درخت حوزه‌های تخصصی

One of the basic assumptions of management accounting illustrate that costschanges has a significance Relationship with increasing and decreasing in the levelof activity, recently after being raised of sticky costs issue by Anderson and hiscolleagues this assumption was discussed. It means Increases in costs by increasingthe more activity level of reduction in costs is exchange for the reduction in thelevel of activity. Anderson et al (2003) changed the expression of Cost behavior tosticky costs. The Subject profit forecast error is one of the issues that can affectinvestment decisions to hold or transfer of shares, because purchase of shares ofthe new company's stock is riskier process than other companies; Because of itslack of trading Background, historical information related to their is low. Accordingly,in prediction two fundamental goals follow: The first one obviously is theproper planning and second one is Familiarity and deployment of predictive techniquesusing predictive techniques for decision making and problem solving process.The aim of this study was to investigate the relationship between forecasterror of earnings and sticky cost in Tehran Stock Exchange. To achieve this aim, amain hypothesis and sub-hypothesis has been proposed, and to test hypothesesdata 108 firms listed companies in Tehran Stock Exchange using systematic samplingpurposefully selected, and data of the period between 2007 to 2013 was usedfor statistical analysis. Finally, the results indicate that There is an inverse relationshipbetween sticky costs and forecast error of earnings, and also betweensticky cost of goods sold and sticky costs of sales.

Having adequate, sufficient and timely information and data is very important for investors' decision making. Processing information and allocating the asset are two fundamental tasks in the securities market and the stock price is more likely to have the nature of disclosure, information effectiveness and asset allocation efficiency. Thus, in this research, the effects of large block transactions and ownership nature on the non- financial disclosure have been investigated in the corporates accepted in Tehran Stock Exchange. Research population involves the corporates accepted in Tehran Stock Exchange for a 5-year period (2010-2014) that according to research constraints and using the systematic deletion method, the data related to 133 corporates were gathered. In this study, five main hypotheses have been suggested. To analyze the desired data and test the research hypotheses, the software of Eviews has been implemented. Results indicated that the number of large block transactions and nature of ownership are effective in non-financial information disclosure at the whole and the corporates with high transactions, those with low transactions, those in a business group and those in a diverse business group but the effect and explanatory of dependent variables by the independent ones vary at each level.

The purpose of this paper is to examine the effects of inflation and operating cycle on cash holding in Tehran Stock Exchange in the period 2010 to 2014.The sample is comprised of 103 companies which are selected by systematic elimination method and total of 515 year-firm collected. In this study for testing the hypothesis is used of linear regression and correlation. To analyse data and test hypotheses is used of Eviews software. After designing and testing hypotheses for each main hypothesis, it was concluded that inflation and operational cycle has not a significant impact on the level of firm's cash holding. The results show no significant effect of inflation through the operating cycle on cash holding.

Recently, understanding the anomalies in financial markets have severely chal-lenged the efficient market hypothesis (EMH). The price momentum is one of the anomalies described as the unexplained short-term return by Fama and French (1996). The present research strives for modeling the price momentum of winner stock in the Iranian capital market. The grounded theory method was used to explain this phenomenon. To this end, in-depth interviews were held with 32 experts operating in the professional and academic fields in 2018. The collected data was encoded in three steps, and the results were presented as a conceptual paradigm. The research findings identified the momentum causal factors in the behavioral level, the background factors in the social, macroeconomics, and mar-ket levels, the intervening factors in the global economics, macroeconomics, mar-ket, and company levels, and the strategies in the social, macroeconomics, market, the investment and finances institutions, and consequences factors in market level. The research findings suggest that the winner stock price momentum phenomenon should not be considered a speculation opportunity. Rather, it is an anomaly that has to be regulated with the proposed strategies according to the experts. The consequences of the adoption of these strategies include the stable and normal income for the market actors, the decrease in the loss inflicted on natural persons due to the market volatility, the management of anomalies, more effective attrac-tion and allocation of liquid capitals, the reduced credit risk of brokerages, and the acceleration of liquidation in the market.

Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Algorithms, has led to a lot of research in the field of finance and accounting. Accordingly, the purpose of this research is to model the Stock price crash risk of listed companies in Tehran Stock Exchange using firefly algorithm and compare the results with multivariate regression as a traditional method. Of the companies listed on the stock exchange, 101 companies have been selected as samples. Initially, 19 independent variables were introduced into the model as input property of the particle accumulation algorithm, which was considered as a feature selection method. Finally, in each of the different criteria for calculating the risk Stock price crash risk, some optimal variables were selected, then using firefly algorithm and multivariate regression, the stock price crash risk was  predicted  and results were compared. To quantify the Stock price crash risk, three criteria for negative skewness, high fluctuations and maximum sigma have been used. Two methods of MSE and MAE have been used to compare the methods. The results show that the ability of meta-meta-heuristic methods to predict the risk Stock price crash risk is not  generally higher than the traditional method of multivariate regression, And the research hypothesis was not approved.