Financial and Banking Strategic Studies

Abstract Purpose: Given the critical role of banks in the economy and financial interactions of society, their sustainable and effective performance depends on internal cohesion and employee commitment. Organizational culture, as a set of shared values, beliefs, and norms, guides employee behavior and shapes decision-making processes and workplace interactions. In this context, identifying the current status of organizational culture and its key components in banks provides a foundation for enhancing performance, fostering motivation and commitment, and increasing alignment with strategic objectives. This study aims to examine the organizational culture of Sepah Bank and propose strategies for its effective management. The study aims to examine the key components of organizational culture and provide practical recommendations to enhance performance and align employees with the bank's strategic objectives.
Methodology: The research employed a quantitative survey method. The survey instrument used was the Ethics, Values, and Culture (EVC) questionnaire. The collected data were analyzed using Excel and SPSS software.
Findings: The results indicated that the organizational culture of the bank is at a moderate level across all seven components of the EVC model, highlighting the need for developmental and improvement initiatives. The lowest mean score was related to the Structure component, indicating it as a priority for development programs. Additionally, the ranking of component importance from the employees' perspective showed that Strategy, Employees, and Shared Values were of the highest priority, whereas Skills and Systems were of the lowest priority. Demographic analysis revealed that employees' perceptions of organizational culture were influenced by age, work experience, education, contract type, job position, and organizational unit. Younger, more educated, and headquarters staff provided lower evaluations, whereas more experienced employees and branch staff offered higher evaluations.
Originality/Value: This study provides a comprehensive assessment of Sepah Bank's organizational culture using the EVC model to identify strengths, gaps, and development priorities. It presents a practical framework for enhancing organizational culture and ensuring alignment with the bank's strategic objectives.

Abstract Purpose: The objective of this research is to present a comprehensive framework for identifying anomalies in Commitments of Traders (COT) report data and to investigate their role in detecting economic trends, market disruptions, and sudden changes.
Methodology: In this study, following the preprocessing of the COT data, statistical methods, including the standard score and the interquartile range, were combined with machine learning algorithms, including Isolation Forest and One-Class Support Vector Machine. Then, by creating ensemble anomalies, the points identified as anomalous by all methods were extracted. Subsequently, linear and non-linear dimensionality reduction methods PCA, Isomap, UMAP, and LLE, were applied to the COT dataset, and the One-Class Support Vector Machine, Local Outlier Factor, Isolation Forest, and K-Means algorithms were implemented for anomaly detection.
Findings: The findings demonstrated that the detected anomalies closely coincide with prominent macroeconomic disruptions, notably the 2008 Global Financial Crisis and the economic shock of the 2020 COVID-19 pandemic. Additionally, the consensus framework, grounded in the intersection of model outputs, effectively filtered alarms driven by algorithm-specific sensitivities, thereby yielding a focused subset of 1,638 observations characterized by the highest inter-algorithm consensus.
Originality/Value: This research, by integrating statistical methods, machine learning, and Manifold Learning, provides an accurate and reliable framework for analyzing complex financial market data and creates the foundation for developing interactive dashboards for real-time market monitoring.

Abstract Purpose: This study aims to examine the impact of cognitive biases on investment decisions, considering the mediating role of risk perception and the moderating role of robo-advisors among shareholders of companies listed on the Tehran Stock Exchange.
Methodology: This research is applied in terms of purpose and descriptive-survey in terms of methodology. The statistical population consisted of individual investors active in the Tehran Stock Exchange, from whom 335 respondents were selected using purposive sampling. Data were collected through a standardized questionnaire and analyzed using Structural Equation Modeling (SEM) with SPSS and SmartPLS software.
Findings: The results revealed that availability bias, anchoring bias, loss aversion bias, representativeness bias, and overconfidence bias have a positive and significant effect on investment decisions. Furthermore, risk perception plays a significant mediating role in the relationship between overconfidence bias and investment decision-making. In addition, robo-advisors significantly moderate the relationship between overconfidence bias and investment decisions.
Originality/Value: The novelty of this study lies in simultaneously examining the effects of cognitive biases on investment decisions while considering the mediating role of risk perception and the moderating role of robo-advisors. The findings contribute to the behavioral finance literature and provide insights into improving investment decision quality through the application of emerging financial technologies.

