فصلنامه بررسی های حسابداری و حسابرسی
پیاپی 117 (پاییز 1403)

Given that managers play a crucial role in grounded and old theories such as agency theory, stewardship theory, and contracts theory, the concept of managerial ability and its measurement has attracted the researchers' attention. Recent dominant perspective emphasizes that manager's ability is reflected in the firm's performance. Therefore, measuring managerial ability depends on the separation of the manager's performance from the firm's performance, and the more the manager's contribution to the company's performance, the higher ability he/she has. The current study seeks to develop a model to measure managerial ability, taking into account the unique circumstances of the country in which the companies operate.

To develop a model for measuring managerial ability, a comprehensive approach is applied including systematic literature review, interpretive analysis of interviews with experts, and a descriptive method. In systematic literature review domestic and foreign studies and reliable information sources and databases is applied based on specific related keywords. Additionally, interviews with experts such as executive managers, financial managers, internal and external auditors, and academics were analyzed using interpretive analysis based on chain sampling. To run and validate the designed model, data from 158 firms listed on the Tehran Stock Exchange (TSE) across 12 industries from 2012 to 2021 is used. Triangulation was employed to evaluate the research validity, considering multiple dimensions including methodology, data, and the researchers. This rigorous approach ensures the reliability and robustness of the model developed for measuring managerial ability.

According to the research findings, we focus on three areas of performance indicators (PI), namely operational, financing, and dividend performance indicators. The performance indicators, as dependent variable, represent different aspects of firm performance based on managers decision-making process and the status of firms in real world. Variables such as firm size, firm age, the percentage of active institutional shareholders, export sales, and transactions with related parties represent the inherent characteristics of each company as independent variables. Considering environmental conditions and performance indicators are very important in determining related variables. By running Tobit regression, the residual value is considered as managerial ability measurement or managers’ contribution in firm performance. Furthermore, the comparison of the designed model and the model developed by Demerjian et al. (2012), as the most widely used model in prior literature, in terms of log-likelihood and information criteria including Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn Criterion (HQ) at the overall and industry-specific levels shows that our model has higher goodness-of-fit.

Generally, by combining operational, financing, and profit distribution indicators, as well as taking into account shareholder structure and intra-group transactions, this model provides a more proper measurement of managerial effectiveness and it can be a basis for selecting managers and determining appropriate remuneration methods.

Religiosity is a key social norm that has a significant effect on the moral behavior of managers. Religious managers are less likely to make unethical judgments and decisions. An ethical manager is expected to provide high-quality voluntary disclosure to reduce information asymmetry. Religion is considered a source of moral behavior and will have a significant effect on the behavior of managers, including their disclosure behavior. Moreover, narcissistic managers manipulate financial reports by not disclosing important information. They try to achieve their goals by reducing discretionary information disclosure to stakeholders. In addition, religiosity influences the quality of financial reporting by enhancing the moral dimension of managers' performance. Therefore, it is expected that religiosity impacts the phenomenon of narcissism and, in turn, positively affects the voluntary disclosure of information. Therefore, the purpose of the current research is to investigate the effect of religiosity on the quality of voluntary disclosure of information, emphasizing the mediating role of managers' narcissism as one of the most important factors influencing information disclosure.
The current research is quantitative in terms of the implementation method. The collected data was analyzed by structural equations. The research tool was a standard questionnaire and research variables were evaluated by it. The statistical population of the research includes financial managers who were selected as the research sample by using Cochran's formula.
The results of the structural equation model analysis showed that for the first hypothesis, religiosity has a significant effect on the quality of disclosure. Also, the resulting standard path coefficient proved the positive effect of religiosity on the quality of voluntary disclosure. Therefore, the first hypothesis of the research was confirmed with a direct and positive relationship. In the second hypothesis, the effect of religiosity on narcissism was investigated, and its significance was confirmed. The resulting standard path coefficient also showed the positive effect of religiosity on narcissism. Therefore, the second hypothesis of the research was not rejected with a direct and positive relationship. In the third hypothesis, the direct effect of narcissism on the quality of voluntary disclosure was investigated, and the findings asserted the significance of the path. Also, the resulting standard path coefficient showed the negative effect of narcissism on the quality of voluntary disclosure. Therefore, the second hypothesis was confirmed with a direct and negative relationship. In the fourth hypothesis, the mediating role of narcissism on the relationship between religiosity and the quality of voluntary disclosure was investigated and the findings showed that the narcissism of managers with a positive influence of religiosity has a significant effect on the quality of voluntary disclosure. Also, the standard path coefficient showed the positive effect of religiosity on the quality of voluntary disclosure through managers' narcissism. Therefore, the fourth research hypothesis was not rejected with an indirect and positive relationship.
The results showed that religiosity is a strong and effective behavioral stimulus influencing the quality of voluntary disclosure of information, while narcissism is an effective obstacle to the quality of voluntary disclosure of information. Religiosity will not only neutralize its effect but also improve the quality of voluntary disclosure of information. The findings of the research contribute to the literature on religiosity and moral values within economic and commercial systems and enhance the quality of financial reporting by emphasizing informal principles such as religiosity.

