Business, Marketing, and Finance Open

Determinants of Bank Risk-Taking: The Role of Regulation, Competition, and Financial Disclosure

Jinan Haleem Sharhan iD , Ezatollah Abbasian ^* iD , Islam Fakher iD

This study aimed to examine the determinants of risk-taking behavior among Iraqi commercial banks by assessing the effects of regulation, competition, financial disclosure, bank-specific characteristics, macroeconomic conditions, and institutional quality. This study employed a quantitative causal-explanatory design using a dynamic panel-data framework. The study population included licensed Iraqi commercial banks operating between the first quarter of 2010 and the fourth quarter of 2024. After data screening, the final unbalanced panel included 38 commercial banks and 2,036 bank-quarter observations. Bank-level data were extracted from audited quarterly financial statements, while regulatory and sector-level indicators were obtained from Central Bank of Iraq reports. Macroeconomic variables, including real GDP growth, inflation, exchange-rate volatility, and oil-price volatility, were collected from international databases. Institutional quality indicators were also incorporated. Data were analyzed using descriptive statistics, correlation analysis, static panel regressions, and dynamic System-Generalized Method of Moments estimation. Semi-structured expert interviews were used only to contextualize the quantitative findings. The inferential results showed that regulatory intensity had a significant negative effect on bank risk-taking in both static and dynamic models. Financial disclosure quality, capital adequacy, liquidity, profitability, and institutional quality also significantly reduced risk-taking. In contrast, banking competition pressure, bank size, inflation, exchange-rate volatility, and oil-price volatility significantly increased risk exposure. The dynamic System-GMM results confirmed strong persistence in bank risk-taking over time. Institutional quality significantly strengthened the risk-reducing effect of regulation, while financial disclosure significantly weakened the risk-enhancing effect of competition. Ownership-based robustness analysis indicated that privately owned banks were more sensitive to competition-driven risk, whereas foreign-affiliated banks benefited more strongly from disclosure discipline. The study concludes that bank risk-taking in Iraq is shaped by the interaction of regulatory enforcement, competitive pressure, disclosure transparency, internal financial resilience, institutional quality, and macroeconomic instability. Strengthening prudential regulation alone is insufficient unless accompanied by improved disclosure standards, stronger governance mechanisms, and more credible institutional enforcement.

Modeling the Determinants of Public Budgeting Effectiveness in Oil-Dependent Economies: Evidence from Iraq Using Dynamic Panel Data

Jecup Raham Mohamed iD , Ezatollah Abbasian ^* iD , Islam Fakher iD

This study aimed to model the determinants of public budgeting effectiveness in Iraq as an oil-dependent economy, with particular emphasis on the effects of oil price volatility, corruption levels, institutional capacity, political stability, and capital expenditure allocation. This study employed a quantitative econometric design based on dynamic panel data analysis. The dataset consisted of longitudinal fiscal, institutional, political, and macroeconomic observations related to Iraq’s public budgeting system across multiple fiscal years and expenditure sectors. Public budgeting effectiveness was measured as a composite index reflecting GDP growth, budget deficit behavior, and budget execution rate. Oil price volatility was measured through the standard deviation of oil prices, corruption levels through a transformed corruption perception index, institutional capacity through an institutional quality index, political stability through a political stability index, and capital expenditure allocation as the percentage of total public expenditure assigned to capital spending. Data were analyzed using Pooled Ordinary Least Squares, Fixed Effects, Random Effects, and Generalized Method of Moments estimations. Model selection and validity were assessed through the Breusch–Pagan LM test, Hausman test, Arellano–Bond serial correlation tests, and Hansen and Sargan tests of instrument validity. The inferential results showed that oil price volatility had a negative and statistically significant effect on public budgeting effectiveness, indicating that greater oil market instability weakens fiscal planning and budget execution. Corruption levels also had a negative and significant effect, confirming that corruption reduces budget credibility and expenditure efficiency. Institutional capacity had a positive and highly significant effect and represented the strongest enabling determinant of budgeting effectiveness. Political stability and capital expenditure allocation also had positive and significant effects. The dynamic GMM results showed that the lagged value of public budgeting effectiveness was positive and significant, confirming the persistence and path-dependent nature of budgeting performance in Iraq. The findings indicate that public budgeting effectiveness in Iraq is shaped by both external oil-market instability and domestic institutional-governance conditions. Strengthening institutional capacity, reducing corruption, improving political stability, and prioritizing productive capital expenditure are essential for improving budget performance in oil-dependent economies.

