درخت حوزه‌های تخصصی

This study aims to develop a working capital management model for companies listed on the Tehran Stock Exchange. The proposed model determines the expected level of working capital for a company, enabling it to create the highest possible value. Additionally, this model can be used to assess the efficiency of working capital management. The discrepancy between the actual level of working capital and the expected level serves as an indicator of inefficiency in working capital management. Initially, based on theoretical foundations and expert opinions, 28 variables affecting working capital were selected. Then, using the operational working capital index, the research models were estimated using multiple regression and genetic algorithm techniques for data from 156 companies over the period from 2011 to 2022. Influential variables were identified and filtered. Finally, suitable working capital management models were identified based on two criteria: (1) the strong correlation between the errors of the fitted models and the working capital efficiency of the company, and (2) the model’s accuracy in identifying companies prone to excess or shortage of working capital. In total, after estimating 119 different models using regression and genetic algorithm methods, four suitable working capital management models were determined. The regression method resulted in models with an average accuracy of 77.27% and 79.54% for the dependent variable of working capital and the cash conversion cycle, respectively. The genetic algorithm method resulted in models with an average accuracy of 89.03% and 82.08%. The final model, with the cash conversion cycle as the dependent variable, was identified as the best model. It includes the variables of the previous year's cash conversion cycle, company-specific risk, gross profit margin, trade credit, growth opportunities, operating cycle, economic policy uncertainty, and exchange rate changes.

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).

Embedded options are virtually new instruments identical to options in many aspects except their non-tradable nature. Testing the efficiency of the Variance Gamma and Black-Scholes-Merton model on these instruments would provide a vision of transitioning from the classical model with its deficiency to more intricate models. Considering the complicated nature of the Variance Gamma stochastic process to price options, the Fast Fourier Transform (FFT) method is used in conjunction with the Nelder-Mead Simplex method to calibrate models. This research uses the Fast Fourier Transform (FFT) to price four embedded options with the ticker symbols Hefars912, Heghadir912, Heksho208, and Hetrol911 under the two models. The result approves that the Variance Gamma process is more efficient than the Black-Scholes-Merton model in pricing embedded options. Consequently, the variance gamma process would generate fewer errors in pricing those options that can be used in a practical sense.

For decades, debates surrounding the drivers of inflation and exchange rate fluctuations in Iran's economy have persisted. Despite this, a well-established and widely accepted theory among economists exists to explain long-term inflation. This study aims to demonstrate that natural resource rents, particularly oil rents, have partially bridged the aggregate demand-supply gap in the commodity market and the excess money supply gap during periods of increased real oil exports, primarily driven by favorable terms of trade for Iran. Consequently, this leads to two outcomes: first, inflation rates are lower than those implied by monetary theory, and second, exchange rate appreciation is less than what predicted by monetary theory. Preliminary findings suggest that these two claims are plausible. Indeed, the core contribution of this study lies in providing a theoretical explanation and empirical analysis of the impact of oil rents on taming inflation and stabilizing exchange rates in Iran, consistent with established macroeconomic frameworks.

Article 3 of the new law of the Central Bank of the Islamic Republic of Iran identifies supporting economic growth and employment as one of its primary objectives. Moreover, other articles of this law also assign the Governor of the Central Bank the responsibility of submitting periodic reports to relevant authorities regarding the degree of achievement of this objective. In economic literature, expansionary monetary policy is usually mentioned as the Central Bank’s tool for supporting economic growth. However, with a redefinition of this role, it can be argued that this path is not exclusive, and the Central Bank can also exert positive and significant influence on economic growth through two alternative channels: maintaining banking stability and promoting banking technologies. Using the Panel ARDL method for D8 countries, the empirical results confirm that the coefficients of variables related to banking stability and banking technology have a positive and significant impact on economic growth, consistent with the theoretical foundations of this study. Hence, the objective envisioned by the legislator in the new law can be pursued by the Central Bank of Iran through these two channels as well.

In recent decades, financial inclusion has been recognized as a key pillar of sustainable development and economic justice in developing countries. This study analyzes the performance of Qard al-Hasan funds as community-based Islamic financial institutions in promoting financial inclusion in Iran. Three core dimensions, access, usage, and quality, were examined in relation to macroeconomic variables such as poverty reduction and economic stability. Using structural equation modeling (SEM), time-series data for 2017-2024 (1396-1403 in the Iranian calendar) were collected from the Central Bank of Iran and the Statistical Center of Iran. The findings showed that Qard al-Hasan funds significantly improved financial inclusion, which mediated reductions in multidimensional poverty and enhanced stability. Access to financial services (β = 0.529) and quality of services (β = 0.639) had the strongest impact on poverty reduction, while inflation and unemployment (β = 0.240) affected fund performance. By mobilizing deposits, fostering investment, and reducing financial volatility, the funds assumed a pivotal role in sustainable development. The results emphasize the need to integrate modern financial technologies with the traditional structure of these institutions and highlight policy reforms to strengthen their social and economic effectiveness. By providing an applied empirical model, the study offers a basis for designing comprehensive financial policies to promote justice and inclusive development in Iran. All analyses were conducted under sanction conditions (2017-2024), directly reflecting the funds’ role in managing economic constraints. Notably, even amid international sanctions, when access to external finance and banking resilience were severely restricted, Qard al-Hasan funds contributed meaningfully to financial inclusion. Iran’s experience can thus serve as a practical model for other countries facing international pressures.