Accounting, Finance and Computational Intelligence
Year:2025 | Volume:3 | Issue:1|Papers Count:18

The legal quality of audit engagements is one of the key factors in enhancing financial transparency and providing assurance to organizational stakeholders. Organizational culture and business ethics, as influential factors on this quality, play a vital role in improving auditors’ performance. This study aims to analyze the impact of these factors and the moderating role of the auditor’s professional judgment, seeking to provide a novel insight toward strengthening audit engagements. The objective of this research is to examine the effect of organizational culture and business ethics on the legal quality of audit engagements, considering the moderating role of the auditor’s professional judgment. According to the classification of research by purpose, this is an applied study, and in terms of data collection method, it is survey-based. The statistical population consists of financial managers and employees of institutions affiliated with the Association of Certified Accountants in Tehran. Based on Morgan’s table, the sample size was determined to be 384 individuals, and the sampling method was random. The validity and reliability of the questionnaire were confirmed, and data were analyzed using PLS3 software. The findings of this study indicate that improving the legal quality of audit engagements can be achieved through enhancing organizational culture and adherence to business ethics. Moreover, the moderating role of the auditor’s professional judgment shows that fair and appropriate determination of audit fees can strengthen the effectiveness of audit engagements. These results are beneficial for company managers, auditors, and regulators, as emphasizing these factors can enhance financial transparency and trust. Ultimately, these achievements will lead to improved financial health and sustainable organizational performance.

This study was conducted with the aim of examining the impact of strategic factors, challenges, and drivers on the future development of electronic banking services in the Keshavarzi Bank. The statistical population of the research consisted of all employees of the Keshavarzi Bank. Accordingly, based on the Krejcie and Morgan table, a sample of 384 individuals was selected. Anticipating that some questionnaires might not be completed correctly or returned, a total of 390 questionnaires were distributed to ensure that 384 valid questionnaires would be available. After collecting the questionnaires, it was found that 384 of them were usable. Therefore, the response rate was 98%. The data were analyzed using SPSS 24 and Smart PLS 3 software through structural equation modeling (SEM). The results indicated that strategic factors have a positive and significant effect on the future development of electronic banking services in the Keshavarzi Bank. Challenges have a negative and significant effect on the future development of electronic banking services in the Keshavarzi Bank. Drivers have a positive and significant effect on the future development of electronic banking services in the Keshavarzi Bank.

Investments in productive assets intended for the modernization and expansion of production processes—and in response to market demand—are influenced by a range of internal and external organizational parameters, which together shape the investment strategy model. Considering the critical role that investment plays in profitability and the attainment of financial competitive advantage within businesses, this study aims to identify the factors influencing investment and to develop a model for investment strategy. The firms within the study’s scope are divided into two categories: chemical companies and food & beverage companies, with the results of the model implementation analyzed and compared across these two groups. In order to ensure compatibility of the model with environmental fluctuations and internal organizational changes, the variables of the business cycle and the corporate life cycle are incorporated into the model. The research methodology follows a mixed-methods approach comprising both qualitative and quantitative phases. The study's findings, covering the period from 2016 to 2023, reveal that the declining trend in the value of fixed assets indicates equipment obsolescence and a critical need for investment—particularly in the chemical sector, which relies heavily on imported advanced technologies. However, under current sanctions, such investment is unfeasible. Moreover, due to the presence of ownership entities and existing conflicts of interest in this profitable industry, excess investment is observed, which—based on the findings—is ineffective and lacks the necessary efficiency. In the food industry group, regulated pricing policies have led to reduced profitability and financial constraints, thereby preventing the sector from making the necessary levels of investment despite its strategic importance.

The aim of this study is to identify the components of corporate stock returns using novel composite variables in the Tehran Stock Exchange. Employing a systematic review and meta-synthesis approach, the researcher analyzed the findings and outcomes of previous scholars. Through the application of the seven-step method proposed by Sandelowski and Barroso, the influential factors were identified. Out of 553 articles, 51 were selected based on the CASP method, and the validity of the analysis was confirmed with a Kappa coefficient of 0.747. In this context, the Kappa index was used to assess reliability and quality control, and the value indicated an excellent level of agreement for the identified indicators. The analysis of the collected data using MAXQDA software led to the identification of 48 initial concepts based on 12 indicators across 4 dimensions. To identify the components of stock returns using novel composite variables in the Tehran Stock Exchange, the meta-synthesis technique was applied. The identified dimensions include financial and economic factors, behavioral and emotional factors, technological and data-driven factors, and institutional and regulatory factors. The findings of this study indicate that stock returns are influenced by a network of diverse factors that interact in complex ways. Attention to these factors and the adoption of appropriate strategies in investment management, economic policymaking, and the development of technological and regulatory infrastructures can enhance market efficiency and increase investor returns. Therefore, innovative and comprehensive analytical approaches are deemed essential for a better understanding of the mechanisms influencing the stock market.

