Accounting, Finance and Computational Intelligence

This study aims to investigate the structural relationship between board characteristics, risky behaviors, and bank performance using the profit frontier approach in banks listed on the Tehran Stock Exchange. The research employed a quantitative, descriptive-correlational design. The statistical population included managers of public and private banks in Tehran, with 384 participants selected using Cochran's formula. Data were gathered via three tools: a risky behavior questionnaire, a performance checklist based on the profit frontier model, and a board characteristics inventory. Instrument reliability and validity were confirmed through confirmatory factor analysis, AVE, CR, and the Kolmogorov–Smirnov test. Data analysis was performed using Structural Equation Modeling (SEM) in SmartPLS. Results showed that board characteristics significantly and directly influenced both risky behaviors and bank performance. Risky behaviors also had a significant and positive effect on bank performance based on the profit frontier approach. All t-statistics exceeded the 1.96 threshold, and the overall model fit index was reported as 0.603, indicating a strong model fit. The findings underscore the critical role of board structure in shaping banks’ risk-taking behaviors and ultimate performance outcomes. When governed effectively, risk-taking can contribute positively to performance. The study highlights the necessity of integrating corporate governance and risk behavior frameworks in bank performance evaluations.

The present study employs a parameter estimation method with time-varying coefficients and the Kalman Filter approach to conduct a state-space analysis of the convergence between the financial market and economic development in Iran from 1996 to 2023. The results obtained from the estimation of the state-space model (Kalman Filter) indicate that financial development, governance quality, rentier income, trade volume, and employment contribute to economic development by 6%, 0.2%, 7%, 10%, and 3%, respectively. Additionally, for every one percent increase in inflation rate and exchange rate, economic development declines by 2% and 7%, respectively. Moreover, the estimated parameters of the state-space model using the Kalman Filter reveal that the elasticity of economic development relative to financial development over the period under study is less than one. Specifically, between 1996 and 2005, the relevant sensitivity coefficient was 0.16. From 2010 to 2023, the elasticity of economic development with respect to financial development follows a declining trend. It is important to note that the implementation of financial market development processes does not necessarily result in growth or positive effects and requires structural alignment of the domestic economy, economic regulation, coordinated policies, and macroeconomic stability. Furthermore, an examination of the sensitivity of economic development to rentier income shows that the average sensitivity throughout the study period is less than one, approximately around 3%. In other words, with an increase in oil prices in the Iranian economy, capital is directed not into the productive and value-added sectors, but rather into imports. This diversion is a response to the country's stagflation conditions, intended to counter inflation. As a result, the productive sector faces serious harm, with many production units exiting the economic cycle. Capital that would otherwise be utilized in the productive economy remains stagnant and is inevitably redirected toward the black market and speculative activities.

Information serves as the primary resource for stakeholders' decision-making. The greater the readability and quality of information, and the lower the information asymmetry, the easier and more effective the decision-making process becomes for stakeholders. In this context, the present study aims to examine the relationship between the readability of financial reports and the quality of financial reporting, with a particular emphasis on the role of information asymmetry, in the Tehran Stock Exchange between 2016 and 2022. Financial reporting quality was assessed using the Dechow and Dichev model, while the readability of annual reports was measured using the FOG index. Information asymmetry was evaluated from the adverse selection perspective based on the model of Venkatesh and Chiang (1986), and from the moral hazard perspective based on the model of Abdi Golzar et al. (2021). The research hypotheses were tested using data from 129 companies through least squares regression analysis using Stata software, version 17. The findings indicated a significant and direct relationship between the readability of annual reports and the quality of financial reporting. Information asymmetry, from the adverse selection perspective, weakens the positive relationship between financial report readability and financial reporting quality, while moral hazard does not have a significant impact on this relationship.

CEO characteristics significantly influence corporate decisions regarding financing, investment, and operations. Overconfident CEOs tend to overestimate their capabilities and underestimate the risks associated with projects. This behavior can negatively affect the financial continuity and sustainability of small and medium-sized enterprises (SMEs), as such CEOs often perceive external funding to be more costly than internal resources and, consequently, restrict their firm’s access to capital. In this context, the aim of this article is to examine the effect of managerial overconfidence on corporate sustainability in SMEs. To achieve this, two indicators—capital expenditure and overinvestment—are used as proxies for CEO overconfidence. The research method employed in this study includes a sample of 132 companies listed on the Tehran Stock Exchange during the period from 2017 to 2023. Quantile regression technique is used for data analysis. The results of the analysis indicate a significant and negative relationship between the overconfidence indicators and corporate sustainability. In other words, an increase in CEO overconfidence leads to suboptimal financial decisions, which in turn have a detrimental impact on corporate sustainability. The findings of this study highlight the adverse and complex effects of overconfidence on firm performance and sustainability and suggest that managers and decision-makers should consider realism and caution when making financial and investment decisions.

The digitalization of banking processes has significantly impacted financial performance and operational efficiency in banks. This study investigates the effects of digitalization on banks’ financial efficiency using a data-driven modeling approach. Financial data from 30 banks across different countries from 2012 to 2024 were collected and analyzed using dynamic panel models and Data Envelopment Analysis (DEA). The findings indicate that digitalization positively influences financial efficiency by enhancing data processing speed, reducing operational costs, and improving customer experience. Additionally, banks that leverage AI and machine learning in their financial processes exhibit superior performance compared to their counterparts. However, challenges such as high initial investment costs, cybersecurity risks, and organizational resistance to technological changes hinder full-scale adoption. This study provides strategic recommendations for policymakers and banking executives to optimize digitalization strategies.

In recent years, Robo-Advisors have emerged as effective tools for assisting individual investors in making optimal financial decisions. This study examines the impact of Robo-Advisors on individual investors' decision-making performance in Iran’s capital market. Data were collected through a randomized controlled trial (RCT) involving 200 individual investors, tracking their investment performance over a 12-month period. The results indicate that investors using Robo-Advisors made more rational decisions and achieved higher investment returns compared to their counterparts. Furthermore, financial experience and trust in technology played significant roles in the adoption and utilization of these systems. Challenges such as a lack of understanding of algorithm functionality and concerns about data security were identified as major barriers to adoption. This study provides recommendations for enhancing the acceptance and efficiency of Robo-Advisors in capital markets.

Deep learning-based financial forecasting models are increasingly used to analyze corporate profitability. This study investigates the impact of such models on profitability prediction accuracy compared to traditional methods. Financial data from 60 Tehran Stock Exchange-listed companies from 2015 to 2024 were collected and analyzed using deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Gradient Boosting models. The findings indicate that deep learning models outperform traditional regression and time series models in profitability prediction accuracy. Additionally, integrating deep learning models with dimensionality reduction techniques enhances performance. However, computational complexity, data volume requirements, and high processing costs remain major challenges in implementing these models in financial environments. This study provides recommendations for optimizing the use of deep learning in profitability forecasting.

Big data plays a crucial role in the financial decision-making of multinational corporations (MNCs). This study aims to analyze the impact of big data utilization on improving financial decision-making in MNCs. Financial and operational data from 50 multinational firms between 2015 and 2024 were collected and analyzed using dynamic panel regression models and time series analysis. Market volatility indices, return on investment, and financial efficiency were considered dependent variables. The findings suggest that big data enhances financial forecasting accuracy, improves resource allocation, and reduces macroeconomic decision-making risks. The role of AI-driven analytics in interpreting big data and optimizing financial processes was also evident. This study provides strategic recommendations for financial managers on maximizing big data’s potential.