Financial Research Journal
Year:2025 | Volume:27 | Issue:2|Papers Count:10

ObjectiveIn competitive markets, companies focus on establishing long-term relationships with their customers and strengthening their loyalty. Due to the high costs associated with acquiring new customers, businesses tend to focus on retaining existing ones. Predicting which customers are likely to churn in the future plays a crucial role in shaping effective customer retention strategies. To predict customer churn and identify its drivers, companies use customer information and historical data recorded from them. This paper investigates customer churn prediction in the banking industry using real customer data from one of the largest banks in Iran. MethodsTo analyze and predict customer behavior, two equal and consecutive periods were considered. Customer behavior in the first period was used to predict a target variable in the second period. A significant drop in the average effective balance during the second period, compared to the first, was defined as the indicator of customer churn. By processing a large volume of banking transactions in the first period and aggregating them at different levels, various behavioral features for customers. We selected. One fixed validation set and three training sets of different sizes were selected. To address the issue of dataset imbalance, class weights were determined based on the ratio of class sizes, ensuring that the minority class received greater weight during the training process. To predict customer churn, widely used machine learning algorithms—including Naive Bayes, k-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Decision Tree—were applied, along with ensemble learning methods such as Random Forest, Adaptive Boosting, and Gradient Boosting. Subsequently, deep learning methods were applied, and a model incorporating modern modules such as residual connections and layer normalization—similar to state-of-the-art architectures—was proposed. Exhaustive experiments were conducted to evaluate the performance of the aforementioned methods. ResultsThe results showed that ensemble learning algorithms and the proposed deep learning models outperformed the baseline models. Additionally, increasing the size of the training set contributed to improved model performance. Among the traditional machine learning classification algorithms, the decision tree trained on two training sets obtained the highest AUC ROC on the validation set with 0.8531 and 0.8597. The gradient boosting model obtained the overall highest AUC ROC on the validation set with 0.8984 and 0.9010. Deep learning-based single models achieved AUC-ROC values of 0.8825, 0.8909, and 0.8958, outperforming all traditional methods and two ensemble learning approaches while performing competitively with the gradient boosting algorithm. ConclusionExtracting behavioral features from customers' banking transactions and applying ensemble methods, along with the proposed deep learning-based models, proves effective in predicting banking customer churn, particularly in cases of a significant decrease in the average effective balance.

ObjectiveIn late 2019, the world faced a profound challenge with the emergence of the COVID-19 crisis, which had a lasting impact on economies across both developed and developing nations. The consequential impact reverberated through international financial markets, forcing their constriction and eventual closure as nations enforced sweeping nationwide quarantines. Simultaneously, healthcare expenditure soared, juxtaposed against a downturn in economic growth. Amid this upheaval, the domain of digital currencies, notably Bitcoin, experienced reverberations from the COVID-19 outbreak. Bitcoin's introduction signaled a new era of direct electronic payments and streamlined cross-border wealth transference, pivotal components underpinning the scaffolding of global trade. While COVID-19 doesn't solely account for the surge in Bitcoin prices, its presence likely played a substantial role, exacerbated by escalated uncertainties and risks amidst the global pandemic. The focal point of this study lies in examining the interplay between COVID-19 dynamics and the US dollar index on Bitcoin prices, utilizing Continuous Wavelet Transform (CWT) data spanning from April 8, 2020, to April 8, 2023. This study's novelty lies in its utilization of the Continuous Wavelet Transform, offering a distinctive edge within the realm of research on this subject. MethodsThe COVID-19 pandemic emerged as one of humanity's most daunting challenges in recent decades, significantly impacting digital currencies, epitomized by Bitcoin, as an indispensable economic sector. Primarily, this study aims to examine the complex effects of the COVID-19 pandemic and the US dollar index on Bitcoin prices over 977 days from April 2020 to April 2023, utilizing the Continuous Wavelet Transform (CWT). The research framework integrates two pivotal variables: the volume of confirmed COVID-19 cases per million and the tally of confirmed COVID-19 deaths per million. ResultsThe findings unravel a compelling correlation between the frequency of COVID-19 cases and associated fatalities with Bitcoin price fluctuations during the study's duration. Intriguingly, the impact of case occurrences seemingly eclipses that of mortalities, possibly rooted in escalated uncertainties and risks during the pandemic, igniting an amplified demand surge for Bitcoin as investors sought refuge to safeguard their wealth and assets. Conversely, the US dollar index emerges as a noteworthy antagonist, wielding a pronounced negative influence on Bitcoin prices throughout the study period, potentially attributed to the plausible substitution of two commodities functioning as currency. ConclusionThe study's findings reveal a clear positive impact of COVID-19 cases and deaths per million on Bitcoin prices during the observed period. This increase is largely driven by heightened demand, as investors, faced with risk and uncertainty, sought out Bitcoin as a safe-haven asset. On the flip side, the U.S. Dollar Index, acting as a competing asset, showed a significant negative effect on Bitcoin’s price during the same time. This is likely due to a substitution effect: as the U.S. dollar strengthens, Bitcoin prices tend to decline.

