جستجوی مقالات مرتبط با کلیدواژه "information content" در نشریات گروه "مالی"

Investing in the stock market undoubtedly is a vital part of the economy of the whole country, and the national economy is heavily influenced by stock market performance. Therefore, it is very important to study and identify the factors affecting stock returns for managers and investors. This study was conducted aimed to examine the information content of announcing the news on the coronavirus disease (COVID-19) outbreak in the Tehran's stock market. This study is considered as an applied research in terms of purpose and event study in terms of nature. In this study, the data was collected through the CODAL and TSETMC websites and the statistical sample includes all companies listed in the Tehran Stock Exchange in 2019. Temporal scope of the research was a week before and after the announcement of the coronavirus disease (COVID-19) outbreak in the world and a week before and after announcing the news on the coronavirus disease (COVID-19) outbreak in Iran. Sampling was done systematically and the sample size for two time periods was considered 407 and 375 companies, respectively. Data were analyzed by Means-test. According to the results, the announcing the news on the coronavirus disease (COVID-19) outbreak in the world and Iran did not have abnormal returns among sample companies, nor was there a significant difference between the returns of large and small companies.

Purpose of the present study is to understand the relationship between investors sentiment behavior and financial reporting quality with the information content of financial statements of listed companies in Tehran Stock Exchange. The data of 110 companies were collected from the databases of the Iranian Stock Exchange during the years 2009-2019 and analyzed in three stages using ANOVA- FRIEDMAN- OLS methods. In this study, the criteria for measuring the quality of financial reporting are the method of Jones, Dechow and Ball.From the OLS test results, it can be concluded that the information content determination coefficient has the highest value when the investors sentiment behavior is high and the financial reporting quality of the Jones model is average and it can also be stated that according to the test results ANOVA only in the model of financial reporting quality of Jones has the sentiment behavior of investors and the quality of financial reporting has an increasing impact on information content. The results of Friedman test also showed that there was a significant difference between different groups of investors sentiment behavior and financial reporting quality of Jones, Ball and Dechow.

Open limit order book can be used as a tool to enhance transparency and price discovery in financial markets by showing the offered volumes and prices of buy and sell orders. In this study, we aim to answer two questions by examining the limit order book on  Tehran Stock Exchange: 1) Does the information in the limit order book have the predictive power for stock price movements in the short-term?; 2) Can increasing the public disclosure of the limit order book from three to five levels enhance price discovery?

For this purpose, three different regressions areused, each of which measures the volume and price information of the two sides of supply and demand in a different way and examines the ability to predict short-term behavior of stock price by this information.

The results show that the limit order book contains information about future short-term returns. Our results also indicate that increasing the public disclosure of the limit order book from three to five levels can enhance transparency and price discovery but it causes the marginal predictive power of additional levels to decrease.

Financial distress prediction is always a concern for shareholders, creditors and business unit managers. Hence, various models proposed to predict financial distress. In this research, decision tree model and linear multiple discriminant analysis model have been used to predict financial distress of companies. In addition, the results of these two models are compared with each other. In this regard, it has been tried to use intellectual capital and value-based performance in addition to conventional financial ratios in predicting financial distress. To achieve the objectives, three hypotheses formulated. The purpose of the research hypotheses is to investigate the information content of intellectual capital and performance indicators and financial ratios in predicting financial distress in Tehran Stock Exchange companies using decision tree models, linear multiple discriminant analysis and the combined model derived from factor analysis - linear multiple discriminant analysis and the factor analysis is the decision tree. To test these hypotheses, a sample selected from Tehran Stock Exchange companies during the years 2010-2010. The results indicate that the prediction of financial distress is possible using a decision tree, but in the decision tree model, intellectual and functional capital indicators do not have information content to predict financial distress. The linear multiple discriminant analysis model has been successful in combining financial ratios and performance indicators in the prediction of financial distress. Comparison of the capabilities of the two models indicated that although the accuracy of predictive analysis of the linear multiple discriminant analysis based on the assessment criteria (competing table) and the level below the rock curve (0.920) was higher than the decision tree (0.901), but based on the t-test, This difference is not significant (p = 0.207). In other words, the results of the two methods are very close.

Using high frequency data from 33 member companies of the TEFIX30 (about 5 million data), this study examined the information content of limit order book in Tehran Stock Exchange. We try to find that does limit orders behind the best bid and offer provide useful information about stock value? And how much is their information share? We use two well-known methods based on a vector error correction model. Results show that based on Hasbrouck (Gonzalo-Granger) measure, contribution of steps 2 to 10 to price discovery is about 18% (25%) and also contribution of steps 4 to 10 that are not publicly visible is about 8% (10%).

Management forecasts about future earnings have valuable potentials that can help investors to make efficient decisions. The value of these forecasts related to precision and believability of them. With a sample of 176 companies listed in Tehran securities exchange, we exam the Information content of management earnings forecasts, repeat of bias in forecasts and factors affecting management earnings forecasts error. Results indicate that management’s earnings forecasts have information content, and the precision of current year forecasts related to market-adjusted stock return cumulated over the seven trading days around the forecast release date. Investors are more responsive to new management forecast news when past management forecasts met. Other results indicate that managers who have high return on assets, appear to over-extrapolate past performance in forecasting future earnings. We also find that that working capital accrual, forecast error in current year and institutional investor related to management’s earnings forecasts error of future year.