Abstract Purpose: Bond tokenization holds transformative potential for financial markets, promising enhanced liquidity, lower transaction costs, and democratized access. Yet, the existing literature remains fragmented and contradictory, failing to offer a comprehensive picture of its actual effects. This paper aimed to systematically integrate existing findings and provide a holistic analysis of the benefits, barriers, and structural impacts of tokenization on bond markets.
Methodology: Using a qualitative meta-synthesis approach based on Sandelowski and Barroso's seven-step model, 25 articles were selected through systematic database searching and screening, then qualitatively assessed using the CASP tool. Findings were synthesized via thematic synthesis in three stages. Reliability was confirmed through Cohen's Kappa, the Intraclass Correlation Coefficient, and an expert panel.
Findings: Three overarching themes emerged: 1) Practical efficiency gains, reduced settlement time and transaction costs, though enhanced liquidity remains unrealized; 2) The regulatory labyrinth, legal ambiguities and fragmented frameworks creating a vicious cycle of uncertainty; and 3) Structural and governance evolution, revealing a fundamental paradox whereby decentralized technology does not eliminate intermediaries but reinvents them through hybrid models.
Originality/Value: Previous studies have operated in silos, addressing efficiency, regulation, or technical architecture in isolation. This research, for the first time, integrates these three streams into a unified analytical framework. Critically, this synthesis uncovers the "Governance Paradox", where technological benefits, constrained by regulatory barriers, lead to the reinvention rather than elimination of intermediaries. This feedback loop, visible only through integrated analysis, constitutes the paper's principal theoretical innovation, advancing knowledge while providing a coherent roadmap for policymakers and market participants.

Abstract Purpose: This paper aimed to diagnose the role of Iran’s banking system focusing on Bank Sepah as a case in financing the knowledge-based economy. Although knowledge-based companies constitute more than 11% of the country’s active economic enterprises, they received only 4.8% of the banking network’s facilities in 2023, and Bank Sepah’s share (2.2%) lags well behind comparable banks, revealing a substantial financing gap. The study seeks to identify the structural, behavioral, and contextual challenges that hinder the banking system from playing an effective role in developing the knowledge-based economy and to propose corresponding policy solutions.
Methodology: The research is applied-developmental in purpose and qualitative-interpretive in method, employing a descriptive-analytical case-study strategy. Based on the principle of triangulation, data were gathered from three complementary sources documentary analysis, semi-structured interviews, and focus-group sessions and analyzed through thematic analysis within the framework of Mirzaei Ahranjani’s “Three-Branch Model” (structural, behavioral, and contextual factors). Purposive sampling was applied, and the trustworthiness of the findings was ensured through methodological triangulation, member checking, and independent coding.
Findings: The pathologies of the banking system in the knowledge-based domain are not single-cause but stem from an interconnected causal network of three sets of factors: Structural factors (post-merger strategic ambiguity, uniform credit-evaluation procedures, and limited acceptance of intellectual property as collateral); behavioral factors (institutionalized risk aversion, short-term return expectations, the tendency toward enterprise ownership, and information asymmetry); and contextual factors (a stringent legal framework, the gap between legal text and banking practice, macroeconomic instability, and international sanctions). The findings indicate that the fundamental challenge is “institutional-procedural” rather than “resource-based”.
Originality/Value: This study provides the first systematic pathology of a specific Iranian bank (Bank Sepah) in the knowledge-based field using the Three-Branch Model, shifting macro-level analysis to the micro-organizational level. By incorporating the variable of the 2019 merger of the armed forces’ banks absent from prior research it offers a coherent set of policy recommendations applied simultaneously across the structural, behavioral, and contextual layers.

Abstract Purpose: The purpose of this paper is to examine the interaction between the government and the central bank in stabilizing public debt using a non-cooperative differential game, with government expenditure and tax revenue modeled both independently and as interdependent fiscal variables.
Methodology: A non-cooperative open-loop differential game is developed based on an extended Tabellini framework. Two fiscal-policy structures are modeled and their equilibrium outcomes are evaluated through numerical simulation calibrated for the Iranian economy.
Findings: The results show that modeling government expenditure and tax revenue as interdependent variables reduces the equilibrium public debt from 0.173 to 0.160, the monetary base from 0.075 to 0.062, and the fiscal deficit from 0.080 to 0.067. The convergence rate is 0.307 in the interdependent structure, compared with 0.378 when the fiscal variables are modeled independently.
Originality/Value: The originality of this paper lies in separating government expenditure and tax revenue as independent control variables and comparing this structure with an interdependent fiscal-deficit formulation within a differential-game framework. The findings highlight the importance of coordinated fiscal-policy design for public debt stability.