Transactions on Quantitative Finance and Beyond

This study investigates the relationship between audit quality and earnings management, based on Loan Loss Provisions(LLPs), in 15 active banks listed on the Tehran Stock Exchange from 2010 to 2024. By analyzing data from these sample banks, this study evaluates the impact of various factors on Earnings Management Practices(EMP), including audit firm size, auditor tenure, and auditor specialization in the banking industry. The results indicate a significantly negative relationship between audit firm size and earnings management, suggesting that banks audited by larger firms are less likely to engage in earnings manipulation. Conversely, a positive and significant relationship was found between auditor tenure and earnings management, implying that prolongedauditor-client relationships may lead to a decline in auditor independence, thereby facilitating earnings management. Additionally, auditors with specialized knowledge in the banking sector demonstrate a greater ability to detect and mitigate opportunistic earnings management behaviors, contributing to a reduction in earnings manipulation within these banks

The present study aims to examine and explain the role of rail transport in Iran’s economic development from the customers' perspective. This research was conducted using a survey method, employing a questionnaire distributed among 384 rail transport customers. The validity of the questionnaire was confirmed by five experts, and its reliability was assessed through Cronbach’s alpha coefficient, with values exceeding 0.7 for all variables, indicating satisfactory reliability of the measurement tool. Data analysis was performed using SPSS software. The results revealed that rail transport has a significant impact on attracting global business opportunities, economic competitiveness, traffic congestion reduction, and road maintenance. Additionally, this study examined the relationship between rail infrastructure development and its impact on economic productivity. The findings indicated that increased investment in the rail transport sector can enhance transportation efficiency and improve Iran's trade conditions. One of the most critical outcomes of this study was identifying the challenges hindering the expansion of the rail system, including high development costs and a lack of attractiveness for private investors. This research can assist policymakers in developing appropriate strategies to improve rail transport. The findings align with international studies in this field, emphasizing the importance of this sector in sustainable economic development. It is recommended that precise planning be undertaken to expand rail networks and enhance service quality in this domain

This research investigates the relationship between Economic Policy Uncertainty (EPU), credit risk, and lending decisions using the Generalized Method of Moments (GMMs)over the period from2019 to 2023 for12banks listed on the Tehran Stock Exchange. The study employs three regression models to analyze the dynamics of Non-Performing Loans (NPLs), Loan-To-Deposit Ratios(LTDRs), and Return onAssets (ROAs)within the banking sector. Findings reveal a significant persistence in NPLs, indicating that banks with higher past NPLs face ongoing challenges that adversely affect their financial health. A notable negative relationship between Leverage(Lev) and Non-Performing Loan Ratio (NPLR)suggests that more leveraged banks may implement effective risk management strategies, reducing their exposure toNPLs. Additionally, capital adequacy emerges as a critical factor, with higher capital ratios correlating with lower NPLs. The analysis of LTDR indicates thatLevand capital adequacy significantly influence lending practices, while a marginally significant relationship between EPU and LTDR suggests external uncertainties may slightly impact lending decisions. Model results further demonstrate strong persistence in profitability, with historical ROA positively predicting current ROA. Overall, this study underscores the importance of effective risk management practices in banking and highlights ongoing challenges posedbyNPLs, particularly for larger institutions. Recommendations include prioritizing capital buffers and monitoring lending practices to mitigate risks while fostering sustainable profitability growth. Future research should explore additional variables to elucidate the complexities of banking performance metrics

This research examines the performance of banks' Asset Liability Management (ALM) strategies using Data Envelopment Analysis (DEA) to improve bank efficiency and estimate the efficiency scores of emerging banks. ALM is an essential process for financial institutions to manage their assets and obligations effectively, ensuring profitability, liquidity, and risk oversight, while DEA offers a comprehensive methodology for evaluating and comparing the efficiency of Decision-Making Units (DMUs). By utilizing DEA in the context of ALM, this research seeks to uncover inefficiencies and recommend optimization strategies. The results reveal considerable differences in efficiency levels, underscoring potential improvement areas and best practices. This study adds to the existing literature by illustrating the practical use of DEA in ALM and providing actionable insights for banks to boost their performance

Conventional gasoline price can affect the government and society as a strategic commodity in the community. Conventional gasoline price fluctuations have economic, political, social, cultural, and environmental effects. Thus, the prediction of its volatility is essential but there is not any study to examine the price fluctuations. This study aims to hybridize and propose different Garch models based on two distributions and various algorithms in machine learning, such as random forest, ridge regression, Support Vector Regression (SVR), and elastic-net for predicting weekly gasoline price volatility. The results depict Garch and GJRgarch models based on t-student distribution can predict volatility. The combination of ridge regression and GJRgarch model can better predict volatility for the seven-step-ahead. The RMSE scale has been used to compare results that the scale value is 0.01475 in the hybrid method. In fact, combining the ridge regression with t-student-GJRgarch model has the slightest error prediction or the most accuracy among different Garch models and machine learning algorithms

This research investigates how CEO traits influence a company's financial outcomes. The study, which focused on firms listed on the Tehran Stock Exchange (TSE) from 2013 to 2023, employed panel data models for estimation and testing. Indicators like CEO ownership, duality, and tenure were utilized to represent CEO characteristics, while Return-on-Assets (ROA) was used to evaluate Firm Performance (FP). The findings revealed a positive and significant correlation between CEO ownership and tenure with FP (ROA). Additionally, CEO duality did not show a significant connection with FP. Although similar research has been conducted in developed markets, comprehensive studies of this nature have been relatively rare in the Iranian economic context, which possesses distinct features such as the impact of governmental institutions, specific taxation regulations, and an ownership structure characterized by concentration