درخت حوزه‌های تخصصی

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.

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.