درخت حوزه‌های تخصصی

Enterprise risk management (ERM) represents a new paradigm that supports organizations in identifying, evaluating, and managing risks. Several factors encourage different organizations, especially banks, to design and apply ERM; among them, the possibility of financial problems and related costs, financial performance decline, growth opportunities, and independence of the board of directors can be pointed out. In addition, applying a suitable risk management strategy is a competitive advantage for supporting companies. Following the previous comprehensive studies, the present study was done by integrating a meta-composition approach and multivariate fuzzy network analysis. In this research, to identify effective factors on ERM based on empirical evidence and selection of studies, description and classification of selected articles, analysis of the content of selected research articles, and finally, the importance and refinement of the identified factors based on the Delphi expert opinion polling technique, and multivariate fuzzy network analysis was discussed. The study aimed to identify and rank the factors affecting the effectiveness of ERM of the firms accepted in selected banks in Iran. While reviewing studies, a semi-structured interview was conducted using an Exploratory-Descriptive Qualitative (EDQ) research design to determine the factors that affect ERM effectiveness. The interviewed experts were comprised of 20 university professors, CEOs, financial analysts of investment and brokerage companies, and senior auditors with accounting and management education. The literature review and the results of the interviews indicate five main themes that classify the factors affecting ERM effectiveness in firms. In this study, key factors were identified, and then the fuzzy Delphi technique was employed to rank and find the weight of the factors. The results showed that corporate governance, financial indicators, environmental indicators, company characteristics, and management indicators effectively enhance ERM's effectiveness. Accordingly, capital market analysts and investment companies should take a broader perspective and make decisions based on companies' financial risks instead of paying attention to companies' profitability and stock price changes. The results demonstrated that five factors determine ERM effectiveness: 1. corporate governance indicators (monitoring the board of directors and ownership structure), 2. Financial indicators (return on assets (ROA), earnings volatility, merger and acquisition (M&A) activities, financial deficiency, and capital opacity), 3. Environmental indicators (performance excellence, industry competition, audit firm credibility, environmental uncertainty, and industry), 4. Firm characteristics (financial leverage, firm size, and growth opportunities), and 5. Management indicators (management career and business diversity).

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

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

Monetary policy is one of the most important tools for policymakers to influence macroeconomic variables including production. However, implementing this policy sometimes yields unintended consequences. Understanding monetary policy transmission mechanisms is therefore critical for effective implementation. Research following the 2008-2009 financial crisis indicates that the shadow banking activity can disrupt the monetary policy transmission and weaken its effectiveness. An analysis of Iran’s financial system reveals increasing shadow banking activity. This paper therefore examines how shadow banking affects monetary policy transmission in Iran using a DSGE model that innovatively incorporates the shadow banking sector. We compare two scenarios: a financial system without shadow banking and one including shadow banking. The effects of two contractionary monetary policies—interest rate increase and reductions in money supply growth—on GDP, investment, and inflation were analyzed under each scenario. The findings indicate that shadow banking diminishes monetary policy’s impact on all three variables by weakening the credit channel of monetary policy transmission. In the scenario without shadow banking, In the scenario without shadow banking, all three variables will decline in response to the monetary shock of decreasing money supply growth. However, in the scenario with shadow banking, investment levels are not declining but rising. The impact of monetary policy on output and inflation is diminished in the presence of shadow banking. In the case of interest rate shocks, the results also indicate a negative impact of shadow banking on the effectiveness of monetary policy.