of the model’s variables on the fiscal deficit over time are presented. In TVP-FAVAR models, the shocks of explanatory variables exert a statistically significant impact on the dependent variable (in this study, the triple crises) only when the impulse response of the corresponding non-fragile variable lies below or at the equilibrium point (where the equilibrium value equals zero). The results of the TVP-FAVAR model indicate that the triple crises have collectively contributed to an increase in the fiscal deficit over time. Specifically, the sovereign debt crisis shows the strongest impact, with an average annual effect coefficient of 1.36%, followed by the banking crisis with 0.7813%, and the currency crisis with 0.049%. These findings demonstrate that, among the three crises, the sovereign debt crisis exerts the greatest influence on the government’s budget deficit.   Conclusion To examine the relationship among the variables, the TVP-Quantile VAR approach was employed. The results indicate that under conditions of low uncertainty (5th percentile of uncertainty), the direction of causality runs from the triple crises toward the fiscal deficit. Under moderate uncertainty levels (50th percentile), the direction of causality flows from the sovereign debt and currency crises toward the fiscal deficit and the banking crisis. However, under high uncertainty levels (95th percentile), the direction of causality reverses, flowing from the fiscal deficit and sovereign debt crisis toward the currency and banking crises. The TVP-FAVAR model further revealed three temporal decomposition levels: short-term, medium-term, and long-term. Subsequently, the co-movement and correlation among the triple crises were analyzed across these different time horizons. The findings suggest that as the time horizon extends toward the long term, the interdependence between the triple crises and the fiscal deficit strengthens, with this dependency and co-movement being particularly pronounced in the fluctuations of the sovereign debt crisis. Moreover, the results demonstrate a positive correlation between exchange rate fluctuations and the government budget, which intensifies over the long term. Accordingly, exchange rate volatility can influence government debt to the banking system by affecting changes in current government expenditures. https://jfr.ut.ac.ir/article_105168.html Objective One of the most important topics in economics is the impact of the stock market on economic growth and development. By providing liquidity to companies, the stock market facilitates long-term investment in projects, thereby promoting economic growth. Along with the role of an efficient stock market in economic progress and development, economic stability and low inflation are also considered very important factors for economic development. Although these two factors influence economic development, they also interact with each other. While inflation disrupts investment and economic growth, it can stimulate the stock market and increase its yield in line with inflation. In addition, inflation can weaken the stock market by strengthening other asset markets such as currency, housing, and gold. Therefore, studying inflation's effects on the stock market can be very useful in understanding the role played by the stock market in Iran's economy. This research is to investigate the impacts of inflation on the stock market and to determine the debate under which inflationary regime the stock market has the most returns. The analysis of Iran's inflation shows that in the 1390s, various economic and political shocks caused different inflationary regimes to prevail in the country, each having different effects on the stock market.   Methods To investigate the effects of inflation on the performance of the Tehran Stock Exchange market in the period of 1390-1400, in the form of monthly data, this study has used two methods, Markov-Switching Vector Autoregressive and time-varying parameters. The main advantage of both methods used is their non-linearity. Comparing the results of two methods can guide economic officials in making the right policy.   Results The results of Markov switching show that extremely high inflation first causes a sharp increase in stock market returns, but its lags severely weaken returns. Also, low inflation, despite the low impact factor compared to high inflation regimes, generally leads to the strengthening of the stock market. In order to check the robustness of the results, the present study has used the time variable parameter method. The results of this method indicate that in the period of 1392-1395, when the inflation rate was low and stable, the stock market yield had a high positive reaction compared to other periods.   Conclusion This research, using these two methods, shows that low and stable inflation has a significant impact on strengthening stock market returns. Therefore, the important reason for the low efficiency of the stock market of the Iranian economy can be mentioned as a result of severe inflation. Just as inflation has caused economic instability, it has slowed economic growth by disrupting investment, fostering speculation, and turning the stock market into an unproductive arena focused on short-term profits. Therefore, controlling inflation is necessary to correct and rationalize market efficiency. https://jfr.ut.ac.ir/article_102334.html ObjectiveSystemic risk, with contagion between different parts of the market, causes financial crises and instability in the economy; therefore, it is necessary to recognize, measure, control, and combat systemic risk. Since financial institutions have extensive connections with all institutions and can transfer and spread risk, in the financial literature, the systemic risk of the financial sector has a special place, and most of the research conducted on the systemic risk field has been devoted to the financial industry, while the non-financial sector in this field has received less attention. On the other hand, non-financial corporations (NFCs) are also connected to each other and to the financial sector, and as a result, NFCs have potentially systemic importance. Therefore, considering the importance of systemic risk in the economy's stability and the position of the non-financial sector in the society's economy, the systemic importance of non-financial institutions and industries should be evaluated and explained. Also, knowing the drivers of systemic risk in NFCs helps to identify important systemic NFCs, make economic decisions, and formulate appropriate rules. On the other hand, in portfolio risk management decisions, the individual risk criteria of institutions are often used, and risk transmission between institutions has not been adequately considered. Therefore, for better portfolio risk management, systemic risk should also be included in decisions. According to the mentioned cases, the current research aims to comprehensively examine the systemic risk of the non-financial sector from four different perspectives. For this purpose, the systemic importance of non-financial corporations was investigated, and the corporations were ranked in terms of systemic risk. Also, the systemic importance of various industries in Iran's capital market was investigated, and the ranking of all industries in terms of systemic risk was determined. In addition, firm-level characteristics related to the systemic risk of non-financial corporations were identified. And at the end, the application of systemic risk in portfolio risk management by asset managers, retail investors, and policymakers was explained. MethodsTo measure systemic risk, the Marginal Expected Shortfall criterion (MES) was used and analyses were performed at the firm, industry, and random portfolio levels. For this purpose, 284 financial and non-financial corporations, in addition to the financial industry and non-financial industries, and three separate portfolio groups, with each portfolio group consisting of 100 random portfolios, were examined from 2008 to 2022. The systemic importance of non-financial corporations and industries was determined by comparing the median of MES between non-financial corporations and financial corporations, as well as non-financial industries and the financial industry. Also, by ranking, the most important and least important corporations and industries were determined from a systemic perspective. Additionally, a new estimation method known as 'random effects within-between' (REWB) regression was employed to understand firm-level characteristics associated with MES in non-financial corporations. In REWB regressions, the cross-sectional (between) and longitudinal (within) relationships between each firm characteristic and systemic risk are estimated simultaneously. Finally, to evaluate the effect of the systemic importance of corporations and the weight assigned to systemically important corporations on portfolio risk, the median of downside risk measures was compared between different portfolio groups. ResultsThe findings of the research confirmed the systemic importance of non-financial corporations and non-financial industries. Also, separate analysis results on large corporations in the capital market indicate that the systemic importance of large non-financial corporations is almost equal to the systemic importance of large financial corporations. In addition, during the research period, Isfahan Mobarakeh Steel Company and Damavand Mining Company were found to have the highest and lowest ranks of systemic importance, respectively, among companies. The rubber and plastic industry and the pharmaceutical industry were also identified as having the highest and lowest ranks of systemic importance, respectively. In addition, firm-level characteristics of Beta, Value at Risk, Accounts Receivable, Size, Cash Holding, Dividend, Asset Tangibility, and External Financial Dependence are directly and significantly related to MES. Furthermore, the systemic importance of corporations and the weight assigned to systemically important corporations significantly affect the downside risk criteria of the investment portfolio. ConclusionTo maintain economic stability, the systemic risk of the non-financial sector should be considered, as well as that of the financial sector. For example, knowing the drivers of systemic risk in non-financial companies can help to control systemic risk. Also, retail investors, portfolio managers, and regulators of mutual funds, by including the systemic risk of all companies in their decisions, can achieve better portfolio risk management. https://jfr.ut.ac.ir/article_105171.html Objective This research examines and enhances operational risk management in banks using machine learning algorithms. Effective management of operational risk, which arises from internal or external failures in processes, systems, and personnel, is crucial due to its significant impact on the performance and stability of banks. Its primary goal is to introduce an innovative approach to improving operational risk management in banks through machine learning algorithms. Given the importance of accurately predicting operational risks to prevent potential losses and improve decision-making processes in the banking industry, this research purposes to enhance the accuracy and efficiency of risk prediction models. The focus is on leveraging real-world banking data and evaluating machine learning algorithms to identify the most effective methods for predicting different levels of operational risk.   