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West Texas Intermediate (WTI) crude oil is a pivotal benchmark in the global energy market, exerting a decisive influence on economic expectations and national macroeconomic policies. As the primary pricing basis on the New York Mercantile Exchange and numerous energy futures contracts, WTI is subject to persistent and severe price volatility. Such fluctuations, often appearing as abrupt upward or downward shocks, profoundly affect key macroeconomic indicators. These include inflation, economic growth, trade balances, corporate profitability, production costs, and government budgets. Consequently, variations in WTI prices influence oil and gas markets, financial stability, energy security, and even international geopolitical relations. To address these challenges, this study develops a hybrid VMD+SVR framework to model and forecast WTI crude oil futures prices across short-, medium-, and long-run horizons. Empirical findings reveal that across all three horizons, the proposed hybrid model consistently achieves the lowest forecasting errors compared with alternative approaches. Moreover, the Diebold–Mariano and Wilcoxon tests statistically confirm the superior predictive performance of the hybrid VMD+SVR model. These results highlight the importance of integrating advanced adaptive signal decomposition (VMD) with powerful nonlinear learning algorithms (SVR) for accurate oil price forecasting. The proposed approach not only enhances forecasting accuracy but also provides practical insights for policymakers in managing economic risks, stabilizing budgets dependent on oil revenues, and formulating sustainable energy strategies. It opens a new avenue for developing financial forecasting models inspired by advanced signal processing.

The exchange rate in Iran’s economy, is one of the most critical indicators influencing fiscal, trade, and monetary policymaking. Given that a substantial portion of the country’s foreign exchange earnings is dependent on crude oil exports, any fluctuations in oil revenues can directly or indirectly affect the exchange rate. This relationship becomes especially significant under circumstances such as international sanctions, global oil price volatility, and the structural dependency of the government budget on oil revenues. Consequently, a precise understanding of the mechanisms by which oil income influences the exchange rate is essential for designing sustainable currency policies and ensuring the country’s macroeconomic stability. The present study investigates the long-term relationship between oil revenues and the exchange rate in Iran during the years 2002 to 2024, utilizing the Fully Modified Ordinary Least Squares (FMOLS) econometric method. Through the analysis of quarterly time series data and unit root tests, the stationarity and then cointegration of the variables were examined. The test results indicate that the variables are integrated of order one, and a cointegration relationship exists among them, justifying the estimation of the model using FMOLS.The findings reveal that, according to the model estimates, oil revenues and non-oil exports exert a significant negative effect on the exchange rate. Specifically, a one-percent increase in oil revenues leads to a 0.41 percent decrease in the exchange rate (signifying an appreciation of the Rial), while an increase in non-oil exports results in a 0.72 percent decrease in the exchange rate.

The primary objective of this study is to develop a predictive model for money laundering risk in Iranian listed firms. Initially, firm-level money laundering risk is measured using auditor assessments of anti-money laundering (AML) activities disclosed in annual audit reports. Subsequently, a quantitative modeling approach is employed, using financial and governance-related variables identified in prior research. To validate the quantitative findings, a qualitative approach based on grounded theory is also applied to identify additional explanatory factors. This research follows a mixed-methods design, incorporating both quantitative and qualitative phases. In the quantitative phase, a panel logit regression model is estimated using data from 1,680 firm-year observations covering the period 2012–2023. Independent variables include firm size, return on equity, leverage, investment opportunities, board independence, and board size. In the qualitative phase, semi-structured interviews were conducted with 10 experts to identify key risk factors, followed by the design and administration of an 18-item questionnaire distributed to 110 professionals. Exploratory factor analysis was then used to extract latent variables. The quantitative analysis reveals significant relationships between money laundering risk and several variables, such as firm size (positive), return on equity (negative), leverage (positive), and board independence (negative). The qualitative analysis identifies three core factors: (1) organizational culture and employee training, (2) corporate governance, and (3) a composite factor comprising compliance, organizational complexity, financial performance, firm size, and capital structure. Together, these factors explain over 50% of the variance in expert responses. The convergence of results from both methodological approaches confirms the robustness of the proposed model. Corporate governance indicators—particularly board size and independence—alongside financial attributes such as firm size, profitability, and capital structure, are found to be significant predictors of firm-level money laundering risk. The findings underscore the importance of strengthening internal control mechanisms and compliance structures in reducing money laundering risk.

