Accounting, Finance and Computational Intelligence
Year:2024 | Volume:2 | Issue:4|Papers Count:17

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.

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.

This study aimed to design and explain the structural model of factors influencing digital financial innovation using a structural equation modeling (SEM) approach. The research followed an exploratory mixed-methods design. In the qualitative phase, semi-structured interviews were conducted with 18 financial and accounting experts selected via snowball sampling until theoretical saturation was reached. Thematic data were analyzed using the grounded theory method through open, axial, and selective coding, which resulted in 18 main categories. A 93-item questionnaire was developed based on the extracted categories and verified for reliability and validity. In the quantitative phase, the questionnaire was distributed to 385 capital market professionals, and the model was evaluated using LISREL 8.80 and SmartPLS 3.0. Confirmatory factor analysis (CFA) showed that all measurement models achieved acceptable or excellent model fit indices. The standard path coefficients for causal conditions, context, intervening conditions, strategies, and outcomes were all above 0.30, and t-values exceeded 1.96, indicating statistical significance. The proposed model demonstrated strong construct validity and good explanatory power for digital financial innovation determinants. The final structural model effectively explains the formation of digital financial innovation. The three core categories—“adoption capability,” “ICT infrastructure,” and “digital literacy and awareness”—emerged as central to the model. Other components were organized into five contextual groupings: causal conditions, background, intervening factors, strategies, and outcomes, all showing statistically significant relationships with the core constructs.

The aim of this study is to examine the impact of auditors’ mental structure dimensions on the stages of auditing. The statistical population includes all auditors employed in the Audit Organization and audit firms that are members of the Iranian Association of Certified Public Accountants during the study period. Due to the absence of official statistics, the population was considered unlimited. Therefore, Cochran’s formula for an unknown population size was used to determine the sample size. The approximate sample size was estimated at 384 individuals, which may vary depending on the participation level of the target group in the quantitative phase. A researcher-made questionnaire was designed to collect data related to this variable. To assess the auditing stages, a standardized questionnaire was utilized to measure the dimensions of this construct more precisely. Finally, structural equation modeling using the Partial Least Squares (PLS-SEM) method was employed to test the research hypotheses. The findings from the quantitative section revealed that the auditors’ mental structure has a direct and significant effect on the stages of risk assessment, planning, operational execution, and audit reporting. The results of this study can provide numerous insights and applications for standard setters, auditing profession policymakers, and other regulatory bodies in Iran’s auditing market. It can help them, through a deeper understanding of auditors’ mental structure themes, to develop mechanisms and regulations aimed at improving professional judgment and enhancing the quality of audit stages.

This study aims to design and validate a conceptual model of Iranian taxpayers’ avoidance behavior based on behavioral economics theories. The research utilized a mixed-method design. In the qualitative phase, 13 semi-structured expert interviews were conducted and analyzed using open, axial, and selective coding based on grounded theory. In the quantitative phase, 384 valid responses were collected through stratified random sampling. Data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) via AMOS. SEM results revealed that five variables—cognitive biases (β = 0.31), perceived tax justice (β = -0.28), institutional trust (β = -0.26), social norms (β = -0.22), and risk propensity (β = 0.35)—had significant effects on tax avoidance behavior (p < 0.05). The final model explained 62% of the variance in avoidance behavior (R² = 0.62). The findings suggest that behavioral and social dimensions, including cognitive distortions, justice perception, institutional trust, and social norms, play a decisive role in shaping taxpayers’ avoidance behavior and should be integrated into tax policy design alongside conventional enforcement mechanisms.

This study aims to analyze the long-term effects of export diversification and industrialization on entrepreneurship in selected developing Asian countries with relatively high human development indices. The study employs panel data from 2006 to 2023 and applies FMOLS and DOLS econometric techniques to estimate the long-run effects of globalization variables (export diversification and foreign direct investment), demographic variables (urbanization and labor force), and economic development variables (industrialization and human capital) on entrepreneurship across selected developing countries. The estimation results confirm a significant long-run relationship among all variables. Both FMOLS and DOLS coefficients reveal that export diversification, foreign direct investment, urbanization, labor force, industrialization, and human capital each positively and significantly affect entrepreneurship. Labor force participation and human capital exhibited the strongest elasticities, while export diversification stimulated competitive innovation and the emergence of new businesses. Export diversification and industrialization play a critical role in enhancing entrepreneurship through innovation, employment, and increased access to productive resources and global markets. Policymakers in developing nations are encouraged to prioritize industrial expansion, attract foreign investment, and foster human capital development as essential strategies to stimulate sustainable entrepreneurial growth.