Methods This research employs machine learning algorithms to predict the occurrence levels of operational risks. The dataset consists of operational risk data from an Iranian bank collected from 2016 to 2023, comprising 4,213 records and 12 features. After preprocessing the data, various machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, and k-Nearest Neighbors, were utilized for training the models. The data was split into training and test sets in an 80/20 ratio and evaluated using K-fold cross-validation. Model performance was assessed based on metrics such as accuracy, precision, recall, F1-score, and the ROC-AUC curve, with the best model selected for future predictions.   Results The findings show that the use of machine learning algorithms can significantly improve the accuracy of predicting operational risks in banks. In the evaluation of different algorithms, SVM and RF showed the best performance, particularly in classifying the third class (Label 3), where the model's accuracy with the AUC metric was close to one. These results highlight the high capability of these two algorithms in accurately distinguishing between different levels of operational risk. On the other hand, LR and NB demonstrated the weakest performance and failed to predict risks effectively. Overall, the findings indicate that more powerful algorithms like SVM and RF can enhance operational risk management in banks and prevent damage resulting from poor risk management.   Conclusion The results demonstrate that machine learning algorithms can substantially enhance operational risk management in banks. In particular, advanced algorithms such as SVM and RF achieved higher accuracy in predicting operational risks and effectively identified complex and atypical patterns. These technologies, by improving efficiency and reducing the costs associated with risk management, help banks develop better strategies for mitigating operational risks. Therefore, the continuous application of these technologies can enhance banks' performance in operational risk management. https://jfr.ut.ac.ir/article_105174.html Objective Investors in financial markets encounter a diverse and extensive range of financial instruments, each with its own characteristics and benefits. These instruments enable investors to select the most suitable options based on their financial goals, risk tolerance, and expected returns. Among these factors, the role of personality and psychological traits, particularly emotional intelligence, plays a significant role in financial decision-making and investment choices. The ability to manage emotions and emotional behaviors can substantially influence the level of risk-taking and the selection of appropriate financial instruments. Additionally, investors' moods when faced with financial opportunities and challenges are a critical determinant of their risk-taking behavior. This study aims to examine the effect of emotional intelligence on investors' risk-taking behavior, with the mediating role of their mood.   Methods This study is applied in terms of its purpose and correlational in terms of its nature. Data collection was conducted using a questionnaire method. The statistical population of this study consists of investors in the Tehran Stock Exchange, from which 382 investors were voluntarily selected using a non-random sampling method and analyzed. In this study, the following questionnaires were used to measure the variables: the Emotional Intelligence Questionnaire by Salovey and Mayer (1990) with 33 items, the Panas (1988) Mood Questionnaire for investors with 20 items, and the Risk Tolerance Questionnaire for investors by Gerbel (2000) with 18 items. To confirm the validity of the questionnaires, in addition to face and content validity, construct validity was also evaluated. For assessing the reliability of the research variables, Cronbach's alpha, composite reliability, and average variance extracted (AVE) indices were used. The Cronbach's alpha and composite reliability values for all variables exceeded the 0.7 threshold, and the AVE values were above 0.5, indicating satisfactory reliability of the variables. The research hypotheses were tested using the structural equation modeling (SEM) method with Smart PLS software.   Results The measurement model was first examined to ensure that the questionnaire items appropriately measured the latent variables. Factor analysis results indicated that the proposed model's factor structure had acceptable validity. All indicators had factor loadings above 0.5, showing a strong relationship between the indicators and their respective latent variables. Based on the findings, the impact of emotional intelligence on investors' risk-taking behavior was confirmed with a path coefficient of 0.443 and a t-statistic of 8.661. The effect of temperament on investors' risk-taking was also confirmed with a path coefficient of 0.217 and a t-statistic of 4.191. Additionally, the effect of emotional intelligence on temperament was confirmed with a path coefficient of 0.534 and a t-statistic of 13.606. To investigate the mediating role of temperament in the relationship between emotional intelligence and financial risk-taking, the Sobel test was used. The Sobel test result was 3.99. To examine the predictive power of financial risk-taking by the model, the adjusted R-squared coefficient, effect size, goodness-of-fit index, and redundancy index were used. The adjusted R-squared coefficient estimated the model’s predictive power at 0.342 (strong predictive quality). The effect size of financial risk-taking from emotional intelligence and temperament was assessed at 0.215 (moderate predictive quality) and 0.051 (moderate predictive quality), respectively. The overall goodness-of-fit index for the model was 0.436 (strong fit quality), and the redundancy index for the model was 0.86 (very good).   Conclusion This study investigated the impact of emotional intelligence and mood on financial risk-taking. The results revealed that emotional intelligence has a positive and significant relationship with financial risk-taking. Individuals with higher emotional intelligence are better at identifying risks and are more inclined to take risks. Furthermore, mood has a substantial effect on financial risk-taking, particularly positive mood, which can enhance risk perception. Individuals with a positive mood perceive risks as growth opportunities and are more willing to accept them. Emotional intelligence also positively influences mood, as individuals with higher emotional intelligence exhibit a more positive mood. Finally, the findings indicate that mood acts as a mediating factor in the effect of emotional intelligence on financial risk-taking. These results can contribute to improving financial decision-making and reducing behavioral biases among investors. https://jfr.ut.ac.ir/article_101093.html Objective To score customers effectively, it is essential to establish a fair and appropriate scoring system. This system should classify customers into different categories based on credit and behavioral criteria and assign them scores aligned with their performance. Moreover, developing methods to evaluate and monitor customers over time is necessary. This study aims to identify and analyze credit and behavioral indicators to propose a model for ranking customers with unsecured small loans. Customer credit evaluation is a complex process that involves reviewing documents, analyzing financial status, and assessing the customer's payment history. A critical aspect of this evaluation is determining customers' ability, willingness, and capacity to repay loans. This research seeks to present a credit evaluation and ranking model for unsecured small loans and to estimate the probability of default within the bank’s digital banking system.   Methods The statistical population for this research includes all retail customers of the digital banking services of Khavar-e-Miyaneh Bank. A census method was used to study the customers during the fiscal years 1400–1401. The model developed by Ahmadi Koosha and colleagues (1403) was used as the basis for ranking customers with unsecured small loans. Indicators such as age, loan amount, eligible loan request amount, total loans received, score, anti-money laundering approval, gender, occupation, city, education level, loan status, and job type were identified as the initial input variables. Data analysis was conducted using fuzzy regression and was inspired by Python libraries such as SciKit-Fuzzy and NumPy, resulting in a fuzzy regression formula. To finalize the evaluation of the credit scoring and ranking model, an example involving a customer was presented.   