Shahid Beheshti University Financial Management Perspective 2645-4637 14 48 2025 01 20 Role of Evaluation Period Characteristics in the Efficiency Analysis of Futures Contracts for Equity Index Risk Management Role of Evaluation Period Characteristics in the Efficiency Analysis of Futures Contracts for Equity Index Risk Management 9 33 105778 10.48308/jfmp.2025.238487.1467 FA Saeed Fathi Associate Professor University of Isfahan 0000-0001-8067-2437 Elaheh Esmaeili Sheshjavani Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran Journal Article 2025 01 22 Objective: Previous research has examined the effectiveness of futures contracts in risk management for various indices using different strategies and estimation methods across different years, countries, and underlying assets, yielding diverse results. One of the key dimensions influencing the divergence in outcomes of past studies is the characteristics of the assessment period for variance changes and the estimation period for optimal risk hedging. Understanding the role of these factors is crucial for enhancing the quality of future studies on risk hedging via futures contracts. Due to more implication of variance and more coverage in previeouse studies, this measure has been used for risk in contrast ot utility or Var. To validate the results we need to test the robustness of test results fo all three hypotheses. Method: This meta-analysis encompasses all empirical tests conducted on the efficacy of stock index risk hedging using futures contracts, published in articles from 2015 to 2022. It includes 30 papers and 1,373 effect sizes, which consist of 1,846,373 and 1,052,225 observations in the estimation and assessment periods, respectively. To achieve the specified objectives, data were computed and analyzed using CMA software. The excell software has been used to arrange the data. The basis of the evaluation was the weighted average effect size of risk coverage efficacy, and ANOVA was utilized for comparing effect size groups. Due to convergence of effect sizes, the random effect method has been used to calculate the cumulative effect sizes. Seven-sptep meta-analysis has been used in this study. Findings: Results indicated that the efficacy of risk hedging in the category with an assessment period of one year or less was greater than that in categories with an assessment period exceeding one year. Furthermore, the efficiency of stock index risk hedging utilizing out-of-sample strategies was found to be superior to that employing in-sample strategies. Moreover, using a larger dataset, or in other words, extending the estimation period, led to a reduction in risk hedging efficiency. Out of 16 robustness test categories for assessment period length and estimation strategy, results were confirmed in 15 categories, and for estimation period length, results were validated in ten categories. Conclusion: During extended evaluation periods, the frequency of interest rate changes or other environmental factors is likely higher, which may reduce the effectiveness of risk hedging. Regarding the estimation window length, if investors estimate their risk hedging model parameters using datasets spanning less than three years, they can more effectively control price volatility in their cash assets. Indeed, under such conditions, the variance of a portfolio comprising both cash and futures assets experiences a more pronounced reduction. Finally, when investors utilize historical data to estimate parameters and implement risk hedging for future periods—adopting an out-of-sample approach—they can achieve more efficient risk coverage. Objective: Previous research has examined the effectiveness of futures contracts in risk management for various indices using different strategies and estimation methods across different years, countries, and underlying assets, yielding diverse results. One of the key dimensions influencing the divergence in outcomes of past studies is the characteristics of the assessment period for variance changes and the estimation period for optimal risk hedging. Understanding the role of these factors is crucial for enhancing the quality of future studies on risk hedging via futures contracts. Due to more implication of variance and more coverage in previeouse studies, this measure has been used for risk in contrast ot utility or Var. To validate the results we need to test the robustness of test results fo all three hypotheses. Method: This meta-analysis encompasses all empirical tests conducted on the efficacy of stock index risk hedging using futures contracts, published in articles from 2015 to 2022. It includes 30 papers and 1,373 effect sizes, which consist of 1,846,373 and 1,052,225 observations in the estimation and assessment periods, respectively. To achieve the specified objectives, data were computed and analyzed using CMA software. The excell software has been used to arrange the data. The basis of the evaluation was the weighted average effect size of risk coverage efficacy, and ANOVA was utilized for comparing effect size groups. Due to convergence of effect sizes, the random effect method has been used to calculate the cumulative effect sizes. Seven-sptep meta-analysis has been used in this study. Findings: Results indicated that the efficacy of risk hedging in the category with an assessment period of one year or less was greater than that in categories with an assessment period exceeding one year. Furthermore, the efficiency of stock index risk hedging utilizing out-of-sample strategies was found to be superior to that employing in-sample strategies. Moreover, using a larger dataset, or in other words, extending the estimation period, led to a reduction in risk hedging efficiency. Out of 16 robustness test categories for assessment period length and estimation strategy, results were confirmed in 15 categories, and for estimation period length, results were validated in ten categories. Conclusion: During extended evaluation periods, the frequency of interest rate changes or other environmental factors is likely higher, which may reduce the effectiveness of risk hedging. Regarding the estimation window length, if investors estimate their risk hedging model parameters using datasets spanning less than three years, they can more effectively control price volatility in their cash assets. Indeed, under such conditions, the variance of a portfolio comprising both cash and futures assets experiences a more pronounced reduction. Finally, when investors utilize historical data to estimate parameters and implement risk hedging for future periods—adopting an out-of-sample approach—they can achieve more efficient risk coverage.