The misery index is a key indicator for assessing a society's economic condition and its impact on public welfare. An increase in this index signals an intensification of economic problems that affect the well-being of individuals. Consequently, the misery index functions as an important tool for policymakers to adopt appropriate decisions aimed at improving economic conditions. Particularly during periods of crisis and economic recession, this index serves as a warning signal, highlighting the urgent need for corrective policy measures. One of the variables influencing the misery index is the exchange rate. Given the significance of the misery index and recent exchange rate surges in recent years, the present study evaluates the causal relationship between the exchange rate and its volatility with the misery index over the period from Spring 2001 to Summer 2024. Considering the numerous economic fluctuations in the country and the likelihood of structural breaks, the study employs the Fourier approximation to account for structural breaks in the stationarity tests of the variables, cointegration, and causality tests. In addition, the GARCH method is used to estimate exchange rate uncertainty for the purpose of examining the causal relationship between exchange rate uncertainty and the misery index. The results indicate a unidirectional causal relationship from the exchange rate to the misery index and a bidirectional causal relationship between exchange rate uncertainty and the misery index. Therefore, exchange rate instability can have damaging effects on the economic welfare of the population (misery index). Since external shocks primarily influence the national economy—and subsequently inflation and unemployment—through the exchange rate and its fluctuations, it is essential to prioritize the monitoring and forecasting of such shocks and the use of tools that can enhance the resilience of the domestic economy against them.

The present study aimed to investigate the indicators of the FinTech business model in alignment with corporate social responsibility (CSR) practices and the improvement of corporate financial performance. The participants of this research included university professors and managers. Individuals were selected using a purposive sampling method. The sample consisted of 20 experts and specialists. The data collection instrument comprised two components: (1) the examination and analysis of upstream documents and financial planning documents in the library section, and (2) semi-structured interviews in the field section. The semi-structured interviews with participants continued until the point of theoretical saturation. For the analysis of qualitative data, the thematic analysis method based on the Attride-Stirling model was employed. To ensure validity, the interview questions were reviewed and approved by three financial planning and management experts—one holding a master’s degree and two holding PhDs. To assess reliability, Krippendorff’s alpha coefficient was used, which was confirmed. The ATLAS.ti software was utilized for the thematic analysis. The results from the factor analysis indicated that among the 192 existing indicators (items), 48 first-order constructive themes were identified, leading to the derivation of 12 second-order constructive theme categories. The second-order constructive themes of the research model included: service innovation, operational financial transparency, organizational social accountability, environmental sustainability, customer-centric value creation, digital data security, sustainable profitability growth, equity in access, intelligent risk management, stakeholder commitment, organizational decision-making agility, and alignment with social mission. Ultimately, four quantitative criteria were used to evaluate credibility, transferability, confirmability, and dependability. The results were as follows: the level of expert agreement, measured using Holsti’s coefficient (PAO) or “Percentage Agreement Observed,” was found to be 0.810, which is a significant value. Given the critiques of the Holsti method, the P-Scott index was also calculated, yielding a value of 0.813. The fourth index used to estimate qualitative research credibility was Cohen’s Kappa coefficient, which reached 0.804 in this study. Finally, Krippendorff’s alpha was also used, and it was estimated at 0.852 in this study.

This study aims to examine the relationship between corporate social responsibility (CSR) and earnings quality among companies listed on the Tehran Stock Exchange during 2021–2023. This applied, correlational research employed a purposive sample of 80 firms. CSR was assessed through a multidimensional composite index comprising five dimensions: corporate governance, product, employees, environment, and community. Earnings quality was measured using two models: abnormal accruals (Jones Model) and earnings management (Shivakumar Model). Data were gathered from financial statements, accompanying notes, and structured questionnaires and analyzed using SPSS and EViews software. Results revealed a significant negative relationship between CSR and earnings management indicators, suggesting that higher CSR levels reduce earnings manipulation and abnormal accruals. Additionally, CSR dimensions such as environmental responsibility, product integrity, and employee engagement had varying degrees of impact on different measures of earnings quality. However, results differed depending on the measurement model used. This study confirms that CSR enhances earnings quality, though explanatory power varies based on the measurement approach. These findings are valuable for investors, regulators, and managers aiming to improve financial transparency and reporting credibility through CSR integration.