Results The results indicate that variables such as the requested loan amount, total loans received, and occupation do not influence the repayment of installments 60 days past due. The ranking of significant variables affecting repayment within 60 days after the due date is as follows: loan settlement status, anti-money laundering status, gender, education level, age, score, job type, and city. Additionally, the model’s accuracy and predictive power were tested using a hypothetical customer, yielding a score of 0.8738. This score demonstrates that the customer is in a favorable position for repayment within 60 days past due and highlights the model's efficiency in credit risk assessment and management, which are crucial for financial institutions to ensure sustainable lending practices.   Conclusion The results indicate that variables such as the requested loan amount, total loans received, and occupation do not affect the repayment of installments 60 days past due. The ranking of significant variables influencing repayment within 60 days after the due date is as follows: loan settlement status, anti-money laundering status, gender, education level, age, score, job type, and city. Furthermore, the model’s accuracy and predictive power were tested using a hypothetical customer, yielding a score of 0.8738. This score indicates that the customer is in a favorable position for repayment within 60 days past due and demonstrates the model's effectiveness in credit risk assessment and management, which are crucial for financial institutions to ensure sustainable lending practices. https://jfr.ut.ac.ir/article_105179.html Objective Many economists share the view that monetary policies can influence the real sector of the economy in the short run, yet their disagreements center on the transmission channels involved and the relative importance of these channels. Furthermore, the effort to understand the relative significance of monetary policy transmission channels in financial markets remains a primary motivation for conducting empirical analyses of monetary policy transmission, supporting the effective management of monetary policy in many countries.   Methods The study applies a nonlinear approach based on the Gauss–Markov theorem. Linear regression models offer a broad and rich framework capable of addressing many analytical needs and research inquiries. However, linear regression is not suitable for all problems, as in certain cases the response variable and the regressors are related through a known nonlinear function. One of the most important advantages of employing nonlinear models lies in their ability to provide reliable estimates of unknown parameters in the model using relatively small datasets.   Results Past studies on monetary policy transmission mechanisms in financial markets have highlighted that monetary policy in financial markets is transmitted through multiple channels, while its effects on output and prices occur with a time lag. Notably, the share of the inflation rate channel in transmitting money to prices in the zero regime (low money growth) is greater and more persistent than in the one regime (high money growth). In other words, within the zero regime, an increase in money supply leads to a greater rise in the inflation rate, and the higher inflation rate produces more enduring effects on the price level. Given that the inflation rate channel in both regimes exhibits a negative role in transmitting money to output, it is recommended that the central bank, in order to boost output, should control other factors affecting the inflation rate and prevent sharp increases and excessive growth in inflation.   Conclusion This study reveals that the role of the inflation rate channel in the monetary transmission mechanism indicates that increasing the money supply via the inflation rate channel in the zero regime has had no role in transmitting money to output, whereas in the one regime, the inflation rate channel has had a significant share in transmitting money to output. In this latter case, changes in the money supply through the inflation rate channel have led to reductions in output. The monetary policy transmission mechanism in financial markets pertains to the effects of monetary policy on output and prices in the financial market and remains the chief motivation for empirical analysis in support of effective monetary policy management in financial markets across numerous countries, with the aim of limiting its negative impacts on output.     https://jfr.ut.ac.ir/article_103246.html Objective Since its inception in 2009, Bitcoin has experienced tremendous growth in terms of public acceptance as the oldest cryptocurrency. However, transitioning from an experimental digital currency to a mainstream payment network introduced new challenges related to scalability and capacity. This study investigates how bitcoin transaction fees respond to financial and technical factors within the network over different periods of its lifespan. The investigation provides novel insights into the dynamics of a decentralized currency and the maturity of a payment network.   Methods Bitcoin blockchain data from 2009 to 2023 was categorized into three distinct periods for analysis: the Initial Period (2009-2014), Speculation Period (2014-2018), and Scalability Challenge Period (2018-2023). Autoregressive Distributed Lag (ARDL) modeling was used to analyze short-term and long-term relationships. The dependent variable was the transaction fee, and the explanatory variables included the bitcoin price, average transaction value, average block size, network difficulty, and transaction volume.   Results The results showed that during the Initial Period, in the short term, the price and the previous day’s fee had a significant effect on the transaction fee, and in the long term, the price also affected the transaction fee. During the Speculation Period, the transaction volume, block size, and the previous day’s fee in the short term, as well as network difficulty in the long term, had a significant effect on the transaction fee. During the Scalability Challenge Period, in the short term, the previous day’s transaction fee, Bitcoin price, the average value of transactions in Bitcoin, block size, network difficulty, and transaction volume had significant effects on the transaction fee, while in the long term, network difficulty and block size remained significant. Moreover, during the Scalability Challenge Period, the previous day’s fee had a strong effect on the current day’s fee, creating a kind of stickiness that persisted until the end of the period. Overall, fees were stabilized over time as users and miners learned the factors influencing them, optimizing their behavior around block limits and mining reward incentives. During the Initial Period, transaction fees were highly volatile due to Bitcoin’s nascency and limited usage. As cryptocurrencies increasingly became utilized as a payment mechanism in the Speculation Period, the technical parameters affecting block sizes and processing capacity caused fees to be more reflective of demand on the network. The emergence of scalability constraints facing the blockchain in the Scalability Challenge Period has led to linking the dynamics of fees to more metrics that act as proxies for the level of activity and network usage.   Conclusion As bitcoin has become a payment network in the new concept, the fees have been aligned with the demand and motivations of network participants during the scaling challenge period, instead of randomly fluctuating during the Initial Period. The separation of bitcoin network data into three time periods provides a new perspective on how decentralized networks work and the factors affecting the fee market in different periods of network maturity. https://jfr.ut.ac.ir/article_105807.html Objective While insurance companies operate with profit-maximization objectives similar to those of other economic sectors, they possess distinct structural characteristics in financial intermediation. Owing to their role in risk transfer and the resulting concentration of risk, their operational sustainability is uniquely vulnerable. Traditional financial solvency ratios, although effective in assessing capital adequacy, do not necessarily ensure short-term liquidity resilience during catastrophic, fat-tailed loss events. Accordingly, this study aims to develop a model for assessing the resilience of insurance companies in managing claims risk under both normal and critical scenarios, without reliance on restrictive prior assumptions regarding loss distributions.       Methods This study employs an applied, descriptive-analytical research design, focusing on the analysis of historical data from the insurance sector. The statistical population comprises all general insurance companies listed on the Tehran Stock Exchange (TSE) over the period from 2014 to 2024. Sample selection was contingent upon data accessibility and the availability of quarterly financial statements. The dataset incorporates quarterly financial reports and internal corporate records, specifically the total number of issued policies, total written and collected premiums, and the aggregate frequency and severity of paid claims on a quarterly basis. To mitigate inflationary distortions, monetary variables—specifically claim amounts and asset values—were adjusted for inflation using the Consumer Price Index (CPI). Subsequently, a logarithmic differencing filter was applied to detrend the data and ensure stationarity. In the final phase, optimal statistical distributions were fitted to the empirical data. The financial health of insurers was then evaluated by defining and calculating three novel metrics: the Risk Status Index, the Risk Coverage Ratio (Normal Condition), and the Risk Coverage Ratio (Critical Condition).   Results The results demonstrate that the proposed indicators exhibit significantly greater explanatory power than conventional regulatory solvency ratios. The findings indicate that even entities maintaining a standard regulatory solvency ratio (Level 1) may experience “hidden insolvency” as a result of inefficiencies in managing asset quality and liquidity.   