Although extensive literature exists on abnormal audit fees, their determinants remain relatively underexplored in contemporary accounting and auditing research. Audit fees have attracted the attention of standard developers, investors, and researchers. Recent studies highlight a growing perspective that the role of abnormal audit fees in shaping audit report quality has become increasingly contentious. In addition, credit status significantly affects abnormal audit fees. This study aims to investigate the asymmetric reactions of the abnormal audit fees component to changes in credit status.

Considering the data collection method, the current research is descriptive. Descriptive research is carried out to better understand the existing conditions or help the decision-making process. According to the different categories of descriptive research, the current research is of correlational type. To test the research hypotheses, data was gathered from 120 firms listed on the Tehran Stock Exchange from 2014 to 2022. Finally, to examine and analyze the proposed hypotheses according to the theoretical foundations, the regression approach was applied.

The analysis of the first research hypothesis shows that the reduction of credit status has a positive and significant effect on abnormal audit fees. The second hypothesis of the research is that any decrease (increase) in the credit status has a positive (negative) and significant effect on the variable (short-term) part of the abnormal audit fee. The third research hypothesis, which claimed that bankruptcy risk significantly affects the relationship between the reduction (or increase) of credit status and the variable (short-term) component of the abnormal audit fee, was rejected. Finally, the results indicate that the risk of significant distortion has a significant effect on the relationship between the reduction (increase) of the credit status and the variable (short-term) part of the abnormal audit fee.

Based on the results, it can be concluded that as profit manipulation by the owners increases, the credit status of the companies declines. Auditors exert greater effort when dealing with owners who engage in manipulation, have a high likelihood of fraud, and exhibit low-profit quality, leading them to demand higher fees. In this regard, any decrease in credit status can result in problems, financial limitations, and the risk of bankruptcy in the future. Finally, higher profit quality attracts investors, leading to increased opportunities for financing.

The higher the level of corruption in a society, the greater the number of justifications for immoral actions and behaviors. Therefore, companies that operate in highly corrupted environments face a weaker financial reporting system. The current research aims to explain various types of financial corruption and investigate their relationship with financial misstatements. Based on the theoretical foundations, three dimensions of administrative-operational corruption, administrative corruption, and political corruption were identified, and their components and indicators were explained.

The research employed a survey method with a cross-sectional design. The statistical population of the research included faculty members, financial managers, and members of the public accountant community. The sample size comprised 193 individuals, and the data were collected using a researcher-designed questionnaire. To check the validity of the questionnaire, content validity was used and to measure its reliability, Cronbach's alpha method was used. SPSS and AMOS software were used to test the hypotheses.

The results indicated a direct and significant relationship between the commercial and political aspects of corruption and financial misstatements. However, no significant relationship was found between the administrative-operational dimension and the financial misstatements. Moreover, the results of the regression analysis indicated the presence of positive and significant relationships between the dimensions of commercial and political corruption indicators and financial misstatements. In addition, the results of Structural Equation Modeling showed the acceptability of the collected data and the fit of the conceptual model of the research.