ObjectiveFinancial inclusion is a key driver of economic growth. This article examines the impact of financial inclusion on economic growth in middle-income countries. The study measures financial inclusion based on three dimensions: banking penetration, availability of banking services, and usage. Individuals' access to financial instruments seems necessary for financial institutions to expand their market share. However, beyond institutional interests, financial inclusion and universal access to financial markets hold significant importance for policymakers in terms of promoting economic development. Therefore, the purpose of this article is to first design a comprehensive index of financial inclusion and then evaluate its effects on economic growth in developing countries.MethodsThe research employs a quantitative approach and designs a comprehensive financial inclusion index using a generalized method of moments (GMM) and panel data covering the period from 2002 to 2022 across 49 developed countries. ResultsPanel data estimates indicate that financial inclusion has a strong, positive, and statistically significant impact on the economic growth of these countries. The effect of the Financial Inclusion Index (IFI) on economic growth is stable regardless of the estimation method (fixed effects, random effects, and generalized method of moments), showing consistent effectiveness. The multi-country estimation suggests that the impact of IFI on economic growth is higher in countries with lower income compared to higher-income countries, indicating that IFI can be a suitable stimulus for the economic growth of poorer countries. This aligns with the diminishing returns of capital, i.e., establishing banking infrastructure and deepening financial inclusion have more pronounced effects in the early stages and gradually diminish over time. The findings also show that macroeconomic factors (inflation rate, economic openness, and capital formation) have a positive impact, while the unemployment rate hurts economic growth. Population structure (population growth rate) and health system (life expectancy) are also influential factors in economic growth. Therefore, leveraging modern banking tools—such as Internet banking and mobile banking—particularly in underserved areas, along with offering diverse services through bank branches, expanding retail banking, and utilizing user-friendly, accessible applications, plays a pivotal role in advancing and deepening financial inclusion alongside fostering economic growth. It should be noted that the average inflation rate is about 6%, and these countries rarely experience rapid and double-digit inflation. ConclusionThe results of the multi-country estimation indicate that the financial inclusion index has a significant impact on economic growth. Except for the economic openness variable, the results of this method are consistent and compatible with the generalized method of moments. The multi-country estimates suggest that the effectiveness of the financial inclusion index is greater in countries with lower income. Therefore, deepening and developing financial inclusion is crucial for countries with lower income and can contribute to economic prosperity.