https://jfmp.sbu.ac.ir/article_105778_cca92d417984799a0b4b41a7e3da47b7.pdf

Shahid Beheshti University Financial Management Perspective 2645-4637 14 48 2025 01 20 Firm reputation, risk, and stock return Firm reputation, risk, and stock return 34 54 105795 10.48308/jfmp.2025.237519.1438 FA Mohsen Khotanlou Department of Accounting, Faculty of Economics and Social Science, Bu-Ali Sina University, Hamedan, Iran 0000-0002-9772-4325 Ali Souezi MSc, Department of Accounting, Alvand Institute of Higher Education, Hamedan, Iran. 0009-0001-6471-1472 Mahdi Kazemioloum Assistant Professor, Department of Accounting, Faculty of Economics and Social Science, Bu-Ali Sina University, Hamedan, Iran Journal Article 2024 11 26 Abstract Purpose: In recent years, corporate reputation has emerged as a driving force for efficient and productive business operations, effectively managing stakeholder behavior. According to signaling theory, corporate reputation signifies risk, with higher reputation levels indicating lower risk. Additionally, from a resource-based perspective, corporate reputation is a strategic asset and a valuable resource, leading to sustainable performance and competitive advantage. Consequently, corporate reputation is an intangible asset that accumulates over time, potentially generating value and wealth for the company while enhancing its performance. This study examines corporate reputation's impact on various dimensions of risk, including total company risk, systematic risk, financial distress risk, and stock returns. Methodology: The study tests its hypotheses using data from 156 companies listed on the Tehran Stock Exchange over 11 years from 2013 to 2024. Corporate reputation was measured through principal component analysis and supplemented by the Industrial Management Organization rankings. Regression models were fitted using a dynamic panel approach and the generalized method of moments estimator. Findings: Statistical testing of the research hypotheses revealed several vital results. First, using both reputation measures, a significant and direct relationship exists between corporate reputation and total risk. Second, a negative and significant relationship was found between corporate reputation and systematic risk. Third, a negative and significant association was identified between corporate reputation and financial distress risk when reputation was defined based on principal component analysis. Fourth, a positive and significant relationship was observed between corporate reputation and excess stock returns across both definitions of reputation. Conclusion: Corporate reputation shapes companies' strategic responses to environmental threats. A high corporate reputation within a competitive landscape is critical for business success. Reputable companies generally enjoy easier and more cost-effective access to financial resources, which can help alleviate financial and investment constraints during financial stress. Consequently, these companies will likely face lower financial distress risks and deliver superior performance to stakeholders. However, the impact of corporate reputation goes beyond financial performance. High-reputation firms attract greater attention from shareholders, leading to enhanced financial stability and reduced risk. This research offers theoretical and practical insights for policymakers within the stock exchange and shareholders alike. The findings support policymakers in recognizing the importance of corporate reputation and ranking systems. Additionally, these results may assist shareholders in making effective investment decisions and contribute to expanding and developing empirical research foundations in corporate reputation. Conclusion: Corporate reputation shapes companies' strategic responses to environmental threats. A high corporate reputation within a competitive landscape is critical for business success. Reputable companies generally enjoy easier and more cost-effective access to financial resources, which can help alleviate financial and investment constraints during financial stress. Consequently, these companies will likely face lower financial distress risks and deliver superior performance to stakeholders. However, the impact of corporate reputation goes beyond financial performance. High-reputation firms attract greater attention from shareholders, leading to enhanced financial stability and reduced risk. This research offers theoretical and practical insights for policymakers within the stock exchange and shareholders alike. The findings support policymakers in recognizing the importance of corporate reputation and ranking systems. Additionally, these results may assist shareholders in making effective investment decisions and contribute to expanding and developing empirical research foundations in corporate reputation. Abstract Purpose: In recent years, corporate reputation has emerged as a driving force for efficient and productive business operations, effectively managing stakeholder behavior. According to signaling theory, corporate reputation signifies risk, with higher reputation levels indicating lower risk. Additionally, from a resource-based perspective, corporate reputation is a strategic asset and a valuable resource, leading to sustainable performance and competitive advantage. Consequently, corporate reputation is an intangible asset that accumulates over time, potentially generating value and wealth for the company while enhancing its performance. This study examines corporate reputation's impact on various