Designing a Conceptual Model of the Impact of Signals Embedded in Fake News on the Cryptocurrency Market

Hamideh Tabatabaei iD , Seyed Mahmoud Reza Mortazavi ^* iD , Shadi Zabet iD

This study aimed to design a conceptual model of the impact of signals embedded in fake news on the cryptocurrency market. The research method was qualitative and was conducted using the Delphi technique in five rounds with the participation of five experts in the fields of media and business. The interview data were coded and analyzed using MAXQDA software, and seven main concepts were ultimately extracted: the legal status of cryptocurrency, cryptocurrency management strategies, factors affecting the cryptocurrency market, the type of impact of fake news on cryptocurrency, understanding the effects of signals in fake news, methods for detecting signals in fake news, and strategies for managing fake news, signals, and the cryptocurrency market. The findings showed that fake news, whether explicit or implicit, can lead to severe price fluctuations, reduced public trust, the growth of informal markets, and intensified instability. Accordingly, improving media literacy, formulating transparent regulations, using advanced technologies, and strengthening cooperation between public and private institutions are proposed as key strategies for managing these challenges. The results of this study can help policymakers and cryptocurrency market actors in Iran reduce the destructive effects of fake news through a comprehensive approach and benefit from the economic opportunities offered by this technology.

Examining the Effect of Sustainability and Corporate Social Responsibility Disclosure on Reporting Quality and Economic Sustainability in Companies Listed on the Tehran Stock Exchange

Hadi Ghasemi iD , Reza Ziari ^* iD

This study examines the effect of corporate social responsibility and corporate sustainability disclosure on financial reporting quality among companies listed on the Tehran Stock Exchange. The research sample consisted of 120 companies over the 2019–2024 period. Financial reporting quality was measured using the absolute value of discretionary accruals, and a fixed-effects panel data regression model was employed to test the hypotheses. The results indicated that corporate social responsibility and sustainability disclosure had a negative and statistically significant effect on discretionary accruals, meaning that a higher level of disclosure was associated with improved financial reporting quality. Sales growth, loss status, and firm age also had significant effects on financial reporting quality, whereas firm size, financial leverage, and return on assets were not statistically significant. Overall, the findings indicate that strengthening corporate social responsibility and sustainability disclosure can reduce earnings management and enhance financial reporting transparency.

Ranking the Factors Affecting Financial Flexibility Using Artificial Neural Networks and Machine Learning

Ebrahim Lotfinia iD , Vali Khodadadi ^* iD , Alireza Jorjor Zadeh iD , Saeed Nasiri iD

This study aimed to identify and rank the factors affecting the financial flexibility of companies listed on the Tehran Stock Exchange and Iran Fara Bourse using artificial neural network and XGBoost machine-learning models. This applied, quantitative, retrospective, and correlational study used longitudinal firm-level data from companies listed on the Tehran Stock Exchange and Iran Fara Bourse during 2020–2024. Following the application of the inclusion and exclusion criteria, 143 companies were selected through a census approach. Financial flexibility was assessed based on cash holdings and debt capacity. The predictors included default risk, political connections, market power, managerial ability, environmental uncertainty, managerial overconfidence, chief executive officer power, management turnover, and board financial expertise. After data screening and preprocessing, an artificial neural network and the XGBoost algorithm were trained and evaluated. Model performance was assessed using the coefficient of determination and root mean squared error, while variable importance and the direction of predictor effects were interpreted through normalized importance scores and SHAP values. Both algorithms successfully ranked the determinants of financial flexibility, although the artificial neural network demonstrated superior predictive performance. The neural network achieved an (R^2) of 0.499 and an RMSE of 0.179, whereas XGBoost produced an (R^2) of 0.413 and an RMSE of 0.194. Default risk was the most influential predictor in both models, and lower default risk was associated with greater predicted financial flexibility. In the neural network, managerial ability, market power, political connections, and environmental uncertainty followed default risk in importance. In XGBoost, political connections, environmental uncertainty, managerial ability, and market power received the next highest rankings. Managerial overconfidence had moderate importance, whereas chief executive officer power, management turnover, and board financial expertise made comparatively limited predictive contributions. Training and validation errors converged satisfactorily, indicating acceptable model stability and no severe overfitting. Financial flexibility is primarily determined by credit quality, managerial competence, competitive position, institutional access, and environmental conditions, and artificial neural networks provide a more accurate framework than XGBoost for predicting and ranking these nonlinear determinants.

