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Blockchain technology, due to its prominent features such as information transparency, high security, data immutability, and the ability to eliminate intermediaries, has the potential to transform the banking industry. By creating a distributed ledger, this technology enables secure, fast transactions without the need for central authorities, which can reduce operational costs and improve the efficiency of banking systems. This research was conducted to identify and determine the significance of critical success factors of blockchain technology in the banking industry, aiming to help banks effectively leverage this emerging technology. First, critical success factors were identified through a comprehensive literature review, and then they were screened using the fuzzy Delphi technique. Subsequently, the importance of each factor was calculated using the Bayesian best-worst method, a multi-criteria group decision-making approach. The findings indicated that high scalability and fast transaction processing speed, security and standardization of blockchain technology, and interoperability with existing systems are among the most critical success factors. Finally, based on the results and with the goal of improving the efficiency and security of banking systems, practical and research recommendations were provided.

This paper investigates the breakdown of a money-demand relationship derived from the Quantity Theory of Money for Iran during episodes of large fluctuations in the real value of oil exports. The joint rise in the inverse velocity of money and the real value of oil exports in the 1970s and 2000s suggests that conventional specifications fail to capture the oil-driven component of money demand. This issue is crucial for monetary policy, because the central bank must distinguish whether increases in desired real money balances stem from supply-side expansions in the transaction base or from shifts in money demand in order to calibrate an appropriate policy response. We hypothesise that real GDP is an inadequate proxy for the supply of goods and services in the Quantity Theory of Money framework, because swings in natural-resource rents from oil are largely excluded from real GDP growth even though, in practice, oil rents finance additional absorption and effective supply. To address this, we examine the impact of both the real price and the volume of oil on the inverse velocity of money using linear ARDL and nonlinear NARDL models. Using these models, we find a long-run level relationship between inverse velocity, the real oil price, and oil production, with positive long-run effects and a significant error-correction term. Asymmetry is concentrated in the short run for oil prices: positive shocks raise inverse velocity more than negative shocks reduce it. For oil production, increases lift inverse velocity while declines are muted, consistent with fiscal smoothing, monetization, and offsetting macroeconomic adjustments under sanctions. Policy should therefore be explicitly oil-contingent, separating oil-driven shifts in money demand from policy-induced balance-sheet expansion, and relying more on quantity instruments combined with fiscal and foreign-exchange sterilization during oil booms.

In today's complex economic landscape, accurately predicting events such as customer loan defaults presents a significant challenge for financial institutions. Traditional methods have shown limitations in accuracy, prompting the adoption of data-driven machine learning techniques for enhanced predictive capabilities. This study investigates the efficacy of novel machine-learning algorithms compared with linear models for predicting loan defaults at a major commercial bank. Data from over six thousand customer loan files spanning 2019 to 2022 were collected, cleaned, and clustered based on key loan indicators. The accuracy of predicting loan defaults was first evaluated using popular machine learning classification models, including LightGBM, XGBoost, Multilayer Perceptron, and Logistic Regression, and XGBoost performed best. After that, prediction accuracy was evaluated using various time-series machine learning algorithms, with a particular focus on a combined Gradient Boosting and Long Short-Term Memory (LSTM) approach. Results indicate that the combined algorithm outperforms traditional linear models, showing a substantial 40% improvement over the ARIMA algorithm in predicting loan default behavior. This study underscores the potential of advanced machine learning techniques to enhance predictive accuracy in the banking sector, offering valuable insights for risk assessment and financial decision-making.

Iran’s capital market has witnessed substantial transformation over recent decades. However, the sharp downturn of the stock market in 2020 represented a critical juncture, extending beyond retail investors’ financial losses and culminating in a widespread public trust crisis. Addressing this issue, the present study proposes a neurofinance-based model aimed at strengthening public investor trust in Iran’s capital market. The model was developed through a mixed-methods design, integrating qualitative grounded theory exploration with quantitative validation via structural equation modeling (SEM). In the qualitative phase, data were collected in 2025 through semi-structured interviews with 17 capital market experts and analyzed using a three-stage coding procedure comprising: open, axial, and selective coding. In the quantitative phase, the proposed theoretical model was empirically tested using SEM on data obtained from 87 investors in the capital market. The qualitative findings reveal that the development of trust is shaped by causal conditions (e.g., information transparency and emotional responses), contextual conditions (e.g., economic stability and social capital), and intervening conditions (e.g., media, education, and supportive institutions). The quantitative results confirm that all model paths are statistically significant and that the model demonstrates an acceptable level of fit. Accordingly, strategies such as enhancing transparency, empowering retail investors, and promoting financial literacy were proposed, generating outcomes at the individual, market, and macro levels. The novelty of this research lies in integrating a neurofinance perspective with a mixed-methods approach to develop a context-specific model aligned with Iran’s institutional and cultural environment.

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.

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.