It can be argued that corruption in hiring employees and managers alone is not a sufficient factor for the occurrence of corruption and, consequently, distortion in financial statements. However, corruption in the employment of managers and employees can lead to distortions in financial statements through embezzlement, receiving bribes, extortion, influencing company tenders, fraud by managers, and conflict of interests. Also, the results showed that connection with political parties and dependence on the government, connection with special customers and not dealing with new customers and ethnocentrism can lead to distortion in financial statements. This type of communication not only affects the financial resources of the companies but also motivates the managers to provide reports and financial statements under the opinions of the government. Furthermore, on one hand, bribery through management collusion with other people can lead to the concealment of facts and provide misleading financial statements by lengthening and complicating financial reports. In addition, management can distort financial statements through manipulation of financial reports and fraud for personal gain. On the other hand, purchasing goods, materials, and services needed by the company's employees from institutions or companies in which they or their relatives have interests can increase the cost of goods and services, thereby reducing profits. Embezzlement also leads to illegal changes in the company's assets and liabilities. These changes can have serious effects on the accuracy and reliability of financial reporting. Moreover, extortion by employees can lead to the removal of customers and suppliers of raw materials of the company, hence the financial statements are also subject to change. Based on the results of the structural equation model, the indicators used to measure the existing variables have appropriate factor loadings and can be used to measure the variables of administrative corruption, commercial corruption, political corruption, and distortion in financial statements. Also, the results indicate the impact of financial statements on commercial corruption and political corruption.

Overreaction is a noticeable anomaly in financial markets that leads to various consequences, including market inefficiency. As such, one of the prominent topics investigated in major global stock exchanges is shareholders' overreaction. This phenomenon is particularly prevalent in emerging and less developed markets, where investors tend to overreact to financial events. Overreaction, as a behavioral bias, distorts investors' decision-making in uncertain conditions, pulling the market away from its efficient state. Predicting and identifying the persistence of these reactions can assist investors in making more rational decisions regarding the purchase or sale of shares and other securities. Therefore, predicting and identifying the continuation of shareholders' overreaction trends can serve as a valuable tool for investors, financial analysts, and investment managers to make decisions based on intuition and precise analysis. To create an effective predictive model, various methods, such as regression analysis, can be employed to analyze the relationship between different variables and the continuation of shareholders' overreaction trends. Additionally, artificial neural networks (ANNs), as an advanced method, can be used to model the non-linear complexities and intricate connections between variables. This topic is directly related to predicting shareholders' behavior and investment decision-making, ultimately helping improve investment strategies and risk management. Hence, the main objective of this research is to conduct a comparative analysis of artificial neural networks and linear regression in predicting the continuation of shareholders' overreaction trends.

The present study is descriptive-causal, utilizing ex post facto research. To test the research hypotheses, multivariate linear regression based on panel data and a combination of time series was employed. Data was collected using the library research method, and necessary information was gathered by studying the financial statements of companies within the statistical population. The statistical population includes all companies listed on the Tehran Stock Exchange between 2011 and 2021, with 110 companies selected through systematic elimination sampling. In data analysis, regression methods were used to examine the relationships between variables, and the results were compared with those obtained from artificial neural networks.

The results indicate the superiority of the artificial neural network model in terms of the coefficient of determination and the MSE (Mean Squared Error) index. Specifically, the highest coefficient of determination for the artificial neural network (with 1 hidden layer and 9 neurons) for test data is 0.3880, compared to 0.349 for the linear regression model. Moreover, the results show that the MSE for the artificial neural network (1 hidden layer and 9 neurons) for test data is 0.003266, compared to 0.004 for the linear regression model. Thus, similar to the coefficient of determination, the MSE index is also better in the case of the artificial neural network.

The artificial neural network model is capable of uncovering complex and non-linear patterns, providing the most accurate predictions. By using this model, stock return trends can be predicted more precisely and reliably.

Based on cognitive dissonance theory, investors tend to disregard earnings announcements that conflict with their emotional state. This bias causes investors to not react to good (bad) earnings news in pessimistic (optimistic) conditions. Both company information uncertainty and market uncertainty can influence investors’ reactions to earnings announcements. Therefore, this research aims to investigate the effect of company information and market uncertainty on investors’ cognitive dissonance, regarding earnings announcements.

This is an applied and descriptive-correlational study. Using the screening method, the statistical sample comprises 127 companies listed on the Tehran Stock Exchange, from 2011 to 2023. The dependent variable is the cumulative abnormal return for the five-day window [−2, +2] centered on the quarterly earnings announcement date. The independent variables are good and bad news, while the moderating variables include investors' sentiments, information, and market uncertainty. To analyze data and test hypotheses, multivariate regression models, quarterly data, and panel data with fixed effects were employed. The Principal Component Analysis (PCA) was utilized to create a composite sentiment index.