Objective Empirical evidence on the impact of information asymmetry on the cost of equity capital reveals ongoing inconsistencies in the literature. While some studies report a positive relationship, others find it to be negative or statistically insignificant. A significant factor contributing to this divergence is the role of control variables. Although there exists a wide range of potential control variables, the inclusion of certain variables may distort the results, whereas others can enhance the accuracy of the research findings. This meta-analysis aims to investigate which control variables, based on those repeatedly employed in prior studies, significantly influence the regression coefficient in comparison to their exclusion. Methods The empirical examination of the relationship between information asymmetry and the cost of equity capital revealed notable divergences. To investigate this issue, a meta-analysis approach was employed, highlighting the varying effects of control variables on this relationship. This meta-analysis synthesizes the findings from 35 studies published between 1986 and 2022, encompassing a total of 198 tests, to illuminate the intricate relationship between information asymmetry and the cost of equity capital. The analysis was carried out in multiple stages: information asymmetry was designated as the independent variable, and the cost of equity capital was identified as the dependent variable. Control variables included the book-to-market ratio, market beta, company size, type of ownership, yield fluctuations, equity, leverage, and growth. Comprehensive searches were conducted in prominent databases such as Science Direct, Emerald, Google Scholar, SSRN, and ResearchGate, resulting in the extraction of 188 articles based on pertinent keywords. The articles were screened for alignment with the research hypotheses, focusing on studies that provided effect size information and employed correlation methods. Consequently, 35 studies published between 1986 and 2022, incorporating 198 effect sizes, were selected for meta-analysis. Relevant general information and details about effect sizes were meticulously extracted from the selected articles. For each sample, the effect size, denoted as r, was calculated. Effect sizes were aggregated using weighted means, accounting for sampling error. Finally, the homogeneity of effect sizes was assessed.   Results The findings indicate considerable variation in effect sizes across the studies analyzed. Consequently, a random effects model was utilized to calculate the cumulative effect size. Control variables play a crucial role in shaping the relationship between information asymmetry and the cost of equity capital. Specifically, the inclusion of control variables such as the book-to-market ratio, market beta, firm size, and ownership type tends to diminish the strength of this relationship. In contrast, factors such as stock return volatility, leverage, and firm growth do not significantly alter this relationship. A robustness analysis further corroborates these preliminary findings.   Conclusion Empirical researchers often face challenges in selecting appropriate control variables when examining the relationship between information asymmetry and the cost of equity capital. This raises a critical question: which control variables should be included to enhance the validity and reliability of the empirical results? This study identifies four key control variables that significantly impact the observed relationship: the book-to-market ratio, market beta, firm size, and ownership type. Incorporating these variables into the regression model substantially alters the outcomes of the analysis.

Objective Most companies and organizations use e-commerce to gain productivity and efficiency in their services and products in different areas such as credit cards, telecommunications, health insurance, car insurance, etc. Due to the growing volume of credit card transactions and the various methods of fraud and cheating on these cards, the demand for detecting fraud in this area is also increasing. Considering the various solutions and algorithms presented to reduce the cost of fraud detection in credit card transactions in the literature, the purpose of this research is to present a combined and optimal method to reduce the cost of fraud detection in credit card transactions for financial systems, using the fusion of heterogeneous classification and clustering algorithms at the decision-making level by using two fusion methods: Probabilistic fusion and Dempster-Shafer Evidence theory. Methods This study utilizes a transaction dataset from a Brazilian bank, covering two months from July 14 to September 12, 2004. Fraud detection performance is evaluated using a cost function derived from both a supervised learning approach, namely, a neural network, and an unsupervised method, the K-Means clustering algorithm. Drawing on established fraud detection metrics in the literature, the study adopts the cost function introduced by Gadi as the benchmark. Recognizing the high cost associated with using a single algorithm, the study implements two information fusion techniques—Dempster-Shafer evidence theory and probabilistic fusion—to reduce detection costs. Both fusion methods operate at the decision level, integrating heterogeneous outputs from the supervised and unsupervised models.   Results Depending on the algorithms implemented, using only one algorithm to obtain an acceptable cost function can be very costly. While using a fusion approach can have a significant impact on cost reduction. The findings indicate that probabilistic fusion significantly outperforms the Dempster-Shafer evidence theory in minimizing the cost function. Specifically, probabilistic fusion achieves a 21.4% cost reduction compared to the artificial neural network and a 35.8% reduction relative to the K-Means algorithm. The results of this study were finally compared with a paper in which this dataset was first used with the artificial immune system (AIS) algorithm and showed a significant cost reduction.   Conclusion In this study, two classification and clustering algorithms were utilized, and their fusion at the decision level was implemented to demonstrate that combined methods reduce the cost function more effectively than the use of individual algorithms. Furthermore, it was shown that probabilistic fusion yields a lower cost in detecting fraud within financial systems compared to the Dempster-Shafer Evidence theory. This finding is considered significant for banks and financial institutions aiming to develop effective fraud detection systems.