Conclusion The proposed model establishes that liquidity risk management must be integrated as an essential complement to traditional solvency frameworks. Implementation of this model enables supervisory authorities to identify early warning signals prior to the onset of irreversible financial distress. https://jfr.ut.ac.ir/article_102895.html Objective: Objective: Financial literacy offers numerous benefits for individuals, businesses, society, and the planet in promoting sustainable finance. For individuals, it helps align their personal values and goals with financial decisions, diversify their portfolios, reduce their environmental footprint, and contribute to positive social change. For businesses, it facilitates access to new funding sources, enhances reputation, improves risk management, and creates long-term value. For society, it strengthens social inclusion, reduces poverty and inequality, and supports human rights and democracy. This study aims to examine the drivers of financial sustainability within the framework of financial literacy enhancement. The author argues that to make informed investment decisions about financial products with sustainable characteristics, private investors must possess a high level of financial literacy and sustainable financial literacy. Methodology: This research is applied in terms of its objective and descriptive-analytical in terms of data collection. Information was gathered through both documentary-library and field methods. Financial literacy components were assessed using eleven dimensions and analyzed with MICMAC and Scenario Wizard software. The model analyzes the interrelationships between variables, enabling prioritization of influential factors and providing practical strategies to strengthen financial sustainability. Key factors and probable scenarios for financial sustainability within the framework of enhanced financial literacy were identified. The study utilized an expert-based sample of 20 participants, selected using the snowball sampling method. Findings: Eleven primary components of financial literacy were identified for managers, including understanding and analyzing financial statements, familiarity with insurance and labor laws, tax regulations, and commercial law. These components play a critical role in financial decision-making, reducing legal risks, and improving organizational performance. Tax regulations, as a framework for calculating, paying, and managing direct and indirect taxes, are influential in structuring financial activities and preventing issues arising from tax non-compliance. Moreover, commercial law, encompassing regulations on company formation and management, commercial contracts, and bankruptcy, serves as an essential legal tool for managing business relationships and activities. These components provide the necessary infrastructure for optimizing financial and legal processes, mitigating risks, and ensuring organizational compliance with legal requirements. Awareness and mastery of these topics lead to improved overall organizational performance and confidence in managerial decision-making. Conclusion: High financial literacy directly correlates with intelligent financial behaviors, such as optimal resource utilization and cost reduction. Societies that have widely implemented financial literacy programs have experienced significant improvements in welfare indices and financial sustainability. Managers must familiarize themselves with the structure and conditions for participating in international markets. Understanding the necessary conditions and permits, such as commercial cards, customs procedures, and the concept of smuggling, is among the essential knowledge required for managers. Additionally, knowledge of the laws governing free trade zones or special economic zones is crucial for informed managerial decisions, highlighting the necessity of awareness in this area. https://jfr.ut.ac.ir/article_103114.html Objective: This study aims to measure the efficiency of the Iranian capital market and investigate the ability of hybrid machine learning models to predict the direction of the Tehran Stock Exchange's total index. Also, evaluating the importance of factors affecting the index's predictability and explainability in machine learning models is another objective of this study. Method: In order to analyze market efficiency at two weak and semi-strong levels, data related to the Tehran Stock Exchange's total index was used in the five-year period from 2019 to 2024. The proposed hybrid model includes the boosted gradient model (XGBoost), which has been improved by optimizing hyperparameters through the Genetic Algorithm (GA). The performance of this hybrid model has been statistically compared with other machine learning algorithms including boosted gradient, random forest, support vector machine, and logistic regression. Also, to increase the explainability of the model and analyze the importance of input variables in predicting the direction of the index, the SHAP method has been used. Findings: The results showed that the XGBoost-GA model performed statistically better than other comparative models with an accuracy of 84% in predicting the direction of the Tehran Stock Exchange index. Comparing the results at different levels of market efficiency showed that at the semi-strong level, adding fundamental variables to the forecasting model improved the accuracy, which indicates the effect of fundamental information on predicting the direction of the index and, as a result, market inefficiency at this level. Also, at the weak level, the machine learning model based on technical data performed better than the random model, which is also an indication of market inefficiency at this level. In addition, the explainability analysis of the model using SHAP showed that the effect of variables on predicting the direction of the index varies depending on the type of input data. In the purely technical model, factors related to price behavior and short-term fluctuations such as the relative strength index (RSI), trading volume, and moving average divergence and convergence played a key role. In contrast, in the hybrid model that included fundamental and technical data, in addition to technical variables, factors such as real and legal liquidity inflows, gold prices, and financial indicators of companies such as return on assets (RoA) and return on equity (RoE) had a significant impact. Conclusion: The results of the study show that the Iranian capital market is inefficient at both weak and semi-strong levels, and the predictability of the aggregate index is possible using technical and fundamental data. The analysis of the importance of input variables shows that some technical, fundamental, and macroeconomic indicators play a more important role in predicting market behavior, which can help make more informed investment decisions and better understand the behavior of machine learning models in predicting financial time series. Keywords: Market efficiency theory, genetic algorithm, machine learning explainability, extreme gradient boosting, Tehran Stock Exchange aggregate index. Send feedback https://jfr.ut.ac.ir/article_103240.html Banks play a critical role in supporting economies and financial markets through the allocation of resources. They act as financial intermediaries by bridging gaps between savers and borrowers and simultaneously serve as liquidity providers by managing the maturity structure of their balance sheet items. This dual functionality positions banks as pivotal institutions for ensuring the smooth functioning of economic systems. Financing the economy is among the primary socio-economic objectives of the banking sector. Achieving this goal requires banks to access a diverse range of financial resources, enabling them to support economic activities, manage credit, and service their obligations, thereby maintaining stability within the broader banking system. Bank financing involves generating financial resources to sustain banking operations and economic activities. According to the Asian Development Bank, financing encompasses various sources such as deposits, loans, payment services, and shareholder contributions. These resources empower banks to convert debts into liquid assets, effectively creating liquidity that is indispensable for economic operations. This capability underscores the importance of liquidity creation in ensuring resilience within financial systems. Additionally, the market power of banks, defined as their ability to attract customers and maintain competitive advantages, is closely linked to their financing structures. Banks leverage managerial and marketing strategies to secure market shares and expand their client bases, emphasizing the interconnectedness of financing and market dynamics. Through liquidity creation and risk transformation, banks play an irreplaceable role in stabilizing economies and fostering growth. However, previous research has predominantly focused on risk assessment, often overlooking the interaction between financing, market power, and liquidity creation. To address this gap, the present study explores the relationships among these factors, aiming to provide insights into their influence on banking operations and financial stability. To measure market power, the study utilized the Herfindahl-Hirschman Index (HHI), a well-established indicator of market concentration and competition. Bank financing was also analyzed using a modified HHI designed to capture the diversity of funding components, such as interbank borrowings, customer deposits, and issued securities. Liquidity creation was quantified using the index developed by Berger and Bouwman (2009), which categorizes balance sheet items based on their liquidity profiles. Data for this research were obtained from the financial statements of 10 publicly listed banks in Iran, covering the period from 2015 to 2022. Multivariate regression models employing pooled data techniques were applied to test the hypotheses. The findings reveal that diversified and efficient bank financing significantly enhances market power. Banks with diverse funding structures exhibit greater resilience and competitiveness, underlining the importance of effective financial management in optimizing market position. Furthermore, the study demonstrates that liquidity creation amplifies the positive relationship between bank financing and market power. As banks generate more liquidity, they strengthen their ability to attract deposits and improve their operational