درخت حوزه‌های تخصصی

The emergence of a new theory of"macroaccounting" with a new wave of accounting research over the last decade tries to explain and use the Aggregate information of interim accounting statements in economic forecasts. Macroaccounting theory suggests that economists and macroeconomic forecasters use Aggregate accounting information at the macroeconomic level. For example, accounting earning is used to predict GDP, cost stickiness is used to predict unemployment, and the ratio of book value to market value is used to predict inflation. Earnings growth dispersion contains information about trends in labor reallocation, unemployment change, and, ultimately, aggregate output. initial macroeconomic estimates released by the Central bank of Islamic Republic of Iran and Planning and Budget Organization and Statistical Center of Iran do not fully incorporate this information. Accordingly, the present study, based on macroaccounting theory, has examined the Predicting Restatements in Macroeconomic Indicators using Accounting Information. The population of this study includes all companies listed in the TSE. Due to the seasonality of the data and the fit of the models in a time series, the observations reach 40 times (2008:1to2018:4). The research method is based on time series data, VARtechnique. the The results suggest that earnings growth dispersion provides related data about final GDP growth. The results suggest that after considering the effect of other influential factors, specifically real initial GDP, earnings growth dispersion is useful in forecasting future GDP changes. The findings are important for economists and policymakers to have more accurate economic estimation and prediction by applying for accounting Information.

In the recent years, intangible assets and corporate brand have found a significant importance for corporates. These assets are accounted as essential for value making and even stabilizing the continuation of corporate trend. The purpose of the present study is to investigate the effect of profitability and operating cash flow on trademarks value in accepted corporate in Tehran stock exchange. The data were obtained from a sample of 76 different corporates from 2010 to 2015. The data were analysed using the least regular squares regression and integrated method. Considering the lack reassessment of assets in corporates and the effect of financial statement articles, to avoid the probable false distance between market value and stock book value per share (as assessment index of trademark) stock book value per share has been modified using annual inflation rate. Findings, both before and after modification of commercial values by annual inflation rate showed there was a positive and significant relationship between profitability and operating cash flow and trademarks value in the corporates studied.

The phenomenon of the financial crisis is not a new phenomenon around the world. The structure of an economy or a set of economies may face a financial crisis. In fact, it is deformation, dimensions, causes and roots of the financial crisis which create a state of change from this crisis. Recently, evidence has shown that the market value of firms subject to bankruptcy is significantly reduced. In this study, the impact of the financial crisis on conservative accounting and transparency of banks in Iran has been studied. After designing the transparency indicators, the transaction information was collected from the Stock Exchange in the five-year period of 2011-2015. The statistical sample consists of 18 banks selected by systematic elimination method, which in total were 90 years-bank. In this research, linear regression and correlation were used to investigate the hypotheses of the research. Eviews software was used to analyze the data and test the hypotheses. What can be said in the overall conclusion of testing the hypotheses is that the financial crisis affects the conservative accounting and transparency of banks.

Behavioral financial knowledge deals with the behavior of investors and other users in the capital market. According to the financial knowledge, it is no longer expected that only factors such as accounting information and macroeconomic variables will affect decision-making but also a variety of behavioral variables including manager’s optimism, information influence management, patience of major shareholders and other investors' biases can have an impact on the prices and stock returns. The purpose of this study was to investigate the impact of managers' optimism on the relationship between the patience of major shareholder and information influence management. This study attempts to investigate the relationship between these behaviors and information influence management by measuring the degree of major shareholders patience and managers’ optimism. This is a descriptive post-event research. The study population consisted of the companies listed on Tehran Stock Exchange (n = 87) between the years 2008 and 2017. Multivariate regression was used to test the hypotheses. The findings showed no significant relationship between the major shareholders patience and the information influence management (high, low, balanced). Further, it was found that managers’ optimism has a significant effect on the relationship between the major shareholders' patience and the information influence management (balanced level) and no significant relationship was observed between two other levels.

Stock market forecasting has attracted so many researchers and investors that many studies have been done in this field. These studies have led to the development of many predictive methods, the most widely used of which are machine learning-based methods. In machine learning-based methods, loss function has a key role in determining the model weights. In this study a new loss function is introduced, that has some special features, making the investing in the stock market more accurate and profitable than other popular techniques. To assess its accuracy, a two-stage experiment has been designed using data of Tehran Stock market. In the first part of the experiment, we select the most accurate algorithm among some of the well-known machine learning algorithms based on artificial neural network, ANN, support vector machine, SVM. In the second stage of the experiment, the various popular loss functions are compared with the proposed one. As a result, we introduce a new neural network using a new loss function, which is trained based on genetic algorithm. This network has been shown to be more accurate than other well-known and common networks such as long short-term memory (LSTM) for both train and test data.