The purpose of this study is to identify the key factors influencing the implementation of service cost systems in the universities under the Ministry of Science, Research, and Technology. This research employed a mixed-methods design. In the qualitative phase, grounded theory was used through semi-structured interviews with 11 experts from academic financial departments. Data were analyzed using open, axial, and selective coding. In the quantitative phase, a researcher-made questionnaire—based on the qualitative codes—was administered to 132 respondents. Structural equation modeling was employed to test the hypotheses. The results revealed that factors such as financial and budgetary system reform, university strategies, organizational management, performance-based budgeting culture, accurate cost segregation, financial controls, cost management, and financial specialization significantly affect cost system implementation. All hypotheses were statistically significant at the 95% confidence level, with positive path coefficients indicating direct effects of these variables on implementation outcomes. Successful implementation of cost-based financial systems in universities requires coherent strategies aligned with performance budgeting, optimized resource allocation, advanced IT infrastructure, financial transparency, and recruitment of skilled personnel. These measures can enhance organizational accountability, increase informational transparency, and improve operational efficiency and effectiveness in public universities.

The significance of taxation in achieving the annual budget revenue targets necessitates the reform of tax system processes and highlights the inconsistencies between tax auditing practices and accounting standards. The need to address this issue stems from the divergence between the principles governing financial accounting and tax accounting, which leads to discrepancies between accounting profit and taxable income, thereby creating complications for corporate accountants and tax auditors. According to the conducted review, a comprehensive study on the consequences of non-compliance with accounting standards in Iran's tax auditing has not been undertaken. Furthermore, prior research has not addressed the role of accounting standards in the tax auditing process in Iran. Therefore, the present study, with an emphasis on Iranian accounting standards, seeks to identify the consequences of disregarding accounting standards in Iran's tax auditing system. The research method employed is descriptive-analytical and falls within the domain of applied research. In descriptive research, the researcher selects a population or sample to describe or explain phenomena, their relationships, and the impact of variables on each other. Applied research is conducted for a specific group, organization, or region. Accordingly, this study adopts a descriptive-analytical method within applied research. A feedforward neural network alongside the bat algorithm is utilized for prediction purposes. The findings indicate a significant inverse relationship between non-compliance with auditing standards and audit quality metrics. This implies that adherence to auditing standards in financial statements by auditors leads to increased qualifications in audit reports, inconsistency and lack of uniformity in audit reporting, reduction in the scope of professional judgment, diminished reliability of audit reports, increased audit completion time, and inconsistency in auditors' responses to identified deficiencies.

This study aims to investigate the moderating role of managerial overconfidence in the relationship between accounting conservatism and the lack of transparency in financial reporting among companies listed on the Tehran Stock Exchange between 2019 and 2024. The study population included all listed firms on the Tehran Stock Exchange. A final sample of 176 companies (1056 firm-year observations) was selected using a systematic exclusion method. Accounting conservatism was measured using the Khan and Watts (2009) model, financial opacity was estimated using discretionary accruals based on the modified Jones model, and managerial overconfidence was captured via excess investment in assets. Data analysis was conducted using multivariate regression models via EViews 13. The results revealed a significant negative effect of accounting conservatism on financial reporting opacity (β = -0.453, p < 0.01). Additionally, managerial overconfidence significantly moderated this relationship, with the interaction term (Conservatism × Overconfidence) being positive and significant (β = +0.076, p < 0.01). This implies that overconfident managers reduce the effectiveness of conservative accounting in mitigating opacity. Accounting conservatism serves as an effective mechanism to enhance the transparency of financial reports, thereby improving stakeholder trust and market stability. However, managerial overconfidence can dilute this effect by weakening the enforcement of conservative principles, potentially leading to overly optimistic financial disclosures. It is recommended that firms invest in managerial self-awareness programs to mitigate the adverse impact of overconfidence on financial reporting.