Sustainable Communications (SC) is a strategic approach in industries, especially in high-risk sectors, and has gained great importance today. This study explores the non-linear dynamics (U-shaped and S-shaped) between sustainable communication (SC) and financial performance (FP) in high-risk industries, including oil and gas, petrochemicals, mining, and transportation, listed on the Tehran Stock Exchange and Iran Fara Bourse over the period 2015–2024 (48 companies). Utilizing panel regression, cross-sectional regression, quantile panel regression, and Granger causality tests, the analysis integrates organizational learning, supply chain perspectives, and stakeholder theory. Findings confirm U- and S-shaped relationships, suggesting that moderate SC enhances FP, whereas excessive communication may undermine stakeholder trust, with sustained efforts yielding long-term benefits. Financial sector development amplifies the positive effects of SC, while ESG rating divergence exacerbates the adverse impacts of over-communication. Sustainability reporting requirements reinforce the benefits of balanced SC. Quantile regressions reveal heterogeneity, with stronger SC effects in high-performing firms. Granger causality tests indicate unidirectional causality from SC to FP. Industry-specific analyses highlight superior performance in petrochemicals and challenges in transportation. The study offers practical implications for optimizing SC, strengthening financial sector development, and standardizing ESG reporting. Future research should incorporate granular ESG data and dynamic modeling approaches.

Financing upstream oil and gas projects in Iran—particularly for non-governmental companies—faces numerous obstacles, including the lack of effective guarantee mechanisms, absence of credible collaterals, limited access to sovereign guarantees, and the skepticism of financial institutions. This paper, adopting a descriptive-analytical approach and grounded in the study of existing legal and institutional frameworks, explores the legal capacities for designing and utilizing alternative instruments to traditional guarantees in the financing process of high-risk projects. Within this framework, and based on the “Production and Infrastructure Financing Act”, the executive bylaw on the establishment of non-governmental guarantee funds, and other upstream legal documents, the establishment of an institution titled the "Oil Guarantee Fund" is proposed as an innovative solution. This fund is designed to mitigate default risk, facilitate project credit assessment, and replace bank or property-based guarantees for companies active in the oil and gas value chain. Its legal structure is envisioned based on the model of non-governmental institutions and private joint-stock companies. The proposed model has been formulated through a legal analysis of the relevant documents and informed by the outcomes of expert panels comprising specialists in energy law, finance, and public policy. Findings of the study indicate that the establishment of such a fund, capable of issuing valid guarantees based on contractual commitments, provides a practical tool to strengthen institutional trust and facilitate investment in upstream oil and gas projects. The article also presents a conceptual and institutional framework for the fund and offers recommendations for its policy-level and legal implementation.

Iran's gas industry plays a strategic role in supplying and distributing natural gas energy to society. The project-based, operational, and service-oriented nature of this industry, combined with its large workforce, underscores the necessity to address the risks confronting its human resource management system. This study was conducted to identify and analyse these human resource management risks. This study employed a mixed-methods approach. In the qualitative phase, the researcher's lived experience was utilized to identify and categorize 35 human resource management risk factors in Iran's gas industry. For the quantitative phase, a risk management framework was applied to evaluate each identified factor based on two key dimensions: risk probability and risk impact. The assessment results were subsequently visualized through a heat map. The study results demonstrated that none of the indicators fell within the low-risk zone, with 15 indicators classified as medium-risk and 20 as high-risk, while two critical indicators - imposition of salary and benefit restrictions with a risk rating of 9 and dependence on political affiliations for filling key positions with a risk rating of 8 - were identified as the most substantial challenges facing the human resource management system in Iran's gas industry.