efficiency, creating a feedback loop that reinforces market dominance. This research underscores the need for policymakers to consider the interplay between financing structures, liquidity creation, and market power in their strategies. By fostering a deeper understanding of these dynamics, the study contributes to the development of policies aimed at enhancing banking efficiency and ensuring financial stability. https://jfr.ut.ac.ir/article_103309.html Objective The existing theories regarding the capital structure show that information asymmetry is an important factor for adjusting the target leverage. The signaling theory about capital structure shows that the stock market reacts positively (negatively) to the announcement of debt (stocks). In addition, the theory of dynamic balance allows companies to consider the balance between the financial structure below the target leverage limit and the leverage adjustment costs. Therefore, according to this theory, companies with higher transaction costs tend to adjust their leverage ratios at a slower rate than their goals. In this article, we have examined whether the risk of falling stock prices can affect the decision-making process regarding the adjustment of financial leverage. Therefore, it is expected that with the increase in the risk of falling stock prices, the tendency of companies to adjust the leverage will decrease. Methods The available population was selected based on 4 selection criteria from the Tehran Stock Exchange, and the data of these 143 companies were collected during 2013 to 2024, and the research models were estimated based on multivariate regression, taking into account the fixed effects of year and industry. Results The risk of future price fall has a negative effect on the speed of leverage adjustment and the leverage level of companies cannot adjust this relationship. Conclusion Two interesting results may shed light on how dynamic capital structure decisions are made. First, the empirical results of the present study show that companies that are more exposed to the risk of falling stock prices adjust their leverage ratios at a slower rate than their target leverage ratio. In the explanation of this result, it can be stated that the companies that are more exposed to the risk of collapse face more transaction costs in adjusting their financial leverage. Recent evidence regarding stock price crash risk shows that stock price crash risk is related to information asymmetry. Therefore, according to the dynamic balance theory, companies with higher transaction costs have more tolerance for choosing levers less than the maximum and have a slower speed to reach their target leverage. Second, the experimental results of the present study showed that the effect of exposure to the risk of collapse on the speed of adjustment of financial leverage does not depend on the level of actual financial leverage. According to the signaling theory about the capital structure, it is predicted that the stock price will increase (decrease) after the announcement of debt (shares). Therefore, in companies with less leverage, with the increase in the risk of falling stock prices, the speed of lever adjustment decreases ; Because they usually need to issue shares. On the other hand, for companies with more leverage, this effect is less, because issuing debt can help them hide bad news. Therefore, the expectations of the researchers regarding the role of adjustment of financial leverage of companies, on the relationship between the risk of falling stock prices and the speed of adjustment of financial leverage, were not confirmed. Among the possible reasons for this result, we can mention the weak efficiency of the Iranian capital market, the significant volume of transactions of new investors entering the market, and the low level of financial leverage of the sample companies. https://jfr.ut.ac.ir/article_103546.html 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. 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. There exists 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. 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. 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. https://jfr.ut.ac.ir/article_104067.html Objective: The main objective of this study is to analyze feedback behaviors in exchange-traded funds (ETFs) on the Tehran Stock Exchange, with emphasis on the impact of price deviations such as premiums and price Discount. This study examines how investors react to deviations in the price of fund units from the net asset value (NAV) and the impact of these deviations on the formation of positive and negative feedback behaviors. This study attempts to demonstrate how price deviations may affect investors’ trading decisions and cause the market to experience price fluctuations. In this regard, this study analyzes how investors react to different price conditions and return fluctuations, and examines their relationship with feedback behavior. Method: This study is of an applied type and aims to solve practical problems in the capital market that can be effective in the decision-making of investors and financial analysts. In terms of methodology, this research is descriptive-correlational and examines the relationships between the variables. The statistical population of this study includes all stock funds traded on the Tehran Stock Exchange during the period 2014-2024. After selecting 13 funds with superior performance during this period, the daily data were collected. The required information was extracted from the Iranian FIP site, which is a reliable source of Iranian capital market data. The models used in this study are based on the framework of Sentana and Wadhwani’s (1992) model, in which two types of traders, rational speculators and feedback traders, are considered. This model examines the effects of price deviations on investor behavior. In addition, the Liung-Box test (1978) was used to analyze lags in fund returns. In addition, the Chau, Deesomsak, & Lau (2011) model, which is an empirical version of the Santana and Vadwani model, was used to analyze the effect of price premiums and discounts on the feedback trading behavior in ETFs. Findings: The results show that price deviations have a significant effect on ETF trading behavior. In the case of price Premium, where the price of funds is higher than the NAV, positive feedback behavior is observed. This situation increases investors’ willingness to buy and prices. In other words, when the price of the fund exceeds the NAV, investors are more inclined to buy, and as a result, prices increase. This situation creates arbitrage opportunities and enhances the positive market fluctuations. In contrast, in the case of price Discount, where the price of the fund is lower than the NAV, negative feedback transactions are observed. In this case, investors are more inclined to sell funds and their confidence in the intrinsic value of the funds decreases. This reaction leads to a decrease in prices and an increase in negative fluctuations. Overall, the results indicate a significant effect of price Discount and Premium prices on the intensity of feedback behaviors in the market. In addition, the GARCH model shows that the volatility of fund returns significantly depends on price deviations and market conditions. Conclusion: This study shows that feedback trading in ETFs is affected by Premium and Discount. Price Premium increases investors' willingness to buy and price Discount increases their willingness to sell. These results indicate the significant role of price deviation from intrinsic value in the formation of investors' feedback behaviors. These findings suggest that investment fund managers adopt strategies to control price deviations and reduce severe market fluctuations. Additionally, the use of risk management mechanisms and increased information transparency can help improve fund performance and strengthen investor confidence. https://jfr.ut.ac.ir/article_104068.html Objective: This research aims to develop a novel model based on the Gradient Boosting algorithm and Transformer architecture for accurately predicting corporate valuation ratios at the time of Initial Public Offering (IPO) in the Iranian market. Contrary to traditional approaches focusing on first-day returns, this study evaluates the relative pricing of companies through three key ratios: Price-to-Book Value (P/B), Enterprise Value-to-Total Assets (EV/TA), and Enterprise Value-to-Sales (EV/S). Given the complexities of the Iranian market, including severe economic volatility, chronic inflation, economic sanctions, and regulatory restrictions, this research provides an efficient tool for investors, underwriters, and regulatory bodies to make more informed decisions. Methodology: This quantitative-applied study utilized data from 163 companies (42 listed on the Tehran Stock Exchange and 121 on the over-the-counter market - Farabourse) spanning the period 1392 to 1402 (approximately 2013-2023). This period encompasses diverse economic conditions, including severe sanctions, currency fluctuations, recession, and economic booms. Independent variables included firm-specific financial features (size, debt ratio, profitability, return on assets, profit margin, cash flow ratio, financial leverage, and weighted average cost of capital) and macroeconomic variables (inflation rate, main stock market index, sanctions index, exchange rate, and interest rate). The proposed architecture is a hybrid of Gradient Boosting Decision Trees, Long Short-Term Memory (LSTM) networks, and self-attention mechanisms, optimized through an Automated Machine Learning (AutoML) architecture search. Optimal parameters included a learning rate of 0.001, 150 decision trees, 4-time steps, and 3 self-attention layers. The model was compared with traditional methods (Linear Regression) and advanced methods (standard Gradient Boosting algorithms, Random Forest, and Recurrent Neural Networks) and validated using ten-fold cross-validation and bootstrapping. Findings: The results demonstrated the decisive superiority of the proposed model in predicting all three valuation ratios. For the Price-to-Book Value ratio, the developed model achieved an R2 of 0.853 and a Root