Petroleum Business Review

Abstract Purpose: This study examines the impact of product diversification on asset returns in petrochemical companies using a kernel density approach, addressing contradictory findings in the diversification-performance literature within this capital-intensive sector.
Design/methodology/approach: Employing an ex-post facto research design, we analyzed data from petrochemical companies listed on the Tehran Stock Exchange from 2011 to 2021. The study utilized a two-stage analytical approach: first classifying companies into life cycle stages (growth, maturity, decline) using discriminant analysis, then testing hypotheses through multivariate regression and cross-sectional analysis. The kernel density estimation provided a non-parametric assessment of diversification effects.
Results: The results demonstrate that product diversification significantly enhances asset returns in petrochemical firms, with the Herfindahl index revealing seven distinct market concentration levels. The positive effect of diversification was particularly pronounced in monopolistic market structures. Empirical evidence confirms that diversification serves as an effective strategy for improving financial performance and mitigating market dependence.
Originality/value: This research contributes to the literature by introducing kernel density estimation to diversification analysis, providing a more nuanced understanding of return distributions beyond traditional parametric methods. The study also offers unique insights into the petrochemical sector's dynamics in emerging economies, highlighting how market structure moderates the diversification-performance relationship.
Practical implications: The findings suggest that managers should prioritize diversification strategies, especially in monopolistic environments, to enhance asset utilization and financial resilience. Policymakers can use these insights to design industrial policies that encourage strategic diversification in response to Iran's economic conditions.

Abstract The need to speed up the creation of innovation in the oil industry and support it properly in order to respond to the increasing needs of the world today has caused activists in this field to use various forms of protection. Patent systems and trade secrets are the main ways of protecting intellectual achievements in the oil industry. In recession conditions such as the Corona epidemic, which had a negative impact on the production and income generation of oil companies, intangible assets such as trade secrets or registered inventions could be a very good source of income for these companies. The complexity and longevity of technology in the upstream field of the oil industry has caused the reluctance of companies active in this field to register patents, which will slow down the dissemination of knowledge, creation and development of innovation. Therefore, due to the necessity of energy production, lack of resources and the need for complex and combined technologies in this field, patenting becomes inevitable.

Abstract The present study examines the investment model of the National Development Fund (NDF) in the upstream oil and gas industries, focusing on the risks associated with field development. Given the NDF's recent entry into the investment arena, the challenges in this field have been analyzed in this article. Furthermore, the NDF 's investment policies and guidelines, project prioritization, and the Non-Meddle Investment model (I-HOPE) as the NDF’s main strategy to reduce risk and increase resource effectiveness are introduced and analyzed in detail. Finally, alongside a comparative review of the NDF’s investment model and field development challenges, solutions are proposed to improve the NDF’s investment process in this sector. This study, by evaluating the strengths and weaknesses of the I-HOPE model in confronting the inherent risks of field development, provides a framework for optimizing future investments. These measures help the NDF to optimally manage risks, increase resource returns, and play a more effective role in the country's economic development.
Keywords: Non-Meddle Investment Model, Risk, Uncertainty

Abstract 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.

Abstract The compilation of a list of disqualified entities by NIOC serves as both a supervisory and deterrent mechanism to prevent violations and mitigate legal and financial risks associated with high-risk contracts. This directive has been designed with the objective of enhancing the integrity of the tendering process, ensuring fair competition, enforcing regulations governing public procurement and governmental transactions, and guaranteeing that work is not repeatedly delegated to companies that have demonstrated an unsatisfactory performance history in prior projects. However, an exclusive focus on the legal personality of companies may reduce the deterrent effect, as offending entities may continue their activities under subsidiary or shell companies.
This study proposes the inclusion of the managing director and members of the board of directors—as the key natural persons influencing a company’s decision-making—alongside the legal entity itself in the blacklist of disqualified contractors, presenting this measure as a practical and low-cost approach to enhancing the effectiveness of the disqualification system. Using a descriptive–analytical method, the research reviews the current directive, outlines the legal foundations of managerial accountability, and evaluates both the feasibility and effectiveness of this proposed reform, while also addressing the practical and legal challenges associated with piercing the corporate veil. The findings indicate that extending the disqualification list to relevant individuals can meaningfully strengthen the deterrent function of the existing supervisory mechanism

Abstract 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.