dimensions of risk, including total company risk, systematic risk, financial distress risk, and stock returns. Methodology: The study tests its hypotheses using data from 156 companies listed on the Tehran Stock Exchange over 11 years from 2013 to 2024. Corporate reputation was measured through principal component analysis and supplemented by the Industrial Management Organization rankings. Regression models were fitted using a dynamic panel approach and the generalized method of moments estimator. Findings: Statistical testing of the research hypotheses revealed several vital results. First, using both reputation measures, a significant and direct relationship exists between corporate reputation and total risk. Second, a negative and significant relationship was found between corporate reputation and systematic risk. Third, a negative and significant association was identified between corporate reputation and financial distress risk when reputation was defined based on principal component analysis. Fourth, a positive and significant relationship was observed between corporate reputation and excess stock returns across both definitions of reputation. Conclusion: Corporate reputation shapes companies' strategic responses to environmental threats. A high corporate reputation within a competitive landscape is critical for business success. Reputable companies generally enjoy easier and more cost-effective access to financial resources, which can help alleviate financial and investment constraints during financial stress. Consequently, these companies will likely face lower financial distress risks and deliver superior performance to stakeholders. However, the impact of corporate reputation goes beyond financial performance. High-reputation firms attract greater attention from shareholders, leading to enhanced financial stability and reduced risk. This research offers theoretical and practical insights for policymakers within the stock exchange and shareholders alike. The findings support policymakers in recognizing the importance of corporate reputation and ranking systems. Additionally, these results may assist shareholders in making effective investment decisions and contribute to expanding and developing empirical research foundations in corporate reputation. Conclusion: Corporate reputation shapes companies' strategic responses to environmental threats. A high corporate reputation within a competitive landscape is critical for business success. Reputable companies generally enjoy easier and more cost-effective access to financial resources, which can help alleviate financial and investment constraints during financial stress. Consequently, these companies will likely face lower financial distress risks and deliver superior performance to stakeholders. However, the impact of corporate reputation goes beyond financial performance. High-reputation firms attract greater attention from shareholders, leading to enhanced financial stability and reduced risk. This research offers theoretical and practical insights for policymakers within the stock exchange and shareholders alike. The findings support policymakers in recognizing the importance of corporate reputation and ranking systems. Additionally, these results may assist shareholders in making effective investment decisions and contribute to expanding and developing empirical research foundations in corporate reputation.

https://jfmp.sbu.ac.ir/article_105795_7d72d37e36269cb3719ec0154dde1be0.pdf

Shahid Beheshti University Financial Management Perspective 2645-4637 14 48 2025 01 20 Interpreting Forecast the Return of the Price Index of Manufacturing Industries in the Tehran Stock Exchange Using Explainable Ensemble Learning Interpreting Forecast the Return of the Price Index of Manufacturing Industries in the Tehran Stock Exchange Using Explainable Ensemble Learning 55 78 105915 10.48308/jfmp.2025.238860.1475 FA Reza Raei Professor of finance management, faculty of accounting and finance, college of management, university of Tehran. Iran. 0000-0003-4865-5316 Masoud Vahdati Msc of finance management, faculty of accounting and finance, college of management, university of Tehran. Iran. Hossein Mohebbi Assistant Professor, Industrial Management Deptment, Meybod University, Meybod, Iran 0000-0003-3859-8936 Amirhossein Heydari Delooei Msc Student of Algrithms and Computations, faculty of Electrical and Computer Engineering (ECE), college of Engineering, University of Tehran. Iran. Journal Article 2025 02 22 Purpose: In recent years, machine learning has gained significant attention as an effective tool for forecasting financial time series. However, many of these models function as black boxes, and their lack of transparency has led to reduced trust in their predictions. To address this limitation, the use of explainable artificial intelligence (XAI) models-capable of providing detailed insights into the prediction mechanisms-has become essential. Accordingly, the aim of this study is to develop and evaluate an artificial intelligence (AI)-based forecasting model that not only delivers high accuracy but also offers strong interpretability. In this context, the contribution and role of input variables in the model's predictions are explicitly identified, and the stability of the results in terms of both accuracy and explain ability is assessed using cross-validation techniques, particularly time series splitting. Method: This applied research adopts a descriptive-analytical method with a quantitative forecasting approach. For the first time