Financial and Banking Strategic Studies

Abstract Purpose: This study aims to evaluate and rank the financial performance of banks listed on the Tehran Stock Exchange using a combined BWM–RAPS approach. Given the key role of banks in the capital market, this study is necessary to provide a scientific and reliable framework for decision-making by investors and analysts. This research seeks to provide the most appropriate method for ranking banks based on financial ratios.
Methodology: The present study employs a quantitative and descriptive–analytical approach and examines 5 criteria and 19 financial sub-criteria over the period from 2008 to 2021. The criteria were weighted using the BWM method based on the opinions of 10 expert specialists, and these weights were then applied to the TOPSIS, VIKOR, and RAPS techniques for ranking the banks. The data were calculated and analyzed based on the average financial ratios of the banks.
Findings: The results showed that profitability and efficiency were the most important criteria, with ROA, P/E, and the volume ratio being the most significant sub-criteria. In the final ranking, Parsian Bank ranked first in the TOPSIS and VIKOR methods, while Pasargad Bank ranked first in the RAPS method. Additionally, Shahr Bank exhibited the weakest financial performance across all three methods.
Originality/Value: By integrating BWM and RAPS and comparing them with TOPSIS and VIKOR, this study provides an innovative framework for evaluating bank financial performance. The use of 19 financial ratios and the combination of different MCDM methods distinguish this research from previous studies. Its results can serve as a basis for investor decision-making and for improving banking ranking models in the country.

Abstract Purpose: The research population consisted of managers, department heads, and experienced experts with at least eight years of relevant experience in the banking sector. Data were collected through semi-structured interviews and analyzed using thematic analysis. Through coding and theme development, the essential indicators for competitor assessment were extracted. Methodology: In today's highly competitive, technology-driven environment, the banking industry has undergone profound transformations, intensified by the emergence of new rivals, such as fintech companies. This study aims to identify and analyze competitive indicators to assess and neutralize rival banks' strategies, providing a foundation for improved strategic decision-making. Findings: The research findings showed that evaluating competitors in the banking industry can be explained in terms of two levels of needs: strategic and supporting. In this framework, indicators fall into two main categories: strategic performance indicators, which include criteria such as market share, revenue, profitability, facilities, cost of money, bank capital, and economic value added; and strategic management indicators, which include items such as target markets, strategies, digital banking, programs and actions, value proposition, business model, bank value chain, organizational structure, and strategic pillars. This categorization reflects the nature and level of impact of indicators that are key to competitor analysis. Originality/Value: This study provides a comprehensive framework for systematically comparing a bank's competitive position within the industry and across strategic groups. The framework can serve as a valuable tool for banking managers in competitor monitoring, strategy formulation, and the enhancement of competitive advantage.

Abstract Purpose: The purpose of this paper is to present an operational model for implementing blockchain technology in the Know Your Customer (KYC) and credit verification processes for corporate clients within Bank Sepah's "Negin" system. This research aims to identify the challenges of traditional systems and propose an innovative solution to enhance transparency, security, and efficiency in corporate banking services.
Methodology: This research employed a mixed-methods approach with a sequential exploratory design. In the first (qualitative) phase, Grounded Theory, using the systematic approach of Strauss and Corbin, was used. Data were collected through semi-structured interviews with 22 experts from the banking and technology industries and were analyzed using MAXQDA software. In the second (quantitative) phase, the conceptual model extracted from the qualitative phase was validated using the Delphi method through a questionnaire administered to 20 experts.
Findings: Data analysis identified a comprehensive model comprising 6 core categories, 4 main dimensions, and 238 indicators. Weaknesses in digital infrastructure, the high cost of the current structure, and institutional resistance were identified as the main causal conditions. Key strategies derived include implementing an integrated blockchain-based technical infrastructure, designing a sustainable financial model, and strengthening regulatory frameworks. The results of the Delphi method confirmed the validity and feasibility of the proposed model.
Originality/Value: By providing a localized, validated model for one of Iran's largest banks, this research fills a research gap and offers a practical roadmap for the digitalization of identity and credit verification processes in the country's corporate banking system. This argument can lead to cost reduction, increased security, and a move towards digital governance.