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.

The development of effective and competitive strategies in financial markets is vital. Therefore, the main objective of this study is to design and validate a value chain model for Tejarat Bank based on marketing strategies. This research is qualitative in nature and employs a descriptive-exploratory approach in terms of data analysis, using thematic analysis as the primary method. The data collection tool used in this study is in-depth (semi-structured) interviews. The statistical population includes 10 experts and specialists in the field of marketing. Members of the expert panel were selected using a purposive sampling approach; key codes extracted from the interview transcripts were analyzed using the thematic analysis approach with the aid of MAXQDA software. The validity of the research findings was assessed and confirmed using Creswell’s (2002) criteria. Based on the results of this study, through analyzing and categorizing descriptive codes derived from the interview texts, 42 foundational themes were identified. Considering their semantic similarities and relevance, these themes were categorized into six overarching themes: data-driven analysis and decision-making, transformative banking technologies, innovation in banking revenue models, digital customer experience, environmentally friendly banking, and banking service architecture.

The present study aimed to identify the components influencing the optimization of enterprise risk management (ERM) with an emphasis on brand equity and financial technology. The participants in this research were selected experts with at least 15 years of executive experience in the field of risk and branding, as well as at least 10 years of experience in authoring articles and books on ERM and conducting research related to this field. The selection of participants was carried out through purposive sampling. The sample consisted of 19 subject-matter experts. The data collection instrument in the field section was a semi-structured interview, which continued with the participants until theoretical saturation was reached. For the qualitative data analysis, the thematic analysis method based on the Attride-Stirling framework was employed. To assess reliability, Holsti’s coefficient, Scott’s pi coefficient, Cohen’s kappa index, and Krippendorff’s alpha were utilized and confirmed. In the thematic analysis phase, ATLAS.ti software was used. A total of 95 initial themes were identified and classified into 19 first-level organizing themes and 4 second-level organizing themes. The main organizing themes included: brand–risk integration, technological innovation in risk management, organizational empowerment in risk response, and soft and knowledge-based infrastructure. Overall, this study, by presenting a conceptual model comprising four main axes and 19 first-level organizing themes, provided a comprehensive framework for understanding and implementing ERM optimization—one that not only perceives risk as a threat but also considers it as an opportunity for value creation and brand capital enhancement. The attention to financial technologies alongside brand equity represents a new and intelligent strategy for reducing organizational vulnerability in the digital era. The findings of this research can serve as a basis for developing operational models, designing managerial software, and providing practical training for managers at various levels in the domains of risk and branding. The future of risk management will be a data-driven, technology-based, and customer-oriented future that can be better understood and guided through such frameworks.

The objective of the present study is to predict audit risk using the Long Short-Term Memory (LSTM) algorithm in companies listed on the Tehran Stock Exchange and to compare its results with those of other deep learning algorithms. To achieve this objective, a total of 1,650 firm-year observations (150 companies over 11 years) were collected from the annual financial reports of companies listed on the Tehran Stock Exchange during the period from 2013 to 2023. In this study, four deep learning algorithms—including Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)—were utilized. Additionally, to select the final research variables for model construction, the two-sample mean comparison test method was applied. The results of the deep learning algorithms show that the overall accuracy of the LSTM, SVM, CNN, and RNN algorithms was 99.1%, 89.6%, 85.8%, and 96.4%, respectively, indicating that the LSTM algorithm has the best performance and the CNN algorithm the poorest performance in predicting audit risk. In other words, the results demonstrate the superior efficiency of the Long Short-Term Memory (LSTM) algorithm compared to other deep learning algorithms. Therefore, in companies listed on the Tehran Stock Exchange, the LSTM algorithm provides the most efficient model for audit risk prediction. The findings of this study can offer useful insights into enhancing the prediction of audit risk and minimizing errors in evaluating financial statement information, improving the assessment of audit evidence based on data, and facilitating auditors’ ability to issue more reality-based opinions.