in Iran, it investigates the explain ability of optimized artificial intelligence models in forecasting the return of the price index for eight manufacturing industries listed on the Tehran Stock Exchange. The dataset, covering the period from 2018 to 2023, was collected from the Bourse View database. The Random Forest algorithm, as an ensemble learning method, was trained using a combination of technical, fundamental, and macroeconomic variables as input features. A Genetic Algorithm was utilized to optimize the model’s hyperparameters. To enhance transparency and model credibility, the SHAP (shapley additive explanations) technique was employed to analyze the influence and importance of each feature in the prediction process. Findings: The results demonstrate that combining the Random Forest algorithm with Genetic Algorithm-based hyperparameter optimization and incorporating explain ability techniques such as SHAP values not only improves the prediction accuracy of the price index returns for Tehran’s manufacturing industries but also enhances model transparency and reliability. The findings highlight those technical indicators-particularly the Exponential Moving Average (EMA), MACD (Moving Average Convergence Divergence) index, trading volume, and free float shares-play the most significant role in enhancing predictive accuracy. In contrast, fundamental variables such as the price-to-earnings ratio and interest rates are influential but less impactful compared to technical indicators. Furthermore, time series cross-validation confirms the robustness and generalizability of the proposed model across different time periods. Conclusion: In line with reputable international studies, the results suggest that explainable artificial intelligence (AI) models not only outperform traditional models in predictive tasks but also assist financial analysts in making informed and effective decisions. These models can play a pivotal role in risk management and portfolio optimization. Therefore, the proposed model-featuring operational transparency and high reliability- is introduced as an effective tool for financial analysts, opening new horizons for the application of explainable artificial intelligence (AI) in Iran’s financial sector. Purpose: In recent years, machine learning has gained significant attention as an effective tool for forecasting financial time series. However, many of these models function as black boxes, and their lack of transparency has led to reduced trust in their predictions. To address this limitation, the use of explainable artificial intelligence (XAI) models-capable of providing detailed insights into the prediction mechanisms-has become essential. Accordingly, the aim of this study is to develop and evaluate an artificial intelligence (AI)-based forecasting model that not only delivers high accuracy but also offers strong interpretability. In this context, the contribution and role of input variables in the model's predictions are explicitly identified, and the stability of the results in terms of both accuracy and explain ability is assessed using cross-validation techniques, particularly time series splitting. Method: This applied research adopts a descriptive-analytical method with a quantitative forecasting approach. For the first time in Iran, it investigates the explain ability of optimized artificial intelligence models in forecasting the return of the price index for eight manufacturing industries listed on the Tehran Stock Exchange. The dataset, covering the period from 2018 to 2023, was collected from the Bourse View database. The Random Forest algorithm, as an ensemble learning method, was trained using a combination of technical, fundamental, and macroeconomic variables as input features. A Genetic Algorithm was utilized to optimize the model’s hyperparameters. To enhance transparency and model credibility, the SHAP (shapley additive explanations) technique was employed to analyze the influence and importance of each feature in the prediction process. Findings: The results demonstrate that combining the Random Forest algorithm with Genetic Algorithm-based hyperparameter optimization and incorporating explain ability techniques such as SHAP values not only improves the prediction accuracy of the price index returns for Tehran’s manufacturing industries but also enhances model transparency and reliability. The findings highlight those technical indicators-particularly the Exponential Moving Average (EMA), MACD (Moving Average Convergence Divergence) index, trading volume, and free float shares-play the most significant role in enhancing predictive accuracy. In contrast, fundamental variables such as the price-to-earnings ratio and interest rates are influential but less impactful compared to technical indicators. Furthermore, time series cross-validation confirms the robustness and generalizability of the proposed model across different time periods. Conclusion: In line with reputable international studies, the results suggest that explainable artificial intelligence (AI) models not only outperform traditional models in predictive tasks but also assist financial analysts in making informed and effective decisions. These models can play a pivotal role in risk management and portfolio optimization. Therefore, the proposed model-featuring operational transparency and high reliability- is introduced as an effective tool for financial analysts, opening new horizons for the application of explainable artificial intelligence (AI) in Iran’s financial sector.