Objective The purpose of this research is to design a comprehensive and holistic model in the field of factors affecting the improvement of the quality of financial reporting and internal control in companies listed on the Tehran Stock Exchange The absence of a comprehensive model that encompasses the factors affecting financial reporting quality, with clear classification and specific indicators, has motivated this research. By considering integrated reporting and internal controls, along with the dimensions identified in previous studies, this research aims to assist managers and experts in adopting a more holistic view of financial reporting and integrated control. This approach seeks to safeguard stakeholder interests by summarizing, combining, and classifying relevant categories and concepts.   Methods This research is quantitative in nature, with an applied purpose. It is also a survey in terms of data collection methodology. The statistical population of the study included faculty members and professors of financial management and accounting at Tehran universities, experts from the Stock Exchange and financial and auditing institutions located in Tehran province, and financial and accounting managers of companies listed on the Tehran Stock Exchange. Since the most basic application of factor analysis is to reduce the number of variables to a limited number of factors that usually indirectly affect the main variable, this method was used in this study to analyze the data and reduce the number of variables.   Results From the selected sample, 168 people responded to the questionnaires, which were used in the research data analysis. According to the studies conducted concerning the research topic and also according to the results obtained from other researches, studies, articles and research projects and the results of the questionnaire, 41 variables were identified, and after further investigation and consultation with the elites, 11 variables are part of Other variables were considered; Therefore, they were included in its inclusion, thus 30 variables were identified as effective factors on the quality of integrated financial reporting and internal control of listed companies. After applying factor analysis, the Kaiser criterion was used to determine the number of factors. Based on this criterion, five factors with eigenvalues greater than 1 were identified as extractable. These factors collectively explained 91.396% of the total variance of the variables.   Conclusion In the presented model, the factors were ranked as follows based on their total factor loadings: management factors with a total factor load of 6.039, legal and regulatory factors with a total factor load of 3.127, organizational and strategic factors with a total factor load of 3.038, cultural and social factors with a total factor load of 2.961, and market structure-related factors with a total factor load of 2.415. These were determined as the most important factors, respectively.

Objective Sustainability has gained significant attention across various domains in recent decades, including the media, institutions, and national and international policymaking organizations. Concurrently, corporate social responsibility (CSR) literature has experienced rapid expansion, particularly about family businesses—entities distinguished by unique characteristics that differentiate them from other private organizations. This growth has created diverse and compelling research avenues at the intersection of sustainability and CSR. This study employs a bibliometric analysis to examine previous research on CSR in family businesses and its integration with sustainability, aiming to consolidate fragmented studies across industries while addressing gaps in the implementation of dispersed CSR practices among family enterprises.   Methods The research adopts a mixed-methods approach, combining quantitative and qualitative techniques through Bibliometric analysis, utilizing the ADO-TCM framework (Antecedents, Decisions, Outcomes – Theory, Context, Methods). For the scientometric analysis, we extracted data from Scopus (1990–2023), the most comprehensive and reliable database for scientometric research. From an initial pool of 592 identified articles, 120 were selected for preliminary analysis using VOSviewer software. Following Aiden et al.’s (2017) model, we implemented a four-stage screening process: (a) identification and extraction of relevant articles, (b) title and abstract screening, (c) full-text review of introductions and results, and (d) final evaluation based on research objectives, yielding 36 core articles.   Results Results indicate a substantial growth in CSR research focusing on family businesses since 2010. While early studies originated primarily in Western Europe and North America, the current research epicenter has shifted toward Asia, particularly to emerging and advanced economies like China. Quantitative methods dominate the field (accounting for 78% of studies), with regression analysis being particularly prevalent.   Conclusion Our researcher-developed model, based on the secondary screening of the 36 articles, reveals that antecedents primarily concern CSR, sustainable development, SDGs, ethics, and socio-economic-cultural factors. Strategic decisions predominantly involve long-term corporate strategies and board-level decisions focusing on earnings management, ownership structure, environmental disclosure, downsizing, workforce reduction, gender diversity, and investment. These interventions aim to enhance sustainability disclosure transparency, promote responsible approaches, and align investments with sustainability, which represents a key recent trend. The study highlights significant contributions from family businesses across Asian (e.g., China, South Korea, Russia, Turkey), European (e.g., Lithuania, Bulgaria, Czech Republic, Poland), and American (particularly the United States) contexts. The research integrates multiple theoretical perspectives, including Socioemotional Wealth Theory, Stakeholder Theory, Institutional Theory, Resource-Based View, Neo-Institutional Theory, Stewardship Theory, Agency Theory, Goal Systems Theory, Theory of Planned Behavior, and Signaling Theory.