Mean Square Error (RMSE) of 0.497, representing a 60% improvement in prediction accuracy compared to Linear Regression (R2 of 0.534). In predicting the Enterprise Value-to-Total Assets ratio, an R2 of 0.865 and a 69% reduction in Mean Squared Error (MSE) (from 0.152 to 0.047) were obtained. For the Enterprise Value-to-Sales ratio, an R2 of 0.846 was achieved, showing a 67% improvement in accuracy over traditional methods. Other advanced models also performed acceptably but did not reach the level of the proposed model. Cross-validation tests confirmed the model's high stability with a very low standard deviation (around 0.01), indicating its generalizability and reliability under various conditions. Variable importance analysis revealed that the inflation rate acts as the second most important factor across all models, with an average importance of 0.19, indicating the profound impact of macroeconomic conditions on corporate valuation in the Iranian market. Return on Assets (0.21) was identified as the most important financial performance indicator for the P/B ratio, Profit Margin (0.225) for the EV/S ratio, and Cash Flow to Assets Ratio (0.2) for the EV/TA ratio. Conclusion: The findings emphasize the importance of employing novel machine learning techniques in predicting IPO valuation ratios. The developed model, by reducing prediction error by 60% to 69%, provides an advanced tool for underwriters, investors, and fund managers to mitigate the risk of undervaluation or overvaluation. The identification of the pivotal role of the inflation rate highlights the high sensitivity of valuations to macroeconomic conditions in the Iranian market. This research contributes in three key areas: introducing a novel hybrid architecture, providing a comprehensive validation framework, and identifying the differential importance of variables for various valuation ratios under unstable economic conditions. https://jfr.ut.ac.ir/article_104071.html Objective: Herding behavior is a type of behavioral bias in financial markets that can lead to irrational decision-making and deviation from the intrinsic value of assets. If not managed, this behavior increases market volatility and can result in the formation of price bubbles. The bursting of these bubbles leads to significant losses for investors and financial institutions. Individual investors, due to limited time, attention, and access to uniform and limited information sources, are unable to analyze all stocks. Therefore, it seems likely that, during trading, they are drawn to stocks that attract more attention. The primary aim of this study is to investigate the effect of individual (retail) investor attention on herding behavior in the Tehran Stock Exchange. Furthermore, the study compares the effect of investor attention on herding behavior between individual and institutional investors, and also examines differences in trading and order data. Finally, factors such as the intensity of price limit hits, company size, momentum, and investor sentiment are analyzed in relation to this behavior. Method: To test the effect of individual investor attention on herding behavior, a panel data regression approach has been used. Herding behavior is calculated using the LSV and FHW models as upper and lower bounds of the true value of this behavior. Investor attention is measured through the Google Search Volume Index (ASVI) and abnormal trading volume (AVOL). In this model, to control for the effects of other influencing variables, institutional investor herding behavior, stock returns, the inverse of the P/E ratio, trading volume, standard deviation of stock returns, market capitalization, and information demand are included as control variables. To further examine this effect, companies are divided into two groups based on the median size (large and small companies) and median momentum (high and low momentum). The model is separately estimated for each group, and the results are compared. Finally, the effect of investor sentiment, measured by the Arms index, is also investigated, and the effect of attention on herding behavior is separately analyzed in both positive and negative sentiment conditions. Data was collected monthly, covering active companies in the Tehran Stock Exchange from 2009 to September 2023. Results: The findings of this study indicate that individual investors' attention plays a key role in the formation and intensification of herding behavior in the Tehran Stock Exchange. Increased attention is often driven by investors' preferences for specific stocks, leading to reduced diversity in trading decisions and greater behavioral convergence. The relationship between attention and herding behavior becomes more significant when this attention spreads widely across the market, with many investors relying on the decisions of others without thorough analysis. Research has shown that, under conditions of increased attention, individual investors exhibit stronger herding behavior due to limited information and higher susceptibility to market sentiment. This widespread attention directs investors towards similar decisions, ultimately resulting in herding behavior. Furthermore, psychological factors such as positive sentiment attract more attention to stocks, and the prevailing optimism among investors exacerbates herding behavior. Additionally, well-known and large companies, due to their reputation and media coverage, tend to attract more attention, creating a favorable environment for the formation of herding behavior. Similarly, stocks with low momentum are more likely to attract attention due to concerns about losses or the possibility of price reversals, further intensifying herding behavior. Overall, the findings emphasize the importance of individual investors' attention in shaping herding behavior and demonstrate that as individual investors' attention increases, the tendency for group decisions and convergence in the market grows stronger. Conclusion: Investors' attention, especially that of individual investors, is a key factor in the formation of herding behavior. Increased attention to specific stocks leads to reduced decision-making diversity and greater convergence among investors. Large companies, due to media coverage, and stocks with low momentum, due to concerns about losses, are more susceptible to this behavior. Market sentiment also strengthens herding behavior in positive conditions and intensifies emotional reactions in negative ones. In addition to individual investors, institutional investors' behavior also impacts this process, as it can act as a signal for retail investors. These findings align with behavioral finance theories, showing that investor decisions are shaped not only by information but also by psychological factors and the behavior of others. https://jfr.ut.ac.ir/article_104072.html Objective: The marketing of pension fund services is tied to providing the future of people, so it should be done in a specialized way. Service marketing methods should be planned with regard to the literacy and financial knowledge of the fund's audience in order to have the maximum effect, so this issue is in the focus of policy makers, managers and activists of the social security organization. From a negative point of view, this study is also important because if there is no evaluation, monitoring and monitoring of people's financial literacy, it will not be possible to achieve the goals of marketing the services of the mentioned funds, and many budgets and resources spent in this field will remain unused. From a theoretical point of view, this issue is of particular importance, and earlier researchers have conducted several studies on "pension funds". But this study was not done from the perspective of marketing and focusing on people's financial literacy. The study of the mentioned components was done separately in such a way that there is no combination and alignment between them. The development and evolution of the role of service marketing in the field of pension funds with an emphasis on financial literacy has been neglected from the perspective of researchers. The review of studies shows that there is a deep research gap in the field under investigation. Therefore, the present study has been done with a practical-developmental view of service marketing for the acceptance of pension funds with a financial literacy approach in the social security organization. The theoretical and knowledge contribution of the present study is linking the concepts of service marketing and financial literacy in the field of pension funds. Also, since the social security organization has its own conditions and requirements, in this research, an effort was made to identify these factors with a method based on an exploratory mixed research plan. In this regard, the present study answers the key question, what is the service marketing model for accepting social security pension funds with a financial literacy approach? Methods: This study is a cross-sectional survey in terms of its practical-developmental purpose and in terms of the method and time frame of data collection. In order to achieve the goal of the research, an exploratory mixed research design was used. The community of participants of the qualitative part includes the long-term managers of the Social Security Pension Fund, 20 of whom were selected in a purposeful way until reaching theoretical saturation. In the quantitative part, the views of 384 people from compulsory and self-employed insured persons were used. Quantitative sampling was done by cluster-random method. The data collection tools were semi-structured interviews and researcher-made questionnaires, which were validated by construct validity, convergent validity and divergent validity methods. Using Cronbach's alpha and composite reliability, the reliability of the questionnaire was also evaluated. In order to identify the dimensions and components of service marketing for the acceptance of social security pension funds with a financial literacy approach, qualitative thematic analysis method was used, element relationships were determined using structural-interpretive modeling method, and partial least squares method was used to validate the model. Results: The