https://jfmp.sbu.ac.ir/article_105915_4c8bd1a73e2ad2b49a11c17ef6838b16.pdf

Shahid Beheshti University Financial Management Perspective 2645-4637 14 48 2025 01 20 The Role of Financial Statement Comparability and Product Diversification in the Internal Capital Allocation Efficiency The Role of Financial Statement Comparability and Product Diversification in the Internal Capital Allocation Efficiency 79 99 105911 10.48308/jfmp.2025.239790.1489 FA Mohsen Rashidi Associate professor of accounting, Lorestan University 0000-0001-8198-0415 Journal Article 2025 05 03 Objective: Comparability of financial statements can be considered as one of the dimensions of corporate governance because it reduces the value reduction caused by diversification in the company. In addition, empirical results show that comparability of financial statements can provide better benefits for companies that face more severe agency problems. As a result, comparability of financial statements can reduce agency problems for multi-sector companies, which are the root of internal capital inefficiency and control the decline in future values. Managers usually tend to allocate internal resources and capital based on the unit profitability. However, diversity in product offerings leads to changes in the internal capital allocation process. The aim of this paper is to examine the role of financial statement comparability and product diversification in the internal capital allocation efficiency. Method: The purpose of this study is descriptive and based on the nature and method of correlation. Considering that this research can be used in the decision making process of investors, the type of applied research is considered. In this research, library method has been used to collect data and information. Then, for collecting the research data, compact discs, visual and statistical archives of the Tehran Stock Exchange, the official website of Tehran Stock Exchange and other related online databases have been used. In this study, in order to examine and analyze the hypotheses, data related to 120 companies listed on the Tehran Stock Exchange for the period 2016 to 2023 were extracted and a panel data regression model with fixed effects was used to test the research hypotheses. Findings: The results of the study indicate that the comparability of financial statements leads to an increase in the internal capital allocation efficiency. Also, the results of the second hypothesis of the study indicate a significant relationship between product diversity and the internal capital allocation efficiency. The results of the third hypothesis indicate a significant effect of financial statement comparability on the relationship between product diversity and the internal capital allocation efficiency. Also, the results of the fourth hypothesis of the research indicate a significant relationship between information asymmetry and internal capital allocation efficiency. Finally, the results of the fifth hypothesis indicate an interactive effect of information asymmetry, financial statement comparability, and product diversity on internal capital allocation efficiency. Conclusion: The financial statements comparability through the processing and communication of intra-organizational information provides qualitative frameworks of accounting information for capital market practitioners. Therefore, this strategy makes it possible to evaluate the success of investment projects. In other words, comparability, by increasing the accuracy of available information, increases the possibility of efficient internal capital allocation based on output and reduces the managers’ willingness to decrease the shareholders’ interest. Keywords: Financial statement comparability, product diversification, internal capital allocation efficiency, information asymmetry Objective: Comparability of financial statements can be considered as one of the dimensions of corporate governance because it reduces the value reduction caused by diversification in the company. In addition, empirical results show that comparability of financial statements can provide better benefits for companies that face more severe agency problems. As a result, comparability of financial statements can reduce agency problems for multi-sector companies, which are the root of internal capital inefficiency and control the decline in future values. Managers usually tend to allocate internal resources and capital based on the unit profitability. However, diversity in product offerings leads to changes in the internal capital allocation process. The aim of this paper is to examine the role of financial statement comparability and product diversification in the internal capital allocation efficiency. Method: The purpose of this study is descriptive and based on the nature and method of correlation. Considering that this research can be used in the decision making process of investors, the type of applied research is considered. In this research, library method has been used to collect data and information. Then, for collecting the research data, compact discs, visual and statistical archives of the Tehran Stock Exchange, the official website of Tehran Stock Exchange and other related online databases have been used. In this study, in order to examine and analyze the hypotheses, data related to 120 companies listed on the Tehran Stock Exchange for the period 2016 to 2023 were extracted and a panel data regression model with fixed effects was used to test the research hypotheses. Findings: The results of the study indicate that the comparability of financial statements leads to an increase in the internal capital allocation efficiency. Also, the results of the second hypothesis of the study indicate a significant relationship between product diversity and the internal capital allocation efficiency. The results of the third hypothesis