In recent decades, the increasing complexity of the business environment, stakeholder pressures, and the demand for transparency in financial markets have directed organizations toward forward-looking, integrated reporting. This type of reporting, by integrating financial and non-financial information and emphasizing long-term forecasts, contributes to improved decision-making and organizational sustainability. In Iran, the development of the capital market and the regulatory requirements of the stock exchange have underscored the necessity of designing reporting models tailored to local conditions. In response to this need, the present study aimed to provide a comprehensive model for integrated reporting in publicly listed companies in Iran. Therefore, the objective of this research is to propose a forward-looking information-based integrated reporting model. This research is applied in nature, and in terms of data collection, it is exploratory and employs the grounded theory method (Strauss & Corbin). The qualitative population consists of experts in the field of accounting and reporting, selected through purposive sampling and theoretical saturation, ultimately including 11 participants based on the criterion of theoretical saturation. The data collection tools included semi-structured interviews and a researcher-developed questionnaire. The collected data were analyzed using open, axial, and selective coding and structural equation modeling in PLS software, through which the final model was identified. Based on interview analyses, 150 open codes, 29 sub-categories, and 6 main categories were extracted. At the grounded theory stage, forward-looking integrated reporting was defined by the characteristics of information integration, future orientation, and strategic value creation. Influential factors were identified as causal factors (stakeholder requirements, legal pressures), contextual factors (business environment, technology), intervening factors (uncertainty, constraints), strategies (technology, stakeholder engagement), and consequences (transparency, sustainability).

Predicting the occurrence of negative stock returns is a central issue in behavioral finance and risk management, playing a critical role in improving investment decision-making and enhancing capital market efficiency. This study employs advanced artificial intelligence algorithms, including artificial neural networks, support vector machines, decision trees, and AI-based optimization algorithms, to develop models for forecasting negative stock returns. These algorithms, with their ability to learn complex patterns and uncover nonlinear relationships among financial data, demonstrate significantly better performance than traditional methods in the early detection of negative return risks. Financial and reporting data from companies listed on the Tehran Stock Exchange were collected over a five-year period, and variables related to conservative financial reporting were incorporated into the analysis. Statistical analyses and performance evaluations of 101 listed companies during the period 2010 to 2015 indicate that conservative reporting—by emphasizing the early recognition of losses and moderating profit recognition behaviors—has a significant impact on improving the accuracy and generalizability of AI models in predicting the risk of negative returns. The findings suggest that integrating intelligent approaches with conservative reporting measures can enhance the quality of financial information, reduce uncertainty, and increase transparency in investment decisions. This research not only deepens scientific understanding of the relationship between conservative financial reporting and stock return risk but also offers practical applications for financial analysts, managers, and capital market policymakers.

This study investigates the impact of objectivity, political communication skills, and social undermining on auditors’ professional skepticism and explores how this skepticism influences their willingness to report detected fraud. Financial transparency and increased public trust in audit reports are among the main goals of this research. Auditors, as independent financial observers, must maintain high levels of impartiality and precision in their judgment processes and avoid any bias or external influence. The present study aims to examine the factors affecting this professional impartiality and independence. The research employed a descriptive-correlational design and utilized Structural Equation Modeling (SEM) to test the conceptual model. The statistical population consisted of members of the Iranian Association of Certified Public Accountants who were either employed or partners in auditing firms during the winter of 2024. Data were collected using a standardized questionnaire comprising items related to the research variables. The sample size was determined using Cochran’s formula, and data analysis was conducted with AMOS version 26 software. The findings revealed that objectivity (impact coefficient = 0.59) and political communication skills (impact coefficient = 0.50) have significant and positive effects on auditors’ professional skepticism. Conversely, social undermining showed a significant negative impact (impact coefficient = -0.04) on this variable. Furthermore, it was found that professional skepticism, as an independent variable, directly increases auditors’ willingness to report detected fraud. This indicates that auditors with higher levels of professional skepticism are more likely to report violations and prevent the dissemination of misleading information. This study emphasizes that strengthening objectivity and political communication skills among auditors leads to improved quality in professional judgment and enhances financial transparency. Additionally, social and organizational pressures that compromise auditors’ independence and impartiality must be controlled and reduced, as these factors can decrease the accuracy and reliability of audit reports. The findings of this study can be useful for regulatory bodies, financial policymakers, and auditing organizations to help elevate professional auditing standards. It is recommended that training programs be developed to reinforce auditors’ professional skepticism and communication skills, enabling them to maintain independence and impartiality in the face of social and organizational pressures. Finally, future research should examine the influence of other individual and organizational variables on professional skepticism and fraud reporting and apply advanced analytical tools such as artificial intelligence and machine learning to enhance fraud detection processes.

