علی خوزین

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be.

Investor expectations future economic processes are among the crucial factors affecting their decisions. The expectations seem to play a specific role since they are unsupervised variables capable of forming observable economic phenomena. Psychological factors influence investor expectations and corporate market value. Investor sentiments was modelled with an emphasis on psychological factors based on the Grounded Theory (GT). This applied and mixed-methods at its first and second stages. The statistical population comprised 13 experts, senior managers of investment companies, and university professors. The participants were selected through purposive and snowball sampling and the process was continued until theoretical saturation. The data were collected via semi-structured interviews coded via Atlas.ta.8 software. The research data were analysed using an open coding method. The results of the research were presented in 46 categories and 6 key dimensions.

The present study aimed to identify the most effective causes of conflict of interest by examination of accounting literature and expert consensus. Understanding these factors, using cognitive psychology theories, can lead to a model for reducing conflict of interests. The dignity of the audit profession depends on fair and proper professional judgment by auditors, and achieving this requires identification and controlling of the key factors affecting judgment and decision-making. When auditors intentionally or unintentionally accredit financial statements in line with the opinion of their employers, public interests and the auditing profession are at serious risk. Several factors that can categorize into seven categories of structure, community, culture, environment, personality, audit firm characteristic, and ethics and behavior are rooted in a conflict of interests. However, no comprehensive research examining all the above factors and identifying the most effective ones has been done so far. By reviewing the research literature, major and minor factors were identified in domestic and foreign sources. Ten expert auditors were selected by the snowball method and interviewed. The considered major and minor factors were selected from among the introduced factors, and a questionnaire was sent to the experts using the Fuzzy Delphi (Screening) method. The results of the above statistical analysis identified eighteen of the most prominent sub-criteria of the factors affecting conflict of interests and identified structural factors the highest rank in this classification, which was agreed by the experts.