Financial and Banking Strategic Studies

Abstract Purpose: The banking industry is considered one of the most important sectors of the country's economy, as it can drive growth and prosperity by effectively managing its resources and expenses. In competitive conditions, the continuity of banks' activities depends on their sound performance, and poor liquidity management can lead to bankruptcy. In this regard, this study aims to determine the impact of the combination of asset and capital structures on liquidity generated by banks listed on the Stock Exchange during the period 2017-2023 (19 banks).
Methodology: This research is considered applied research in its purpose, and it uses field investigation and document analysis. The extracted information has been tested and analyzed using descriptive statistics and a regression model in EViews.
Findings: The results confirm the six research hypotheses, and it was ultimately determined that the combination of asset and capital structure has a positive and significant impact on the liquidity of banks listed on the stock exchange. In the end, some suggestions are also provided.
Originality/Value: The results of this research can be highly valuable for policymakers, managers, and investors in improving financial decision-making and optimizing capital structure management.

Abstract Purpose: In the banking industry, retaining loyal customers is considerably more cost-effective and profitable than acquiring new ones. Customer churn remains a major challenge for banks, directly reducing profitability, increasing marketing expenditures, and lowering market share. This study evaluates the performance of machine learning algorithms for predicting customer churn across branches of a state-owned bank in Iran between 2021 and 2024. By focusing on customer retention and minimizing the costs of attrition, the study aims to develop an efficient, interpretable model to identify customers at risk of churn. Methodology: This descriptive-analytical, retrospective study analyzed data from 2,025 active customers over 4 years. For each customer, 12 features covering transactional, behavioral, and demographic characteristics were collected. Following data cleaning, z-score normalization was applied. Several machine learning algorithms—including Decision Tree, Random Forest, Support Vector Machine, Multilayer Perceptron, Bayesian Network, and XGBoost—were implemented in R. Their performance was assessed through 10-fold cross-validation based on accuracy, sensitivity, and specificity. Findings: Among the 2,025 customers examined, 325 (16%) were identified as churners. Statistical tests revealed no significant differences between churners and non-churners in age, relationship duration with the bank, or average deposits over the past six months. Among the models tested, XGBoost demonstrated superior performance with an accuracy of 96.89%, sensitivity of 87.11%, and specificity of 98.71%. The area under the ROC curve (AUC) for this model was 0.9907, indicating excellent discriminatory power. Originality/Value: The contribution of this study lies in integrating advanced machine learning techniques with rigorous statistical analysis using real-world banking data. To the best of our knowledge, this is among the few studies to systematically compare multiple ML algorithms within the Iranian banking context, emphasizing both interpretability and robust validation. The findings provide practical insights for banking policymakers to design proactive strategies to improve customer retention.

Abstract Purpose: This study aims to examine the effects of government revenue and corporate bonus mechanisms on transfer pricing decisions. Additionally, the moderating role of tax minimization in these relationships is assessed.
Methodology: The study population consists of 116 companies listed on the Tehran Stock Exchange during 2017–2022. Data were collected from financial statements and official reports, and hypotheses were tested using panel data analysis and multivariate regression models.
Findings: Government revenue and the corporate bonus mechanism have a significant impact on transfer pricing. Furthermore, tax minimization positively moderates the effect of government revenue on transfer pricing but does not moderate the relationship between the corporate bonus mechanism and transfer pricing.
Originality/Value: By integrating government revenue, corporate bonus mechanisms, and tax minimization, this study provides new insights into the factors influencing transfer pricing in Iranian companies. The use of more objective measures for related-party transactions enhances the accuracy of transfer pricing assessment compared to previous studies, offering practical implications for tax authorities and policymakers.

Abstract Purpose: In today's world, the implementation of digital technologies in business sectors is increasingly important. The purpose of this study is to analyze the effects of knowledge spillovers on firm performance, with a focus on the mediating roles of digital capabilities and innovation. Additionally, the study examines the moderating role of the country's level of economic development on these relationships.
Methodology: This research adopts a quantitative approach, using data collected via questionnaires distributed to senior and middle managers at companies located in industrial zones. The sample size was determined to be 120 using Cochran's formula. Partial Least Square Structural Equation Modeling (PLS-SEM) was employed to test the mediating model explaining business performance.
Findings: Knowledge spillovers have a positive, significant impact on firm performance through digital capabilities and innovation. Furthermore, the country's level of economic development acts as a moderating factor, influencing both the direct and indirect effects of knowledge spillovers on firm performance.
Originality/Value: This study proposes a novel conceptual model to analyze the interactions among knowledge spillovers, digital capabilities, and innovation. The insights provided can serve as valuable guidance for business managers in leveraging knowledge resources, enhancing digital capabilities, and fostering organizational innovation to optimize firm performance.

Abstract Purpose: This study investigates the impact of governance quality, corruption, and selected macroeconomic variables on Foreign Direct Investment (FDI), with a particular focus on Iran. The significance of this research lies in the fact that institutional quality and corruption control are critical determinants of foreign investment inflows and play a vital role in financial development and long-term economic growth. To analyze the data, the Autoregressive Distributed Lag (ARDL) model was employed over the study period. Furthermore, diagnostic tests, including CUSUM and CUSUMSQ, confirmed the stability of the estimated coefficients, indicating that the model's short- and long-run relationships are robust. Methodology: To analyze the data, the ARDL model was used in the study period. Findings: The empirical results reveal that the Exchange Rate and the lagged effect of FDI exert a positive, statistically significant impact on FDI inflows. In contrast, inflation and weak governance indicators negatively affect foreign investment attraction, while the impact of government expenditure was found to be insignificant. Moreover, the Corruption Perceptions Index (CPI) highlights the persistence of institutional challenges in Iran, which continue to pose a serious barrier to sustainable FDI inflows. Overall, the findings emphasize that improving governance and reducing corruption are prerequisites for financial development and for strengthening FDI inflows in Iran. Originality/Value: By integrating institutional quality indicators, corruption, and macroeconomic stability into a unified econometric framework for Iran, this study provides novel scientific value. While much of the existing literature has largely overlooked institutional dimensions, this research offers fresh evidence that structural reforms aimed at enhancing transparency and governance efficiency can significantly improve Iran's capacity to attract foreign direct investment.

Abstract Purpose: Investors' feelings and emotions, as well as their attention, are vital to financial markets and can change their fate and affect market trends. One of the most important factors affecting investors' feelings and attention is sustainability reporting. The present study examines the impact of sustainability reporting on investors' feelings and attention, with emphasis on business strategy. Methodology: This research is practical in purpose and methodology; the correlation is of the causal type (after-event). In systematic elimination sampling, 151 companies were selected as the sample and investigated over the 6 years from 2018 to 2023. The method used to collect information is a library, and data are collected to measure variables from the codal website and corporate financial statements. In Excel, basic calculations were made, and then, to test the hypotheses, Stata was used. Findings: The research shows that sustainability reporting has a direct impact on investor sentiment. Also, sustainability reporting directly affects investor attention. However, business strategy does not affect the relationship between sustainability reporting and investor sentiment. Also, business strategy does not affect the relationship between sustainability reporting and investor attention. Originality/Value: By providing comprehensive information beyond financial factors in sustainability reporting, this type of reporting has gained great importance and become a key consideration for investors and stakeholders, potentially affecting their attention and emotions.