Objective A review of Iran’s economic conditions, particularly the financial system in recent decades, reveals its limited participation in international banking. The lack of access to financial markets for investment and trade, limited services offered by domestic banks, their inability to compete in global financial markets, and economic sanctions have created significant challenges in engaging with the international economy. The establishment of offshore banks in Iran’s free trade-industrial zones could serve as an initial step and a connecting link to align with the global economy and enter international financial markets effectively. These banks, by offering a wider range of services, could facilitate capital flow, encourage foreign investment, and enhance the competitiveness of Iranian financial institutions significantly. In addition, such banks could contribute to the development of international trade and help alleviate the existing economic pressures faced by local businesses and individuals. Designing and creating an operational model for utilizing any tool in the field of economics and management is essential for achieving sustainable growth. This study aims to provide an analytical and explanatory framework for establishing such a bank in these free zones.       Methods This study employed a mixed-methods exploratory sequential design, commencing with qualitative data analysis. In the qualitative phase, grounded theory methodology was utilized, incorporating open, axial, and selective coding procedures. Participants were purposefully selected and recruited via snowball sampling from a population comprising finance experts affiliated with leading universities, bank managers operating in free trade-industrial zones, and commercial entrepreneurs and company owners active within these zones. A total of eleven semi-structured interviews were conducted until theoretical saturation was reached. In the quantitative phase, a descriptive-survey method was applied to analyze and explain the proposed framework. In this stage, 90 participants were randomly selected. Data were collected through a 30-item questionnaire derived from the grounded theory approach, and data analysis was performed using SMART PLS software.   Results In the qualitative phase of the research, after three stages of open, axial, and selective coding, a model comprising six main categories (central phenomenon, causal conditions, intervening conditions, contextual conditions, strategies, and consequences of establishing the offshore bank), 30 subcategories, and 153 concepts were presented. In the quantitative phase, the validity of the research model was assessed through structural equation modeling indicators, which demonstrated acceptable fit for all the obtained indices.   Conclusion According to the research findings, an appropriate explanatory framework for the establishment of offshore banks in the free trade-industrial zones of the Islamic Republic of Iran was presented. One of the key outcomes of the proposed framework is the integration of the country's financial institutions with international financial markets, which can serve as a catalyst for economic growth and development through the establishment and operation of offshore banking institutions.