This study aims to investigate the role of institutional ownership in mitigating behavioral market volatility by focusing on stock price synchronicity in the Tehran Stock Exchange. The research is applied and analytical. Data from 124 listed firms during 2016–2023 were analyzed using panel data regression models and evolutionary game simulations. The dependent variable was stock price synchronicity (R²), while independent variables included institutional ownership percentage, institutional trading volume, market return, and cost of capital. Investor behavior dynamics were modeled through replicator equations implemented in MATLAB. Regression results showed a significant negative effect of institutional ownership and trading volume on stock price synchronicity (p < 0.01). Cost of capital had a significant positive effect, while control variables such as firm size and stock liquidity were negatively associated with synchronicity. The evolutionary game model revealed that under conditions of high institutional ownership and low capital cost, analytical strategies became dominant, reducing imitative behavior. Sensitivity scenarios confirmed that institutional impact is conditional on structural and behavioral parameters. Institutional investors can help reduce behavioral volatility through enhancing analytical behavior and limiting emotional trading, provided that capital costs and behavioral sensitivity are appropriately managed. The integration of statistical and behavioral modeling in this study offers a comprehensive framework for understanding decision-making dynamics in emerging markets.

This study aims to examine the determinants of currency depreciation in Iran over the period 1979–2018. This research utilizes time-series data and the Johansen–Juselius cointegration method. The model includes key macroeconomic variables such as exchange rate, liquidity, budget deficit, investment, oil revenues, and balance of payments deficit. After testing the stationarity of the variables, the Toda–Yamamoto causality test and an error correction model (ECM) were used to analyze both long-term and short-term relationships. The findings reveal that liquidity, budget deficit, and balance of payments deficit have a positive and significant effect on the exchange rate (indicating depreciation of the national currency). Conversely, investment and oil revenues exhibit a negative and significant impact, implying their role in strengthening the national currency. The adjustment coefficient in the ECM is –0.80, indicating that 80% of deviations from the long-run equilibrium are corrected within one period. The Toda–Yamamoto causality results confirmed causal relationships from all independent variables to the exchange rate at a 95% confidence level. The study concludes that currency depreciation in Iran is primarily driven by internal macroeconomic imbalances, particularly the expansion of liquidity, persistent budget deficits, and reduced investment. On the other hand, increased oil revenues play a compensatory role in stabilizing the national currency. Therefore, coordinated economic policies targeting fiscal discipline, investment promotion, and monetary control are essential for maintaining currency stability.

Geopolitical risks stemming from global and regional events can play a significant role in shaping asset price volatility. However, there is limited empirical evidence on how these risks influence return volatility in emerging markets, including Iran's capital market. This study examines the impact of geopolitical risks on the return volatility of the Tehran Stock Exchange using the GARCH-MIDAS hybrid model, which allows for the integration of high- and low-frequency data. In this research, quarterly data on the Tehran Stock Exchange Price Index is used as the dependent variable, while monthly geopolitical risk indices serve as the independent variables. The global indices include the Geopolitical Threat Index (GPT) and the Geopolitical Acts Index (GPA), and the regional indices cover the geopolitical risk levels of four countries: Russia, Ukraine, Turkey, and Saudi Arabia. The results obtained from model estimation indicate that geopolitical risks associated with Turkey and Ukraine have significant and varying effects on the daily return volatility of the Tehran Stock Exchange. In particular, Turkey’s geopolitical risk index has demonstrated a notable impact on market fluctuations during specific time intervals. In contrast, there is no strong or consistent evidence of significant effects from the indices related to Russia and Saudi Arabia. The study’s findings suggest that while regional geopolitical risks have a limited overall impact on Iran's capital market, risks linked to Turkey and Ukraine have, at times, led to considerable volatility. These results highlight the importance of incorporating geopolitical risk into modeling and forecasting return volatility in emerging markets—especially in Iran’s capital market—and provide a basis for policymaking by investors and regulatory bodies.

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.