Robust Portfolio Optimization Considering Investor Preferences Using Maenhout Preferences and Reinforcement Learning

Mohammad Mehdi Faraghzadeh iD , Mehrzad Minouei ^* iD , Azadeh Mehrani iD

This study develops a robust and adaptive portfolio optimization framework that integrates Maenhout’s ambiguity-averse preference structure with Proximal Policy Optimization (PPO) for dynamic asset allocation in the Tehran Stock Exchange. The model is designed to address two core challenges in emerging markets: parameter estimation error and model misspecification. To do so, the reward function combines expected return with explicit penalties for tail risk and downside risk, including Conditional Value-at-Risk (CVaR) and the Sortino ratio, so that the learned policy is not only return-seeking but also resilient under adverse market conditions. Using daily data from actively traded firms over the 2011–2025 period, the empirical evaluation shows that the proposed framework consistently outperforms classical benchmarks in risk-adjusted performance, downside protection, and stability across changing regimes. In particular, the Maenhout–PPO strategy achieves higher Sharpe and Sortino ratios, lower maximum drawdown, and more controlled tail losses than competing allocation rules. The results indicate that classical mean–variance logic is not sufficiently robust for a market characterized by nonstationarity, inflation pressure, liquidity concentration, and abrupt policy shocks. By contrast, the proposed framework offers a more realistic decision architecture for portfolio management under uncertainty, demonstrating that combining robust control principles with reinforcement learning can materially improve investment performance in emerging-market settings.

Factors Affecting Policyholders’ Risk Aversion in the Adoption of Additional Endowment Life Insurance Using a Machine-Learning Algorithm

Behzad Bahmanyar iD , Hamid Khajeh Mahmoud Abadi ^* iD , Ali Reza Rayati Shavazi iD , Gholam Reza Askarzadeh iD

This study employed machine-learning analysis on a large-scale real-world dataset comprising first and second endowment life insurance policies issued by Sarmad Insurance Company, which is recognized as a leading provider of second life insurance policies in Iran, as well as data from Fanavaran Company, an insurance technology platform operating within the insurance industry. The study population consisted of 95,962 individuals who collectively purchased their first life insurance policies through a well-known insurance company in Iran, with the policyholder and the insured being the same person. Following customer relationship management activities and follow-up contacts, 18,604 of these individuals purchased a second life insurance policy. The data were collected between 2021 and 2025 and, after data cleaning and normalization, were used as inputs for the predictive model. To predict which customers were inclined to purchase additional insurance coverage, a predictive model was developed using one of the most effective machine-learning techniques, namely Extreme Gradient Boosting (XGBoost). The predictive accuracy of the selected model was subsequently evaluated using k-fold cross-validation. Given that previous studies have consistently confirmed the risk-averse tendencies of individuals who purchase life insurance, those who purchased additional life insurance under highly unstable economic conditions—despite having the option to allocate their funds to alternative assets—may be regarded as a particularly salient group of risk-averse individuals. The model incorporated 13 demographic, behavioral, and socioeconomic factors as independent variables, while the purchase of additional endowment life insurance was considered the dependent variable. Consequently, the distinctive characteristics of customers predicted to purchase a second endowment life insurance policy were identified. The findings demonstrated significant associations between the independent variables and the probability of purchasing additional insurance. Notably, 6 of the 13 independent variables exerted a substantial influence on the purchase of additional insurance. These variables included age, gender, city and province of residence, marital status, possession of other insurance policies, and possession of automobile insurance, including third-party liability and comprehensive motor insurance. By contrast, variables such as occupation, income, and educational attainment were identified as having limited predictive influence because of the high degree of similarity among the data obtained from employees of the large organizations comprising a substantial proportion of the insurance company’s customer base.

