فهرست مطالب mohammadhossein vadiei nowghabi

In an expansive test of nations over distinctive geographic districts and over a long period of time, we discover restricted nation and variable-specific impacts of central bank straightforwardness on figure exactness and their scattering among an expansive set of proficient figures of monetary and macroeconomic factors. More communication indeed increments estimate mistakes and scattering. So the aim at this study is a systematic review of central bank transparency and capital market reaction. This study was conducted using a systematic review. At first all papers (n=165) related to keywords (capital market reaction and central bank transparency) searched for Persian and English databases; Google, Google scholar, PubMed covering the period from 2012 through 2022 was performed. Then all qualitative and quantitative papers related to central bank transparency selected and analyzed (n=28). As a result to inclusion and exclusion criteria, papers related to central bank transparency were found and analyzed (n=28). Predefined inclusion and exclusion criteria were: papers related to central bank transparency, papers were Persian and English, types of papers were original and all the papers were free full text. In the initial search, 165 papers were found that after reviewing the titles and abstract articles and removing repetitive and non-related, 54 possible related articles were investigated. Of these, 31 papers were omitted from the abstract because of lack of access to the original article and lack of sufficient information. Finally, 28 papers were included in the study.There are very few studies that have examined the effect of central bank transparency on capital market turmoil. The effect of central bank transpar-ency on the domestic capital market has not been studied. There is no agreement in the literature on theoretical discussions on whether increasing central bank transparency affects capital market turmoil. In foreign research, we can also refer to the study of Dinser and Eichngreen (2007) who have studied this effect empirically using the main transparency index. The pre-sent systematic review shows that almost whole of papers on central bank transparency and capital market reaction highlighted similar findings con-cerning monetary policy decisions.

Company growth and profitability forecasts are important inputs in the valuation process. Also, mean reversion estimates can serve as inputs in estimating steady-state final value parameters. The main purpose of this study is to test the hypothesis that life cycle-based mean reversion models provide better results for forecasting profitability and growth compared to the industry-level and economy-wide models. This study also tests the hypothesis that managers realize the benefits of industry and life cycle analysis when making their predictions. Totally, this study compares the variables and factors affecting the accuracy of predictions from mean reversion life cycle-based models with industry-level and economy-wide models.  

The data of 161 companies listed in the Tehran Stock Exchange, TSE, in a 10-year period of 2008-2018 were collected from the software, financial statements, and the TSE official website. To test the research hypotheses, we used statistical tests such as t- student, multivariate regression using SPSS software, econometrics estimation using Eviews. The Dickinson (2011) model was used to determine the different stages of the companies' life cycle, which is consistent with the pattern of cash flows (operating activities, investment, and financing).  

Test results of the first hypothesis, in most cases, provided evidence that growth and profitability forecasts derived from industry-level mean reversion models outperform the forecasts of the life cycle and the economy-wide models. By comparing the mean errors in the second hypothesis, the findings of the model are more accurate than other models, indicating that managers realize the importance of the firm's life cycle when predicting profits. The results of the study of factors affecting the accuracy of the life cycle model of prediction compared to other models indicated that the improvement of life cycle growth forecasts lacks significant relationships with systematic and non-systematic risk, beta coefficient, trading volume, the ratio of institutional owners, the market-to-book ratio, and the amount and intensity of the R &D, the volume of intangible assets, and financial leverage. However, for higher profitability scales, improved life cycle forecasts correlate with firm size, assets and equipment, and abnormal (poor) corporate returns. Also, the life cycle approach works best when the percentage of institutional shareholder ownership is high and the company's uncertainty, profitability, and assets are low.  

In this study, we investigated the accuracy of a forecast model based on a firm life cycle for predicting future profitability and growth relative to economy-wide and industry-specific forecast models. In general, the results of the research indicate the relative superiority of the predictions of industry-level models over the predictions obtained from the life cycle and the economy-wide models. Although the research findings do not provide evidence of more explanatory power of the life cycle model compared to other models, they suggest that managers have realized the importance of the life cycle of the company when making profit forecasts. This research has important implications that help investors, analysts, managers, and others make better predictions when making financial decisions. Also, this study identifies the drivers of growth and profitability for companies through a low-cost and high employment strategy to achieve the most accurate forecasts.

In this paper, using a sample including 2642 observations of 2001-2016 annual data of firms listed in TSE, we provide evidence that working capital management policy bears a change following large inflows or outflows of non-operating cash that are correlated with corporate events like IPOs or financing and investing events and predicted to affect firm’s bearable risk of investment in working capital. The research findings show that, because of the ensuing increase/decrease in investment in working capital, this change is likely to cause a change in working capital accruals independent of shock to sales and, consequently, given that its effect are uncontrolled, accruals models tend to overstate or understate abnormal accruals, especially in earnings management detection tests around corporate events. The results of Monte Carlo simulations of type I error show that the examined accrual models understate (overstate) the level of estimated abnormal accruals when applied to random samples of observations with extreme negative (positive) changes in current non-operating cash flows. Since there are correlations between the non-operating cash flows and partitioning variables used in earnings management studies around corporate events, our findings suggests that it is likely that there are correlation between measurement error in the estimate of abnormal accruals proxy and portioning variables, and, as a consequence, the results of such tests may not be unbiased. The study highlights the need for a control of non-operating cash flows effects on working capital management, especially in empirical tests for detection of earnings manipulations around corporate events.

The purpose of this study was to investigation the relationship between growth type and capital structure of listed firms in Tehran Stock Exchange. For this purpose, the firms are classified in to three groups low, mix and high growth and statistical analysis was performed for each one individually. Data were extracted from 940 observation year-firm during 2005 to 2014. For data analysis and hypothesis testing, multivariate linear regression model was used. The results indicate that low growth type firms turn more toward debt financing due to increase of cost of equity. Therefore, they have high financial leverage. The high growth type firms growth due to the increasing cost of debt tend to the financing of the equity. So, they have low financial leverage. Mix growth Firms have average financial leverage ratios.