findings showed that 302 codes were identified in the open coding stage. Finally, 4 overarching categories, 10 organizing categories and 59 basic themes were obtained through axial coding. Conclusion: The results showed that service marketing strategy, users' financial literacy, and physical equipment and facilities affect reliability and responsiveness. Reliability and responsiveness affect the improvement of customer experience and lead to customer participation, customer loyalty, and customer satisfaction. Through the participation of customers, it is finally possible to achieve the acceptance of pension funds. https://jfr.ut.ac.ir/article_104364.html Objective: This study aims to evaluate the sensitivity of machine learning models to input variables and identify the most significant factors influencing the prediction of the Tehran Stock Exchange index. Additionally, it compares the performance of different models in forecasting the stock index, focusing on the impact of input parameters, and offers strategies for optimizing input data and reducing model complexity. The key input variables considered include open, high, low prices, and trading volume. Method: In this research, due to the presence of a target variable (closing price), a supervised learning approach is employed. Furthermore, because the target variable is continuous, the problem is defined as a regression task. Predictions from four powerful machine learning algorithms—Linear Model (LM), Support Vector Regression (SVR), Artificial Neural Network (ANN), and Random Forest (RF)—were compared using performance evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²). MAE represents the average magnitude of errors, while MSE indicates the differences between the predicted and actual values. Lower values for both metrics suggest greater accuracy of predictions. The R² statistic represents the percentage of variance in the data that is explained by the model, with values close to one indicating a higher level of accuracy. The dataset used in this study consists of six selected indices from various sectors, including pharmaceutical, automotive, financial, food industries, basic metals, and petroleum products, covering the period from 2020 to 2024 on a daily basis. Sensitivity analysis was conducted to evaluate the importance of input variables such as open, high, low prices, and trading volume on the predictions of each model. Findings: The sensitivity analysis revealed that Support Vector Regression (SVR) exhibits lower sensitivity to input parameters. This can be attributed to the use of nonlinear kernels in the SVR model, which allow it to model complex relationships between the variables in a transformed feature space, thus reducing the direct impact of input parameters. In contrast, models such as Random Forest, Artificial Neural Networks (ANN), and Linear Regression demonstrated relatively higher sensitivity to input variables. These models are more directly dependent on the input data and provide a better representation of the relative importance of the variables in predicting the output. ANN, in particular, showed superior performance in forecasting stock indices due to its ability to capture complex, nonlinear relationships effectively. Conclusion: This study contributes to identifying the most influential variables in stock index fluctuations and demonstrates that more complex models, such as Random Forest and ANN, can be more effective in providing accurate predictions due to their higher sensitivity to input data. These findings also highlight the value of sensitivity analysis in identifying significant variables and eliminating unnecessary features, which helps improve the speed and reduce the complexity of models. Ultimately, the results can assist investors and policymakers in making more informed decisions and refining prediction models for better capital market strategies. https://jfr.ut.ac.ir/article_105014.html Objective: The disclosure of non-financial information, such as board of directors’ reports, plays a vital role in transparency and investor decision-making. However, leverage manipulation—often motivated by the intent to conceal financial distress—can increase the complexity and opacity of such disclosures. This study aims to examine the relationship between leverage manipulation and the complexity of non-financial disclosures, while accounting for financial constraints and stock price crash risk. By focusing on publicly listed firms in the Tehran Stock Exchange, this research seeks to identify the mechanisms through which managers obscure non-financial information and explores the impact of such complexity on market perception. Methods: This applied study employed a descriptive-correlational approach. The statistical population comprised board activity reports from 129 companies across 19 industries listed on the Tehran Stock Exchange, covering the period from 2007 to 2023 (1386–1402 in the Persian calendar), yielding 2,193 firm-year observations. Data were extracted from the Codal database and analyzed using a panel data approach. Leverage manipulation was measured based on the Zhu (2006) model, while complexity indicators were selected through theoretical literature and factor analysis. Partial Least Squares (PLS) modeling and ordinary least squares (OLS) regression were used to evaluate the relationships, allowing for the examination of cross-sectional dependencies and complex interactions. Results: The main findings of the research proved there is a positive and significant relationship between leverage manipulation and difficulty and complexity of text of non-financial information. For the purpose of covering manipulations, some managers with various motivations make the text of non-financial information difficult, in this case leverage ratios will be less than the truth. The study revealed, there is a significant difference among industries. when it comes to leverage manipulation that some companies are likely to manipulate leverage. In terms of motivation, financial constraint and risk of stock price crash play key role in this case. Additionally, control variables have impact on modeling result which refers some factors have influence and connections with difficulty and leverage manipulation variables. Based on structural equation modeling and variables, three hypotheses are discussed in this article, all of which have been confirmed. Conclusion: This study demonstrates that leverage manipulation affects not only quantitative metrics but also the qualitative dimensions of corporate reporting. Managers may employ linguistic techniques such as passive constructions, excessive verbosity, and technical jargon to diminish the clarity of non-financial information. This tendency intensifies under conditions of financial constraints and stock price crash risk, ultimately distorting investor perception and undermining transparency. Unlike quantitative manipulations, which are often detectable via standard audit procedures, qualitative obfuscation is subtler and harder to identify—making it a critical area for scrutiny. Aligned with signaling theory, the findings show that increasing disclosure complexity functions as a negative signal, reducing investor trust and elevating information asymmetry. This research contributes to the body of knowledge by integrating financial manipulation with linguistic opacity, offering a multi-dimensional framework for understanding disclosure practices. From a practical standpoint, enhancing the financial literacy and accounting knowledge of investors is essential, as non-financial reports assume a foundational understanding of accounting concepts. In addition, policymakers are encouraged to establish clearer reporting standards and enforce disclosure simplicity to prevent undue complexity. The study’s limitations include its focus on Iranian publicly listed firms and the exclusion of international comparisons. Future research may extend this model using structural equation modeling or machine learning techniques across diverse markets and sectors. Ultimately, this research offers a theoretical and empirical framework for enhancing reporting transparency and mitigating information asymmetry in capital markets. https://jfr.ut.ac.ir/article_105502.html Purpose: 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 pressing 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. Methodology: The foundational model for estimating the extent of informed trading is the Probability of Informed Trading (PIN) model, a well-established metric in financial economics that gauges the likelihood of informed traders participating in the market, thereby reflecting the degree of information asymmetry in trading processes. 