Among the anomalies observed in contrast to classical finance is momentum, a concept derived from physics that represents the persistence in past performance and examines the existence of inertia and the root of continuity in the future outperformance of previous winning stocks and the underperformance of previous losing stocks. On the other hand, industry time-series momentum focuses solely on absolute performance, and its strategies are dependent on temporally varying net long positions. Hence, time-series momentum has outpaced other strategies. In examining the role of information dissemination on the effect of industry time-series momentum, indicators with valid meanings were selected under both positive and negative signals in order to incorporate the dimension of the informational environment. In this regard, information discontinuity and noise level were used as proxies and measured across 120 listed companies using a systematic elimination method and within four decile-based portfolios structured under 3-month strategies over holding periods ranging from 1 to 36 months, during the years 2021 to 2023. Ultimately, across all long-term portfolio formation strategies, including two short-term and nine short-term holding periods, the effect of industry time-series momentum was found to be stronger during boom periods; thus, the research hypothesis is confirmed.

Marketing analytics, as a valuable tool, assists businesses in gaining a deeper and more precise understanding of their customers’ needs, preferences, and expectations in dynamic market conditions. Relying on this deep insight, companies can offer high-quality products and services tailored to customer demands, thereby significantly enhancing the customer experience. The aim of this study is to investigate the impact of marketing analytics on customer agility and financial performance, while considering the moderating roles of market turbulence and a data-driven culture within a detergent company. To this end, the required data were collected through a census method using a questionnaire administered to a statistical population of 140 employees of the detergent company. This study is applied in terms of its objective, and from an operational standpoint and in terms of data collection, it is classified as a descriptive survey. For data analysis and hypothesis testing, the PLS (Partial Least Squares) software was employed. The findings of the study indicate that marketing analytics has a positive and significant impact on customer agility and financial performance in the detergent company. Moreover, companies that utilize marketing analytics more effectively are able to achieve superior performance. However, the moderating variables—namely, a data-driven culture and market turbulence—did not significantly influence the relationship between marketing analytics and customer agility.

The objective of this study is to examine the relationship between individual ethics and the disclosure of audit report errors using the DEMATEL method. This applied, mixed-method study involved a qualitative meta-synthesis of 37 selected articles out of 279 reviewed publications. In the quantitative phase, a DEMATEL-based questionnaire was developed and distributed to 20 certified auditors with over 10 years of professional experience. The data were analyzed using matrix-based causal relationships and categorized into five dimensions: causal conditions, context, intervening conditions, strategies, and consequences. The results revealed that the most influential factors were "inherited and acquired personality and behaviors," "auditors’ decision-making and professional judgment," "moral sensitivity and courage," and "commitment and responsibility." Conversely, the most affected factors included "organizational laws and requirements," "supportive rules and incentives," "individual reactions," "community norms and values concerning whistleblowing," and "organizational structures of audit institutions." Among all variables, auditors’ decision-making and judgment had the highest impact on whistleblowing tendencies. The findings suggest that fostering audit error reporting requires a synergistic integration of personal ethics with supportive organizational and cultural environments. Establishing secure, reward-based systems and nurturing a strong sense of professional commitment among auditors can enhance ethical behavior and the willingness to report detected misconduct in audit processes.

The sustainable development approach emphasizes the balance between environmental, social, and economic dimensions, aiming to enhance the quality of life. Given the neglect of these dimensions in traditional management accounting systems, assessing the effective components of management accounting for improving corporate sustainability is essential. This study evaluates the model of corporate sustainability improvement based on modern management accounting methods, which was discovered using the qualitative research method of grounded theory. To this end, a quantitative survey method employing a questionnaire was utilized. The statistical population of the research was selected during the year 2024 through purposeful sampling from among professors and professional members. Analysis was conducted based on structural equation modeling using Smart_Pls and SPSS software. The findings indicate that the sustainable management accounting model based on modern methods assists companies in achieving environmental and social goals while maintaining financial productivity. This leads to the creation of sustainable values and the improvement of societal quality of life. The added knowledge of this research, beyond expanding the management accounting literature for enhancing sustainable development, is that continuous monitoring of goals and the use of appropriate reporting contribute to optimizing strategic decision-making and resource management in companies.