The results of the first hypothesis suggest that investors respond asymmetrically to good and bad news. According to the second and third hypotheses, under optimistic sentiment, investors show a strong positive reaction to good earnings news and a subdued reaction to bad news. Conversely, under pessimistic sentiment, investors react negatively to bad news and exhibit a muted response to good news. These findings confirm the presence of cognitive dissonance in both optimistic and pessimistic market sentiments. The fourth and fifth hypotheses reveal that company information uncertainty does not significantly affect the attenuation of investors’ cognitive dissonance towards earnings news. The sixth and seventh hypotheses demonstrate that reducing market uncertainty weakens investors’ muted reaction to bad earnings news in optimistic conditions but does not weaken their muted reaction to good news in pessimistic conditions.

The cognitive dissonance in stock price reactions to earnings news is influenced by the prevailing optimistic or pessimistic sentiments in the stock market. Consequently, investors should consider market sentiments in their economic decisions. Market uncertainty has a greater impact on investors’ reactions to earnings news than company information uncertainty. During periods of pessimism, investors tend to be more critical of available information, whereas, during optimism, they are more likely to accept information in real terms. This leads to higher psychological thresholds for ‘good news in pessimistic conditions. Enhancing confidence in the capital market can mitigate these cognitive inconsistencies in investors’ reactions to earnings announcements. The current economic conditions in Iran have led to investors' difficulty in accurately assessing the fundamental value of companies due to their conservatism and lack of expertise in interpreting the economic situation. Additionally, high fluctuations in macroeconomic factors have caused market participants to overly focus on external factors, thereby reducing attention to internal company factors.

According to the International Standards on Auditing (ISA), financial transaction complexity, an inherent fraud risk factor, stands among the criteria for selecting accounting journal entries to test, and implement analytical procedures for anomaly detection to assess fraud risk. However, the extant academic and professional literature lacks a structural definition of accounting journal entry complexity. This study aims to fill this gap by (1) proposing a novel quantitative measure of journal entry complexity and (2) applying it to anomaly detection techniques to identify and assess the risks of material misstatement.

Given the purpose of this study, the Design Science (DS) methodology (Hevner et al., 2004) was adopted. The DS includes two phases: artifact design and evaluation. In the design phase, a content analysis of ISA and a literature review of complexity and diversity were conducted to establish the basis for defining journal entry complexity. Subsequently, the proposed measure was adapted from diversity indices used in the biological sciences to meet the specific requirements of the research problem. This adjustment incorporated innovations from both exaptations and improvements in the contributions of DS. In the evaluation phase, descriptive and observational approaches were employed to assess and verify the novelty and utility of the proposed artifact.

In the absence of an explicit definition of transaction complexity in auditing standards and guidelines, the content analysis of ISA led to the extraction of five conceptual dimensions of complexity: (1) the number and relationships of components, (2) the nature and form of transactions, (3) measurement and processing of information, (4) quantity and quality of knowledge, and (5) the degree of change and uncertainty regarding the subject matter. Based on the first dimension of this conceptualization and its adaptation to the theoretical foundations of diversity in biological sciences, the journal entry complexity measure was defined from a structural and data-driven perspective, as a function of the number and diversity of accounts involved. Next, by adapting the biodiversity index (Clarke & Warwick, 1998) and adopting the taxonomic distance measure based on the path length to determine account distances, a quantitative measure of journal entry complexity, as a design science artifact of the model type, was provided. The measure was then applied to detect global and contextual anomalies in journal entries. The implementation and evaluation phases continued through a case study using the Python programming language for analyzing journal entry complexity to identify global and size and pattern-based contextual anomalies in 2,895 journal entries of a manufacturing company. The results and insights obtained from applying the measure were then discussed and evaluated.

Adopting an interdisciplinary approach, this study applies theoretical foundations and biodiversity measurement methods from biological sciences to create a systematic and flexible mechanism for measuring the complexity of journal entries and identifying anomalies. It seeks to improve the identification and assessment of material misstatement risks in audit analytical procedures. Moreover, using this measure helps in planning and optimizing audit resource allocation by accounting for the complexity level of audit engagements. It also improves audit sampling and prioritizes auditing journal entries based on their complexity, as an inherent risk factor.