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 research comprises intraday price, time, and volume data for 92 exchange-traded equity 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. Findings: 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. Conclusion: 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_105890.html Objective Climate change poses an unprecedented challenge to the governance of global socioeconomic and financial systems. Our current production and consumption patterns cause unsustainable emissions of greenhouse gases (GHGs), especially carbon dioxide (CO2): their accumulated concentration in the atmosphere above critical thresholds is increasingly recognised as being beyond our ecosystem’s absorptive and recycling capabilities. The risks caused by climate change are considered to be the most serious climate risks in the next 30 years. These risks directly impact financial stability through channels such as the destruction of collateral, assets, and physical infrastructure of banks and customers caused by extreme climate events. Increasing of banks risks due to climate change can cause financial instability that pose serioes damage to the whole economy. Therefore, it has been the main focus of researchers and policy makers in various countries. In this research, banks risk due to climate change was investigated. Methods In the present study, using data from 17 Iranian banks during the period 2011-2023, using the fixed effects method and the generalized least squares (GLS) estimator, banking risk in the face of climate change was examined. Findings The results of the study show that climate change affects bank risk. The relationship between risk and precipitation is negative. This effect is direct for temperature. Regarding bank variables, the relationship between risk and capital adequacy is significant and direct. Given that the significance level of the variables of bank size, leverage ratio, deposit-to-asset ratio, and deposit-to-facility ratio is less than 0.05 percent, these variables affect bank risk. Since the coefficient of these variables is negative, this relationship is inverse. Also, the effect of bank risk on return on assets and cost-to-income ratio is not significant. Conclusion Climate change affects banks’ risk. Therefore, it is necessary for banks to strengthen their awareness of climate change risks and the physical hazards they pose. First, for industries vulnerable to extreme climate change events, such as agriculture, real estate and other industries, when banks lend to such industries, they should consider the potential risks of environmental factors and climate change to the greatest extent possible. Second, banks and related lending institutions should actively improve the disclosure of information on climate change-sensitive portfolios and enhance the monitoring of loan quality in climate-related industries to ensure that they can adapt to changing climate conditions. Finally, banks should monitor the climate characteristics of different regions, take precautions on climate change issues, and deepen their understanding of related financial laws and regulations. Coordination between the banking and insurance industries is also essential to cooperate and address the challenges of climate change. The release of accident insurance products and bank credit should be used to transfer this risk and reduce the pressure on economic agents. Similarly, increasing the penetration of insurance, reducing credit risks related to climate change, and the ability to recover quickly after disasters can be issues that need to be considered in this context. https://jfr.ut.ac.ir/article_106083.html Abstract Objective: Selecting an optimal combination of assets within an investment portfolio has always been one of the fundamental and challenging issues in investment management. In other words, investors continually aim to achieve an efficient and optimal allocation by considering the key factors influencing the decision-making process and aligning the selection with their individual risk–return preferences. However, traditional portfolio theory often relies on simplified statistical assumptions—particularly the assumption of normally distributed returns—that are inconsistent with the empirical characteristics of financial markets. In reality, financial asset returns frequently display heavy tails, negative skewness, volatility clustering, and asymmetric dependence during periods of market stress. These stylized facts imply that extreme losses occur more frequently than predicted by classical models, highlighting the necessity for more sophisticated tools in modern risk management and portfolio construction. To address these limitations, the present study develops a comprehensive hybrid modeling framework that integrates three advanced components: EGARCH to capture time-varying volatility and leverage effects, Extreme Value Theory (EVT) to model tail behavior and estimate extreme losses more accurately, and t-Copula to model tail dependence and joint downside risk across industries. Additionally, considering that parameter estimates obtained from historical data are subject to uncertainty and may deviate from their true values, a Robust Optimization approach under the Conditional Value-at-Risk (CVaR) criterion is adopted. This framework allows the portfolio to remain resilient against estimation errors and improves decision-making under market uncertainty. Accordingly, the main objective of this research is to present a robust and realistic portfolio optimization model based on the EGARCH–EVT–t-Copula–Robust CVaR approach. Methods: Daily return data for ten major industry indices of the Tehran Stock Exchange, covering the period from September 2015 to September 2025, were employed. Conditional volatility was modeled using the EGARCH(1,1) specification to account for asymmetry in the volatility response to positive and negative shocks. Standardized residuals obtained from the volatility model were analyzed using EVT under the Peaks-over-Threshold method to estimate the heavy-tail behavior of returns. The dependence structure among industries was then modeled through t-Copula to capture joint extreme movements and tail dependence. Finally, portfolio optimization was conducted under the CVaR measure in both standard and robust formulations, and the performance of the resulting portfolios was compared based on expected return, risk, and Sharpe ratio. Results: Empirical findings reveal that the integrated EGARCH–EVT–t-Copula–Robust CVaR framework delivers superior accuracy in capturing extreme risks and provides a more effective portfolio allocation in heavy-tailed environments. The EGARCH model successfully captured conditional volatility dynamics, EVT highlighted the presence of heavy left-tail behavior and significant downside risk, and the t-Copula confirmed strong tail dependence among industrial sectors, implying the synchronized occurrence of severe negative shocks. In the optimization step, the Robust CVaR portfolio outperformed the standard CVaR model by yielding higher expected returns and Sharpe ratios, illustrating its enhanced ability to handle parameter uncertainty and asymmetric market volatility. Conclusion: Overall, the findings confirm that in emerging markets such as Iran—characterized by high kurtosis, negative skewness, and tail dependence—classical risk models based on normality assumptions are inadequate. In contrast, the proposed EGARCH–EVT–t-Copula–Robust CVaR framework provides a more realistic estimation of tail risk and leads to a more stable and efficient capital allocation. Hence, adopting robust CVaR optimization under conditional heteroskedasticity and tail-dependent structures is recommended for professional portfolio management and investment decision-making in high-risk markets. https://jfr.ut.ac.ir/article_106185.html Objective: Excessive use of debt in the capital structure of companies increases the credit risk of companies and, as a result, the probability of their bankruptcy. Given that shareholders are considered the remaining owners of the company, it can be said that how the company is financed or the composition of the capital structure of companies is effective on their expected returns and the pricing process of securities issued by the company. Accordingly, this study aims to investigate the role of the credit risk factor in asset pricing models and evaluate the explanatory power of this factor in explaining stock returns in the Iranian capital market. Methodology: In this study, in order to achieve a comprehensive criterion for measuring credit risk, we used the Geske model, which is an advanced and developed version of the Merton model. Accordingly, we first calculated the probability of default of the total debt of companies based on the Geske model, using numerical algorithm techniques. After calculating the probability of default, the credit risk factor was defined based on the difference in returns between companies with high and low probability of default. Next, the hedging regression method was used to examine the role of the credit risk factor in explaining stock and bond returns. In the next step, the credit risk factor was added to the asset pricing factor models and by running time series regressions on a large set of test assets, the explanatory power of the extended models with the credit risk factor was evaluated and tested in comparison with conventional asset pricing models. Finally, in order to examine the robustness and stability of the results and to more accurately assess the predictability of credit risk factor loadings in explaining cross-sectional excess returns, a two-stage Fama-Macbeth test was used. In the first stage of this test, time-varying factor loadings for the credit risk factor were calculated using time series regressions on asset pricing factor models. Then, in the second stage, cross-sectional regression was performed for excess returns relative to the factor loadings estimated in the first stage, and finally, the credit risk factor price was determined as the average of the estimated coefficients from the cross-sectional regression. To achieve this goal, data from companies listed on the Tehran Stock Exchange and the Iranian OTC market between 2004 and 2023 and a diverse set of test assets, including portfolios sorted based on various company characteristics, were used. Findings: The results of the spanning regression show that the credit risk factor contains unique and meaningful information that cannot be explained by other factors in asset pricing factor modelsIn addition, based on the results of time series regression tests and the performance evaluation criteria of the models, adding a credit risk factor to multi-factor asset pricing models improves the explanatory power of these models in explaining the returns of the test assets. Also, the results of the Fama-Macbeth test show that the time series average of the coefficients related to the credit risk factor is positive and significant, indicating a positive risk premium for this factor. These findings indicate that investors receive excess returns in exchange for accepting higher credit risk and this factor is positively priced in the Iranian capital market. Conclusion: The findings of this study show that adding credit risk factor to asset pricing models significantly increases the power of these models in explaining fluctuations in returns of financial assets and stocks and also increases their forecasting accuracy. Also, the results indicate that credit risk as a systematic and unavoidable factor that is a function of the company's economic environment is reflected in stock returns by taking a positive risk premium and increases the expected return of stocks.