This study investigates the moderating role of corporate risk-taking on the relationship between economic policy uncertainty and managerial ability in companies listed on the Iraq and Saudi stock exchanges. The findings indicate that economic policy uncertainty has a significant and negative impact on managerial ability in both countries, particularly under volatile economic and political conditions. This effect stems from the reduced efficiency and capability of managers in resource allocation and crisis management. Moreover, corporate risk-taking, as a moderating factor, has been shown to mitigate the adverse effects of economic uncertainty on managerial ability. Firms with higher levels of risk tolerance demonstrate greater flexibility in seizing opportunities amid uncertain conditions. This study holds critical operational implications for both managers and policymakers. Enhancing transparency and the stability of economic policies, along with fostering a culture of risk-taking within firms, can reduce corporate vulnerability and improve managerial performance. The findings also suggest that companies should adopt innovative strategies and risk management practices to cope with economic uncertainty. This research provides a foundational basis for future studies and for formulating managerial policies in financial markets influenced by economic instability.

This study investigates the application of deep reinforcement learning in optimizing investment portfolios and integrates it with modern portfolio theory, illustrating significant advancements in financial management strategies. While modern portfolio theory is recognized as a mathematical framework aimed at maximizing expected returns while considering risk, its limitations—such as assumptions regarding the normal distribution of returns and the neglect of transaction costs—clearly highlight the need for adaptable solutions in complex and dynamic financial markets. This research demonstrates that through the implementation of deep reinforcement learning, investors can leverage real-time data and dynamic decision-making capabilities to develop more efficient and robust investment strategies. Moreover, challenges such as data quality, computational complexity, and the interpretability of deep reinforcement learning models are thoroughly examined. In this study, a Levenberg–Marquardt neural network algorithm for deep reinforcement learning is proposed for portfolio optimization based on historical data. For this purpose, data from 10 highly liquid companies listed on the Tehran Stock Exchange during the period from 2011 to 2021 were utilized. The findings of this study indicate that reinforcement learning algorithms can lead to a 15% increase in cumulative return compared to traditional methods in portfolio selection. Furthermore, the article recommends that analysts and investors employ advanced techniques to enhance corporate performance stability, enabling better investment decisions. Finally, this research offers practical pathways for future investigations in this domain.

The objective of this study is to identify and explain the most probable future options related to the auditing profession over the next decade. In terms of nature, this study follows a mixed-method approach (qualitative–quantitative); in terms of purpose, it is applied; and in terms of methodology, it is descriptive–analytical. In the qualitative section, scenario analysis was used to determine the key driving forces influencing futures studies of information technology development in the auditing profession. In the quantitative section, the Delphi method was applied in two stages. The statistical population of the study includes all academic and professional experts in the field of information technology development in auditing—this includes senior managers at the Audit Organization, faculty members, and professors of accounting and auditing departments who have published articles or supervised theses on topics such as factors influencing the auditing profession, the future of accounting and auditing, and information technology development. To select the sample, a purposive non-random sampling method was applied, utilizing the theoretical saturation technique. After conducting 13 interviews, data redundancy was observed, and to ensure saturation, interviews were extended to 15 participants. To ensure data validity in the qualitative section, peer review criteria were used. For reliability assessment, an intercoder reliability index was calculated. In the quantitative section, content validity was confirmed through a survey of eight academic experts, and Cronbach's alpha coefficient was used to measure the reliability of the data. The two-stage Delphi analysis results revealed the acceptance of 16 driving forces in the domain of information technology development in the auditing profession. Interpretation of the findings indicates that with technological advancement in the future, clients will have more confidence in automated auditing than manual auditing. Due to the broader fraud detection capabilities of artificial intelligence, the relationship between auditors and clients will become more strained. Additionally, because of increased automation (resulting in easier and less costly processes), audit clients will find the current pricing of auditing services less reasonable. Therefore, despite the challenges posed by emerging technologies, they are expected to serve a supportive role for auditors.