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PurposeThe purpose of this research paper is to provide a model for reporting quality of financial information based on behavior of listed companies in Tehran Stock Exchange which is based on structural equation modeling approaches.Design/methodology/approachThis study uses applied research and postsemi experimental method of data collection in the field of proofing accounting research with deductive–inductive approach. The statistical population of this study includes the sample of 128 listed companies in the Tehran Stock Exchange between 2007 and 2017. The behavioral characteristics of managers (hidden variables) are measured by observable variables of myopia, opportunistic behavior and overconfidence of managers. Reporting quality of financial information is also investigated based on the scores accrued to each company and the announcement published by the Tehran Stock Exchange based on the companies' rating in terms of the quality of reporting and proper notification.FindingsAfter insuring the acceptable fitness of the measurement pattern and the structure of research in both approaches, structural equations modeling and regression, the results indicate that there is a significant negative relationship between the behavioral characteristics of managers and the reporting quality of financial information.Originality/valueAccountants have a critical and difficult responsibility of dealing with transactions and presenting them in the form of financial reports that can be used by interest groups to assess the performance of companies. This critical responsibility becomes meaningful when professional and ethical behaviors are the basis for disclosure of financial reporting. Based on the behavioral characteristics of disclosing financial reporting in emerging capital markets such as Iran, this study can be successful in developing new and theoretical literature in this field.

Although up to now several researches have been conducted to survey the correlation between ownership concentration and information asymmetry on one hand, and effect of corporate disclosure on the information asymmetry on the other hand, the correlation between information asymmetry, ownership concentration and voluntary disclosure has not yet been surveyed. Thus the current research goal is to study the correlation between ownership concentration, voluntary disclosure, and information asymmetry of companies listed in Tehran Stock Exchange. The current research is a quasi-experimental and ex post facto research which is conducted based on the actual information of stock market and financial statements of listed companies in stock exchange. Models being considered for testing the current research hypotheses are retrieved from multiple regression models of Jiang et al (2011). From all of the companies listed in Tehran Stock Exchange, 140 companies were chosen as the sample. Results showed that there was no positive correlation between the information asymmetry, the level of ownership concentration, the level of ownership concentration of financial-institutional shareholders, and management ownership. Also, there is no inverse correlation between the information asymmetry and voluntary disclosure. Voluntary disclosure affects the information asymmetry of companies having ownership concentration of financial-institutional shareholders. There is no inverse correlation between the information asymmetry, and voluntary disclosure in companies having management ownership concentration. At the end a few suggestions are provided.

For many years, the uncertainty of lie-detection systems has been one of the concerns of tax organizations. Clearly, the results of these systems must be generalized by a high value of accuracy to be acceptable by related systems to identify tax fraud. In this paper, a new method based on P300-based component has been proposed for detection of tax fraud. To this end, the test protocol is designed based on Odd-ball paradigm concealed information recognition. This test was done on 40 people and their brain signals were acquired. After prepossessing, the classic features are extracted from each single trial. After that, time–frequency (TF) transformation is applied on the sweeps and TF features are produced thereupon. Then, the best combinational feature vector is selected in order to improve classifier accuracy. Finally, guilty and innocent persons are classified by K-nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers. We found that combination of time–frequency and classic features has better ability to achieve higher amount of accuracy to identify the unrealistic tax returns. The obtained results show that the proposed method can detect deception by the accuracy of 91% which is better than other previously reported methods. This study, for the first time, succeeded in presenting a novel method for identifying unrealistic tax returns through EEG signal processing, which has significantly improved the yield of this study compared to the previous literature.