Abstract Purpose: This paper aimed to design and evaluate a native Dynamic Asset Allocation (DAA) model based on quantitative criteria, challenging the performance of static strategies in the volatile environment of Iranian asset markets. Given the inefficiency of static allocation approaches for managing risk and capitalizing on opportunities under changing economic conditions, the proposed model introduces a two-level decision-making framework that combines Trend-Following and Relative Momentum. Methodology: Monthly data for four main asset classes (the Tehran Stock Exchange stock index, Emami gold coin, the US dollar in the free market, and a fixed-income fund) from October 2014 to September 2025 were used. The dynamic allocation mechanism was designed based on the 6-Month Simple Moving Average (6-Month SMA) to filter the risk regime: if no risky asset had a 'buy' signal, 100% of funds were moved to the safe asset (fixed-income fund); otherwise, 100% of funds were distributed among the activated risky assets based on their prior period relative returns. The strategy's performance was evaluated using the Sharpe Ratio, Calmar Ratio, and Maximum Drawdown relative to single-asset Buy-and-Hold strategies. Findings: The results showed that the DAA strategy, despite not achieving the highest absolute return, registered the best risk-adjusted performance. This strategy demonstrated significant resilience against downside risk, recording the lowest maximum drawdown (-25%) and the highest Calmar Ratio (1.78) compared to the Total Index (drawdown -40% and Calmar Ratio 1.01) and Gold Coin (drawdown -32% and Calmar Ratio 1.61). Also, the Sharpe Ratio of the dynamic strategy (1.34) was significantly higher than the Tehran Stock Exchange Total Index (0.95). A statistical test (Memmel-modified Jobson-Korkie test) showed that the difference in the Sharpe Ratio between the dynamic strategy and the Total Stock Index was significant at the 99% confidence level, with a Z-statistic of 4.11 and a very small p-value (0.00004). Originality/Value: The originality lies in designing a native, two-level DAA model that uniquely combines simple Trend-Following and Relative Momentum rules tailored for highly volatile asset markets. Its value is demonstrated by achieving superior risk-adjusted performance and significantly reducing tail risk compared to static strategies. It provides a practical, evidence-based framework for local financial institutions to enhance investment efficiency and resilience.

Abstract Purpose: This study aims to design and validate a platform business model for a banking and financial group and addresses the critical gap regarding the lack of an integrated framework that enables synergy, coordination, and value co-creation across multiple financial subsidiaries.
Methodology: A mixed-method approach was employed. First, a Systematic Literature Review (SLR) was conducted to extract conceptual components of platform-based business models. These components were validated through expert judgment from 23 senior professionals in digital banking and fintech. Subsequently, structural relationships were analyzed using DEMATEL, hierarchical layers were identified through Interpretive Structural Modeling (ISM), and the relative importance of components was calculated using DANP.
Findings: Results show that platform governance and core operational components, such as value proposition, channels, and key activities, play the most critical role in the model. Governance-related elements, including data governance and the Regulator, act as overarching constraints that shape how the platform ecosystem operates.
Originality/Value: The proposed model offers a comprehensive framework for designing platform-based business architectures in financial groups. It highlights that synergy among financial subsidiaries is achievable only when the governance and data layers are effectively developed and when customer experience is designed end-to-end and personalized.

Abstract Purpose: Management ability is part of the human capital of companies and is classified as part of intangible assets. Higher management capability can lead to more efficient management of company activities, especially during critical operational periods when management decisions can have a significant impact on performance. It is expected that capable managers will influence companies' capital structures and the speed of their adjustments. The main objective of this study is to investigate the effect of top-level managers' experience on the capital structure of banks listed on the Iranian Stock Exchange.
Methodology: The statistical population of this study includes 18 banks in the period 2018-2023, and the systematic elimination method was used for sampling. The Generalized Method of Moments (GMM) regression was used to estimate the research model.
Findings: The study found that senior managers' experience positively affects the adjustment of banks' capital structures. Experienced managers achieve higher productivity by better managing the organization's resources, engaging in less profit management, and therefore achieve higher-quality accruals. As a result, it can be expected that senior managers' experience will affect the book value of total long-term debt, an indicator of capital structure. Companies with more capable managers are more likely to earn higher returns, and those returns can act as a shield against portfolio risk. Therefore, such companies are better positioned to use debt rather than increase capital.
Originality/Value: This paper separates managerial skills from capital structure and allows for the possibility that senior managers' skills (such as experience) directly affect the company's capital structure. The use of an index of years of experience for bank senior managers, and the application of agency theory and equilibrium theory to maximize the benefits of tax-shield debt in bank capital structure, are among the ways this research differs from others.