درخت حوزه‌های تخصصی

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.

The purpose of a financial manager in dividend policy is to maximize the wealth of its owners while providing adequate funds for the company. In this research, the aim is to determine the relationship between information asymmetry, dividend policies and ownership structure. After designing information asymmetry evaluation indicators, the transaction information has been collected from the Stock Exchange in the five year period of 2011-2015. A statistical sample of 155 companies was selected using a systematic elimination method, which was a total of 775 years-firm. In this research, linear regression and correlation were used to analyze the hypotheses of the research. What can be said in the summing-up and conclusion of the general test of research hypotheses is that the company with a higher information asymmetry is less likely to pay dividends, as well as government-affiliated companies with higher information asymmetry pay more dividends compared to non-governmental corporations. Finally, the results indicated that structural reform in order to increase the transparency of information leads to a positive moderating effect on the relationship between information asymmetry and dividend policies