indicate a significant effect of financial statement comparability on the relationship between product diversity and the internal capital allocation efficiency. Also, the results of the fourth hypothesis of the research indicate a significant relationship between information asymmetry and internal capital allocation efficiency. Finally, the results of the fifth hypothesis indicate an interactive effect of information asymmetry, financial statement comparability, and product diversity on internal capital allocation efficiency. Conclusion: The financial statements comparability through the processing and communication of intra-organizational information provides qualitative frameworks of accounting information for capital market practitioners. Therefore, this strategy makes it possible to evaluate the success of investment projects. In other words, comparability, by increasing the accuracy of available information, increases the possibility of efficient internal capital allocation based on output and reduces the managers’ willingness to decrease the shareholders’ interest. Keywords: Financial statement comparability, product diversification, internal capital allocation efficiency, information asymmetry

https://jfmp.sbu.ac.ir/article_105911_8278041de9b8124ebdd68726521a689a.pdf

Shahid Beheshti University Financial Management Perspective 2645-4637 14 48 2025 01 20 The Impact of Stock Liquidity on Stock Price Crash Risk: The Moderating Role of Institutional Ownership The Impact of Stock Liquidity on Stock Price Crash Risk: The Moderating Role of Institutional Ownership 100 118 105912 10.48308/jfmp.2025.239908.1494 FA Mahdieh Kamran Accounting Group, Humanities Faculty, Science and Culture University 0000-0001-8059-8775 Sima Takbiri Department of Accounting, Humanities Faculty, Science and Culture University, Tehran, Iran 0009-0009-7650-8832 Journal Article 2025 05 10 Objective: The primary objective of this study is to investigate the impact of stock liquidity on stock price crash risk, with a specific emphasis on the moderating role of institutional ownership. Stock price crash risk is considered one of the hidden and dangerous risks in capital markets, with the potential to inflict substantial losses on investors and destabilize markets. This research aims to determine whether liquidity—as a key indicator of market efficiency—can itself contribute to an increase in crash risk, and whether the presence of institutional investors strengthens or weakens this relationship. Methodology: This descriptive–correlational study is based on a panel dataset of 155 firms listed on the Tehran Stock Exchange during the period 2013 to 2022 . To measure crash risk, two common indicators were used: the negative conditional skewness of weekly returns (NCSKEW) and the down-to-up volatility ratio (DUVOL). Stock liquidity was measured using the Amihud illiquidity index, while institutional ownership was defined using a dummy variable equal to one if institutions held more than 5% of a firm’s shares. Following the literature, control variables including SIZE, LEV, ROA, BTM, SIGMA, and RET were included in the regression models. The statistical analysis was conducted using fixed-effect panel regressions estimated with least squares and HC3 heteroskedasticity-robust standard errors. Findings: The empirical results show that stock liquidity plays a critical role in increasing crash risk. This supports the "bad news hoarding and sudden release" theory, especially in markets like Iran where short selling is restricted and information transparency is limited. Specifically, the Amihud illiquidity index—used as an inverse measure of liquidity—showed a positive and significant coefficient in the NCSKEW model (β = 0.0386; p < 0.01). This implies that the more liquid a stock is, the more likely it is to experience a price crash triggered by the delayed release of negative news. In contrast, in the DUVOL model, the effect of liquidity was negative but statistically insignificant, indicating that DUVOL is less sensitive to liquidity than NCSKEW. Institutional ownership alone had no significant direct effect on crash risk; however, its interaction with liquidity (LIQ × DINST) was highly significant: the interaction coefficient was −0.0040 in the NCSKEW model and +0.0044 in the DUVOL model (both at p < 0.01). This contrast in sign suggests that the presence of institutional investors dampens the protective effect of illiquidity. While the relationship remains negative in the skewness-based model, it becomes weaker; in the volatility-based model, it becomes nearly neutral. Moreover, the analytical derivative showed that the inflection point occurs at an institutional ownership level of approximately 6.14%. Given that nearly two-thirds of firms in the sample have institutional ownership above this threshold, the findings indicate that the combination of high liquidity and high institutional ownership—especially in the presence of short-term-oriented institutions—intensifies the risk of a stock price crash for most companies. This outcome is consistent with the short-termism hypothesis. Conclusion:The findings suggest that while liquidity is essential for market efficiency, in environments lacking control mechanisms such as short selling, it may paradoxically function as a risk factor. Additionally, policymakers and regulators should be aware that institutional ownership is not inherently stabilizing; rather, the nature and behavior of institutional investors (active vs. passive, long-term vs. short-term) are critical in shaping their impact. These results offer valuable insights for improving disclosure practices and regulatory frameworks in Iran’s capital market. Objective: The primary objective of this study is to investigate the impact of stock liquidity on stock price crash risk, with a specific emphasis on the moderating role of institutional