فصلنامه دانش حسابداری
پیاپی 39 (زمستان 1398)

The purpose of this research was to investigate the role of management earnings forecast errors in stock market efficiency with emphasis on operation earnings and their components in the Tehran Stock Exchange from 2009 to 2017.

Data was collected using document mining method, and the data analysis method was inferential. Panel data model and multivariate regression method were used to test the research hypotheses, using the Pethnoster test. In this study, the dependent variables are the abnormal stock returns and the first error of management forecast adjustment, and the independent variables are free cash flow and accruals.

The findings of this study showed that operating earnings and their components have positive effects on future abnormal returns. Also, the persistence of operating earnings and their components have negative impacts on the first adjusted management earnings forecast errors. Other results suggested that adding management earnings forecast errors to the earnings persistence model and its components have no effect on future abnormal returns. In addition, it was found that adding abnormal returns to the earnings persistence model and its components affect the error of the first adjusted management earnings forecast errors.

In sum, the results of this study show that participants in the capital market do not consider the persistence of operating earnings and their components to be effective in their expectations, and take their effects less than real. Also, the current information from management earnings forecast errors has not changed market inefficiencies.

This study examined the presence of the phenomenon of recency and the role of accounting information quality in the recency manifestation, concentrating on capital market mechanism with the use of accounting information.

In this study, earning per share, return on assets, return on equity and book value per share were used as indicators for accounting information. The method of data analysis was inferential. To test the research hypotheses, panel data multiple regression model and data of 148 companies in the Tehran Stock Exchange during 2008-2017 were used.

The findings showed that accounting information quality mitigates the recency phenomenon in investors behavior in the Iranian capital Market.

By improving the quality of accounting information reported by companies, behavioral bias in investing decision making cab be reduced.

Money laundering is one of the important issues that auditors should pay attention to in their investigations. Money laundering is a global issue and has devastating effects on corporate performance and auditing, and hence, on the economy. The purpose of this study was to investigate the effects of non-compliance with anti-money laundering law on audit fee.

This research is a quantitative research from the perspective of implementation process, meaning that an objective approach is taken for data collection and quantitative analysis. Hypothesis testing was performed, using regression model and statistical sample of 78 companies listed in the Tehran Stock Exchange during the period 2013-2017.

The findings of this study, in line with the literature in their field, showed that there is a positive and significant relationship between non-compliance with anti-money laundering law and audit fees.

According to the results of this study, non-compliance with the anti-money laundering law increases the clients’ business risk and audit risk. Therefore, auditors increase their efforts that increase audit costs, resulting in requesting additional audit fees.

This study aimed to examine the effects of Political connections on accounting conservatism, investment efficiency and cost of financing in the companies listed in the Tehran Stock Exchange during the period 2013 to 2017.

Data was collected using document mining method and referring to the databases, and data analysis method was inferential. Eviews software was used to provide estimates for the concerning variables. The panel data model was used to test the research hypotheses.

The findings of the research indicated that political connections in the the companies have negative and significant effects on accounting conservatism, and have negative and significant effects on cost of financing. Also, according to the research findings, company's political connections do not have significant impacts on capital efficiency.

As the debt ratio of corporation increases, the level of conservatism decreases. This means that companies that have political connections are less conservative than other companies.

Given the importance of lean inventory and inventory cost as barrier to better performance, there is the challenge of whether absorption costing with higher production and more inventory lead to improved performance, or inventory costs overcome the benefit of absorption costing and makes the performance worse. This study aimed to examine the relationship between inventory investment and company’s performance.

In line with positive accounting research, this study used panel data analysis with data from 163 companies listed in the Tehran Stock Exchange from 2009 to 2016.

Findings showed that there is no significant relationship between inventory investment and company’s performance. Furthermore, the findings indicated a significant relationship between changes in inventory investment and sales growth.

Inventory holding in the Iranian companies could not have statistically significant effects on company performance.

The purpose of this study was to examine the impact of political connection and some key corporate governance proxies, such as independence of the board of directors, institutional shareholders ownership on tax aggressiveness, and the impact of these proxies on the relationship between political connection and tax aggressiveness.

The panel data model was used to test and analyze the research hypotheses, and tax aggressiveness was measured by the difference between temporary and permanent book taxes. After applying some restrictions, 121 companies listed in the Tehran Stock Exchange were selected as the sample of the research in 2012-2017.

The findings of this study indicated that having a political connection with the government has positive and significant effects on tax aggressiveness, and that the existence of independent board of directors and the amount of institutional shareholders in the company have negative and significant effects on tax aggressiveness. Also, the findings showed that the independence of the board of directors and institutional shareholders reduce the positive relationship between political connection and tax aggressiveness.

It can be concluded that because of the advanteges of having relationship with government, companies that have political connection with the government have a high level of tax aggressiveness that can be reduced by using corporate governance mechanisms such as independence of the board of directors and presence of institutional shareholders. These mechanisms may reduce the positive impact of political connection on tax aggressiveness.

This study aimed to investigate the determinants of sticky cost behavior including adjustment costs, agency costs, political costs and corporate governance in the companies listed in the Tehran Stock Exchange, using the structural equation modeling (SEM) approach.

The sample included 1695 company-year in the years 2006-2017. The Smart_PLS program was used to obtain a good-fit model of cost behavior, and confirmatory factor analysis (CFA) was used to construct measurement models to validate the latent variables underlying the cost behavior model. Exploratory factor analysis (EFA) and multiple regression analysis were used to identify the determinants of cost stickiness.

The results showed that adjustment costs and agency costs are positively correlated with the degree of cost stickiness, but political costs and corporate governance are negatively correlated with the degree of cost stickiness.

The results of this study are useful for managers to understand cost behavior needed for planning, controlling and reducing costs.

The aim of this study was to explain the importance of accounting data information in forecasting inflation rates, using a sample of 90 large companies listed in the Tehran stock exchange during 1385-1395 (1980 year-company).

Given the complex and nonlinear properties of inflation in this study, we relied on the predictive power of various artificial intelligence models including neural networks, genetic algorithms and particle swarm optimization. To have contribution to macro-accounting knowledge, a number of accounting variables were selected and their explanatory power was tested in forecasting two inflation rates of producer price index and consumer price index.

The findings indicated that the hybrid model of artificial neural networks, genetic algorithms and particle swarm optimization (HANGAPSO) are more accurate in predicting the inflation rates than other models. The model results, using accounting variables, also showed that the forecast error of producer price index is lower than the forecast error of consumer price index.

In sum, the results of this study verify the importance of accounting information at macroeconomic level, and that this information should be used in macro-level decision-making.