The Financial Accounting and Auditing Researches
Year:2013 | Volume:5 | Issue:17|Papers Count:8

The goal of this paper is examining the impact of Financial Reporting Quality (FRQ) on Dividend Policy of the companies listed on Tehran Stock Exchange. Therefore, using Francis et al. (2005) model, the Innante and Discretionary Accruals Quality has been used as an indicator for the FRQ. The research population composes of 64 companies listed on Tehran Stock Exchange which have been studied. Multivariate linear regression statistical analysis was used to test the hypotheses of the research. The results show that there is a direct significant relationship between the Innante Accruals Quality and the Dividend Payout, but there is not any significant relationship between the Discretionary Accruals Quality and the Dividend Payout. In addition, the results of research concerning control variables show that there is a reverse relationship between Q-Tobin's ratios and operating expenses to sales ratios and the Dividend Payout, but there is not any significant relationship between the other control variables (i.e., firm size and Return on Equity) and the Dividend Payout.

Rapid technological advances and vast environmental changes, leading to increasing competition and limit access to benefits and likely to suffer from financial crisis has increased. Purpose of this study is investigating financial crisis prediction strength of different artificial intelligence techniques (linear and nonlinear genetic algorithm and neural network). Based on available information and statistics, of all companies listed in Tehran Stock Exchange, 72 companies have been subject to Article 141 trade law and 72 companies have not been subject to this Article was elected.Results of Mc-Nemar test for genetic algorithms techniques and neural network showed that there are not significant differences between linear and nonlinear genetic algorithms with neural network. Although the predictive accuracy of nonlinear genetic algorithm (90%) and linear genetic algorithms (80%) is more than of the neural network (70%) but this difference is not statistically significant.

Investors & creditors and others needs to relevant and accurate financial information for make decisions. this research intended to study the relevant between inflation & assessment criteria's capital budgeting in listed companies in Tehran Stock Exchange among 1383 to 1388. Sampling method is simple sample method. In this research selected 30 companies. Results of research show that countries with inflation economic when companies applies cost method accounting for assessment of criteria's capital budgeting is not provides useful and relevant financial information for investment decisions and decision makers has in wrong. Findings shows that for assessment of criteria's capital budgeting we must adjusted the cash in and out flows with included of inflation rate in our calculations.

This research analyzes the determinants of dividend payout ratio, using a sample of Tehran Stock Exchange companies and it also compares the predictive power of neural networks and regression model to estimate dividend payout ratio. The purpose of this research is identification and explanation of the determinants of dividend payout ratio, evaluation of the importance of these determinants, and presentation of a descriptive model of dividend payout ratio determinants. Among the different theories and ideas from researchers in the field of finance about dividend, in this study, signaling theory, agency theory and residual theory of dividend have been studied. In order to review the theories, first substituting variables were determined and then the necessary information for 133 companies was gathered during six years (83-88). The information was analyzed via statistical methods based on correlation coefficient, multivariate regression, and artificial neural networks. The results of this research indicate the existence of a significant and positive relationship between dividend payout ratio and “last year dividend payout ratio, ownership dispersion and free cash flow (FCF)”. A comparison between regression and neural network models indicating more accurate prediction of dividend payout ratio using neural network model. Also in structure (7-13-1) of neural network, a model with “learning initial=0.15 and momentum=0.9” shows that revenue growth, last year dividend payout ratio, and ownership dispersion are the most important determinants of dividend payout ratio.

The aim of this study investigated the relationship between the 52 week high of a stock price and its return under different stock market index levels. In addition In this study the relationship between short-term trend of more including 5-day high, 20-day high, and 60-day high are considered under different stock market index levels. For statistical sampling method systematically removed, the sample includes 22 companies accepted The Tehran Stock Exchange was selected and Evaluated. and In order to analyze the data and test the hypotheses Research of regression with the Combined data used is. The results test the hypotheses Research Shows the a stronger positive relationship between 52-week high and return where the stock index is greater than the 52-week average, whereas a weaker positive relationship between 52-week high and return Exists where the stock market index is below the 52-week average. Despite the positive relationship between 52-week high and return, This ratio can be used to predict future stock returns.

One of the most important tools to determine performance strengths and weaknesses, identifying opportunities and corporate external threats is company ranking. Tehran Stock Exchange try to rank based on qualitative criteria, such as timeliness of information, disclosure quality and appropriate update notification but this paper by combining AHP method and Fuzzy theory consider the uncertainty conditions by logical and practical models. Because of the importance of financial ratios are different between different groups (such as teachers, auditors and investment consultants), by using a questionnaire; weights of indicates were calculated, then by using TOPSIS model in banks in Tehran Stock Exchange (2011) were ranked. The result of research showed that the mentioned rank with the Tehran Stock Exchange ranking according to using the different ways, are not the same.

Nowaday's, one of the most important financial decision's in financial institution's is investigation, assessment and measuring customer's validity. Decision making for granting facilitie's to customer's is vital from this perspective that imprecise assessment of customer's could Past due and delayed claim's and attenuated financial granting facilities power of banks and finally resulted the unpaid claim's of the bank' s. This study has been conducted with the aim of modeling customer's validity in bank by using neural network, decision tree and support vector machine method's. For this purpose, financial and qualitative data for a random sample with 300 member's (218 good customer's and 82 bad customer's) have been selected from legal firm's that were received credit facilitie's from Iranian meli bank's of Tehran city member's in 1389 and 1390 year's, will be survey. In this study, after investigating each of customer's credit dossier's, 31 descriptive variable's were assessed and result's addition to verifying financial and economic theorie's, show that data mining method's have high efficiency for Validation of customer's. Also, performance of neural network model for prediction is much better than other model's.

This article tests and compares two models CAPM and the Reward Beta Model for the prediction of the expected return in the Tehran Stock Exchange (TSE): based on the two-step test methodology 1) estimation of the models’ parameters through time series regressions in an ex-ante sample; 2) use of the parameters as explanatory variables in a cross section regression and ex-post sample; By using Microfit4 and Excel, some relevant statistical and econometrics methods such as t-statistics, F statistics, adjusted coefficient of determination (Adj. R2), Durbin-Watson, Jarque-Bera, Functional Form, were employed for testing the research hypotheses. The results, based on a sample of 112 firms during the period 1998 to 2008, show that reward beta has a higher explanatory power in comparison with CAPM.