ownership. Stock price crash risk is considered one of the hidden and dangerous risks in capital markets, with the potential to inflict substantial losses on investors and destabilize markets. This research aims to determine whether liquidity—as a key indicator of market efficiency—can itself contribute to an increase in crash risk, and whether the presence of institutional investors strengthens or weakens this relationship. Methodology: This descriptive–correlational study is based on a panel dataset of 155 firms listed on the Tehran Stock Exchange during the period 2013 to 2022 . To measure crash risk, two common indicators were used: the negative conditional skewness of weekly returns (NCSKEW) and the down-to-up volatility ratio (DUVOL). Stock liquidity was measured using the Amihud illiquidity index, while institutional ownership was defined using a dummy variable equal to one if institutions held more than 5% of a firm’s shares. Following the literature, control variables including SIZE, LEV, ROA, BTM, SIGMA, and RET were included in the regression models. The statistical analysis was conducted using fixed-effect panel regressions estimated with least squares and HC3 heteroskedasticity-robust standard errors. Findings: The empirical results show that stock liquidity plays a critical role in increasing crash risk. This supports the "bad news hoarding and sudden release" theory, especially in markets like Iran where short selling is restricted and information transparency is limited. Specifically, the Amihud illiquidity index—used as an inverse measure of liquidity—showed a positive and significant coefficient in the NCSKEW model (β = 0.0386; p < 0.01). This implies that the more liquid a stock is, the more likely it is to experience a price crash triggered by the delayed release of negative news. In contrast, in the DUVOL model, the effect of liquidity was negative but statistically insignificant, indicating that DUVOL is less sensitive to liquidity than NCSKEW. Institutional ownership alone had no significant direct effect on crash risk; however, its interaction with liquidity (LIQ × DINST) was highly significant: the interaction coefficient was −0.0040 in the NCSKEW model and +0.0044 in the DUVOL model (both at p < 0.01). This contrast in sign suggests that the presence of institutional investors dampens the protective effect of illiquidity. While the relationship remains negative in the skewness-based model, it becomes weaker; in the volatility-based model, it becomes nearly neutral. Moreover, the analytical derivative showed that the inflection point occurs at an institutional ownership level of approximately 6.14%. Given that nearly two-thirds of firms in the sample have institutional ownership above this threshold, the findings indicate that the combination of high liquidity and high institutional ownership—especially in the presence of short-term-oriented institutions—intensifies the risk of a stock price crash for most companies. This outcome is consistent with the short-termism hypothesis. Conclusion:The findings suggest that while liquidity is essential for market efficiency, in environments lacking control mechanisms such as short selling, it may paradoxically function as a risk factor. Additionally, policymakers and regulators should be aware that institutional ownership is not inherently stabilizing; rather, the nature and behavior of institutional investors (active vs. passive, long-term vs. short-term) are critical in shaping their impact. These results offer valuable insights for improving disclosure practices and regulatory frameworks in Iran’s capital market.

https://jfmp.sbu.ac.ir/article_105912_b0b59deb150d3cdcecf4254549efe049.pdf

Shahid Beheshti University Financial Management Perspective 2645-4637 14 48 2025 01 20 The Effect of Overconfidence of the Manager and Internal Financing on the Scale of Investment Efficiency The Effect of Overconfidence of the Manager and Internal Financing on the Scale of Investment Efficiency 119 139 105937 10.48308/jfmp.2025.239643.1487 FA Dariush Damoori Associate Professor Yazd University, Department of Accounting, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran. 0000-0002-0650-1637 Zahra Momeni Safari Koochi MSc in Financial Management, Department of Accounting , Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran 0009-0008-8959-877X Habib Ansari Samani Associate Prof, Department of Economics, Faculty of Economics, Management and Accounting, Yazd University, Yazd, Iran. 0000-0002-0075-5097 Journal Article 2025 04 25 Abstract: objective Behavioral finance is a field that studies how psychological biases and managerial personality traits influence financial decision-making. One notable behavioral bias is managerial overconfidence, a critical factor in corporate decision-making. Overconfident managers tend to overestimate their own abilities and underestimate potential risks, which can lead to suboptimal financial decisions. This study aims to explore the impact of managerial overconfidence and internal financing on the efficiency of corporate investment in companies listed on the Tehran Stock Exchange. Specifically, it investigates whether managerial overconfidence affects investment efficiency and internal financing and whether internal financing serves as a mediating variable in this relationship. Methodology The research adopts an applied and ex-post factor design, utilizing regression analysis and panel data techniques. Data for this study were collected from various sources, including the capital market, corporate financial statements, accompanying notes, and related documents. The statistical population consists of all companies listed on the Tehran Stock Exchange over an eight-year period, spanning 2015 to 2022. Through systematic elimination sampling, a final sample of 130 companies was selected. The study tests its hypotheses using a