The Causal Relationship between Managers’ and Investors’ Cognitive Biases and the Performance of the Iranian Stock Market: A Mixed-Methods Approach

Jamileh Shakeri iD , Mehdi Heydari ^* iD , Rasoul Baradaran Hasanzadeh iD

This study aimed to develop and test a causal model explaining the relationships among managers’ and investors’ cognitive biases, decision-making quality, artificial intelligence-based bias-reduction mechanisms, and the performance of the Iranian stock market. This developmental study used a qualitative-dominant sequential exploratory mixed-methods design. In the qualitative phase, 12 university professors, behavioral-finance researchers, market analysts, managers, and experienced investors were selected purposively and interviewed through in-depth semi-structured interviews until thematic saturation. The interview data were analyzed through thematic analysis using MAXQDA, and the extracted dimensions and indicators were refined through a Delphi process. In the quantitative phase, 200 managers, investors, financial analysts, consultants, and decision-makers active in the Iranian capital market were selected through random sampling. Data were collected using a researcher-developed questionnaire derived from the qualitative findings. Reliability and validity were examined using Cronbach’s alpha, composite reliability, and average variance extracted. Structural relationships were tested using structural equation modeling and bootstrapping. Fundamental cognitive biases significantly reduced decision-making quality (β = −0.42, t = 5.25, p < 0.001). Investment and financial biases (β = −0.40, t = 4.44, p < 0.001), informational and analytical biases (β = −0.31, t = 3.88, p < 0.001), narrative biases (β = −0.28, t = 3.11, p = 0.002), organizational and cultural biases (β = −0.25, t = 3.57, p < 0.001), and bias-related consequences (β = −0.29, t = 3.63, p < 0.001) also had significant negative effects. Artificial intelligence applications had the strongest positive effect on decision-making quality (β = 0.46, t = 5.75, p < 0.001), followed by advanced artificial-intelligence mechanisms (β = 0.38, t = 5.43, p < 0.001), bias-reduction strategies (β = 0.36, t = 5.14, p < 0.001), institutional strategies (β = 0.32, t = 4.57, p < 0.001), and organizational learning (β = 0.27, t = 3.38, p = 0.001). Decision-making quality significantly improved stock-market performance (β = 0.64, t = 9.21, p < 0.001). Cognitive biases impair managerial and investment decision-making and indirectly weaken stock-market performance, whereas artificial intelligence, systematic debiasing, institutional controls, and organizational learning improve decision quality and market outcomes.

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The Impact of Blockchain on Corporate Financial Reporting Based on Iranian Reporting Standards and IFRS

The Threshold Effect of Oil Rent on Non-Oil Economic Growth in Light of Financial Development: A Case Study of Iraq

Empirical and Comparative Analysis of the Efficiency of Nonlinear Option Pricing Models (Quantum-Based and Heston) and Conventional Linear Models (Black–Scholes and Binomial Tree) in the Tehran Stock Exchange

The Role of Behavioral Empowerment and Smart Technologies in Enhancing Investor Decision-Making and Portfolio Optimization in the Iranian Capital Market

Designing a Model to Explain the Role of Customer Knowledge Management Strategy in Achieving Competitive Advantage in Iraq's Electronic Banking Sector

The Role of Blockchain Technology in the Development of Accounting in Iran

Providing and Validating a Systemic Risk Model in the Tehran and Iraq Stock Exchanges

Examining Audit Strategies Affecting the Quality of Financial Reporting

The Impact of Behavioral, Demographic, Financial Self-Efficacy, and Investors’ Longevity Preferences on Their Decision-Making

Analyzing the Moderating Role of Corporate Reputation in the Relationship between Managerial Ownership and Voluntary Disclosure in Leveraged and Capital-Based Firms

The Impact of Oil Shocks on Business Cycles and Structural Inflation in Iran: An SVAR Approach

Analysis of the Effect of Energy Price Volatility on the Export Competitiveness of Iranian Industries

Identifying the Factors Influencing the Development of Medical Tourism from a Marketing Mix Perspective in Gilan Province

Designing a Model and Ranking the Digital Transformation Components Affecting the Quality of Accounting Information in Iran’s Capital Market: A Fuzzy Delphi Approach

The Role of Earnings Quality and Future Returns: A Rational Decision-Making Simulation Approach

Identification and Analysis of Barriers to the Realization of Synergy Between the Supreme Audit Court of Iran and Internal Auditing in the National Iranian South Oil Company

Presenting a Predictive Model of Stock and Over-the-Counter Market Price Indices Based on Macroeconomic Indicators Using Artificial Neural Networks

Econometric Analysis of the Effects of Dividend Policy Changes on the Implied Cost of Equity Capital under Different Levels of Return on Assets

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