Objective With the rapid growth of the economy and the expanding role of the private sector, financial markets have gained increased importance. Among the factors influencing the efficiency of financial markets, how information is disseminated to various stakeholders plays a crucial role. The method of information transfer stands as a key component in shaping the efficiency of capital markets. Management Commentary (MC), serving as a supplementary report to companies' financial statements, holds a pivotal role in enhancing capital market transparency through the disclosure of both financial and non-financial information. The objective of this research is to formulate an MC framework and subject it to experimental testing utilizing quantitative data. It is to introduce, for the first time, an MC framework specifically tailored to the context of Iran.   Methods This study adopts a mixed research methodology. Initially, qualitative content analysis was utilized, involving the comprehensive review of documents and studies pertaining to the IFRS Foundation in the domain of Management Commentary (MC), along with other relevant guidelines and reporting frameworks. This systematic examination facilitated the construction of the initial MC reporting framework and the formulation of pertinent hypotheses. Subsequently, to adapt and present the MC reporting framework in the Iranian context, survey data, obtained through a researcher-designed questionnaire, was collected. Each sub-category of the reporting framework was assessed and ranked using the one-sample Wilcoxon signed-rank test, with the final ranking determined based on Friedman's test.   Results Based on the findings of the qualitative section, the initial framework for MC comprises five key dimensions: Objectives, Qualitative Characteristics, Content Elements, Forward-Looking Information, and Information Related to the Long-Term Success of the Company. According to the findings of the quantitative section, none of these dimensions and their subcategories were rejected. Therefore, the MC reporting framework in Iran was structured across five dimensions as follows: 1. Objectives: comprising five distinct objectives, 2. Qualitative characteristics: encompassing eleven specific characteristics, 3. Content elements: consisting of six defined content elements, 4. Forward-looking information: comprising seven principles & 5. Information related to the long-term success of the company: categorized into six distinct areas. The relative importance of each dimension within the reporting framework is ranked as follows: 1. Objectives, 2. Qualitative characteristics, 3. forward-looking information, 4. Content elements, and 5. Information related to the long-term success of the company. Furthermore, the subcategories within each dimension of the reporting framework were also assessed and ranked according to their relative importance.   Conclusion The suggested reporting framework has the potential to serve as a valuable reference for institutions and organizations responsible for formulating capital market regulations. This includes entities like Audit Organization and Securities & Exchange Organization. By utilizing this framework, these regulatory bodies can develop native standards and guidelines for the presentation of MC. Furthermore, companies can benefit from this framework when presenting their MC. This ensures a more tailored and effective approach to MC reporting within the context of the Iranian capital market.

Objective The selection of board members used to take place before any in-depth review of the board's foundational existence, without any specific regulations and solely through informal means such as kinship, long-standing friendships, or collaboration and shared interests. This approach often led to an inappropriate board composition, a lack of sufficient expertise, and ultimately a decline in organizational efficiency. Therefore, the process of selecting board members has various consequences for organizations. Given the existing information gap regarding the selection of board members in Iran, this study aims to develop a model for board member selection, identifying the criteria that matter most to shareholders in this process.   Methods The present research employs the thematic analysis method. The statistical population includes CEOs and board members of Iranian investment companies and holdings. The statistical sample was selected using the snowball sampling method. Research data were collected through semi-structured interviews with 15 experts, reaching theoretical saturation.   Results When selecting board members, various criteria—such as individual and group characteristics—are taken into account. Individual characteristics refer to qualities specific to each board member, including inherent traits, communication skills, managerial and leadership abilities, professional ethics, expertise, and social competencies. In addition, company-specific factors also influence the selection process. Clearly defining the expectations and mission of board members within the organization is essential to ensure alignment with these requirements.   Conclusion Four main themes were identified for board member selection: (1) characteristics of board members, (2) requirements for their selection, (3) factors influencing the selection process, and (4) desirable outcomes of selecting board members. Board member characteristics include individual and group characteristics. Personal characteristics refer to traits specific to an individual serving as a board member. These can be categorized into seven key themes: intrinsic qualities, communication skills, managerial abilities, leadership capabilities, professional ethics, specialized expertise, and social competencies. Intrinsic characteristics of board members reflect their unique personal backgrounds and personality traits. Communication characteristics pertain to creating and maintaining good collaboration among themselves, shareholders, management, and other individuals. Managerial characteristics relate to the qualities and abilities of a board member that demonstrate their effectiveness. Leadership characteristics include traits related to leadership style and the strategic outlook of board members. Professional ethics characteristics involve observing fairness and ethics during the decision-making process. Specialized characteristics include skills and knowledge that a board member should have in line with the company's activities, such as experience, academic qualifications, industry-related expertise, etc. Social characteristics refer to an individual’s reputation, social connections, and interactions with others, including both internal and external organizational stakeholders. Group characteristics of board members pertain to a balanced composition in which members collectively possess diverse expertise relevant to the company. Selecting desirable board members leads to an improvement in the company's working environment, efficiency, and performance.