multivariate linear regression model. Data collection tools included the Rahavard Novin software and the Codal website, while data analysis was conducted using EViews 12 and Microsoft Excel. Findings The analysis reveals that internal financing mediates the relationship between managerial overconfidence and investment efficiency. There is a negative and significant correlation between managerial overconfidence and internal financing, suggesting that overconfident managers are less reliant on internal sources of financing. Furthermore, internal financing has an inverse and statistically significant effect on investment efficiency, indicating that internal financing alone does not directly improve investment outcomes. The study also finds no significant relationship between internal financing and the prevalence of overinvestment or underinvestment, further underscoring its limited role in driving investment efficiency. Conclusion The results suggest that managerial overconfidence reduces the reliance on internal financing, a finding that contrasts with prior research. This shift may be attributed to the inclination of overconfident managers to pursue external financing, especially when such opportunities are readily available. The presence of external financing options appears to decrease the dependency on internal financing and mitigate the likelihood of suboptimal investment decisions. Empirical evidence further highlights that internal financing does not directly enhance investment efficiency but instead acts as a mediating factor between managerial overconfidence and investment efficiency. Overconfident managers often overestimate the profitability of investment opportunities and underestimate associated risks, which can lead to deviations in investment strategies. By overvaluing potential returns and undervaluing risks, these managers are more likely to engage in inappropriate or misaligned investment projects. This study underscores the importance of recognizing behavioral biases like overconfidence and their implications for corporate financing strategies and investment efficiency. It also emphasizes the need for balancing internal and external financing sources to achieve more optimal investment outcomes. Abstract: objective Behavioral finance is a field that studies how psychological biases and managerial personality traits influence financial decision-making. One notable behavioral bias is managerial overconfidence, a critical factor in corporate decision-making. Overconfident managers tend to overestimate their own abilities and underestimate potential risks, which can lead to suboptimal financial decisions. This study aims to explore the impact of managerial overconfidence and internal financing on the efficiency of corporate investment in companies listed on the Tehran Stock Exchange. Specifically, it investigates whether managerial overconfidence affects investment efficiency and internal financing and whether internal financing serves as a mediating variable in this relationship. Methodology The research adopts an applied and ex-post factor design, utilizing regression analysis and panel data techniques. Data for this study were collected from various sources, including the capital market, corporate financial statements, accompanying notes, and related documents. The statistical population consists of all companies listed on the Tehran Stock Exchange over an eight-year period, spanning 2015 to 2022. Through systematic elimination sampling, a final sample of 130 companies was selected. The study tests its hypotheses using a multivariate linear regression model. Data collection tools included the Rahavard Novin software and the Codal website, while data analysis was conducted using EViews 12 and Microsoft Excel. Findings The analysis reveals that internal financing mediates the relationship between managerial overconfidence and investment efficiency. There is a negative and significant correlation between managerial overconfidence and internal financing, suggesting that overconfident managers are less reliant on internal sources of financing. Furthermore, internal financing has an inverse and statistically significant effect on investment efficiency, indicating that internal financing alone does not directly improve investment outcomes. The study also finds no significant relationship between internal financing and the prevalence of overinvestment or underinvestment, further underscoring its limited role in driving investment efficiency. Conclusion The results suggest that managerial overconfidence reduces the reliance on internal financing, a finding that contrasts with prior research. This shift may be attributed to the inclination of overconfident managers to pursue external financing, especially when such opportunities are readily available. The presence of external financing options appears to decrease the dependency on internal financing and mitigate the likelihood of suboptimal investment decisions. Empirical evidence further highlights that internal financing does not directly enhance investment efficiency but instead acts as a mediating factor between managerial overconfidence and investment efficiency. Overconfident managers often overestimate the profitability of investment opportunities and underestimate associated risks, which can lead to deviations in investment strategies. By overvaluing potential returns and undervaluing risks, these managers are more likely to engage in inappropriate or misaligned investment projects. This study underscores the importance of recognizing behavioral biases like overconfidence and their implications for corporate financing strategies and investment efficiency. It also emphasizes the need for balancing internal and external financing sources to achieve more optimal investment outcomes.

https://jfmp.sbu.ac.ir/article_105937_ac3df97ea553d07878f346448d4ffca5.pdf