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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Iranian Journal of Finance
- نوع مقاله: Journal Article
- کلمات کلیدی: Earnings Management,Financial Performance,Voluntary Disclosure,life cycle
- چکیده:
- چکیده انگلیسی: This study aimed to investigate the relationship between voluntary disclosure and earnings management and financial performance during the life cycle of the listed companies in Tehran Stock Exchange. The statistical population of the study included all listed companies in Tehran Stock Exchange since 2013-2018. In this study, earnings management, the financial performance of the companies (including return on equity, returns on assets, Tobin Q ratio, economic value-added, and refined economic value added) were the dependent variables, and the level of voluntary disclosure was the independent variable and the life cycle of the company was considered as the moderating variable. Also, in order to test the research hypotheses, a linear multivariate regression model using combined data was used. The results showed that earnings management and financial performance indicators have a significant relationship with voluntary disclosure over the life cycle. Accordingly, an increase in the level of voluntary disclosure increased the company's performance. Also, the results of the study indicated that the company's life cycle mediates the relationship between the level of voluntary disclosure and the company's performance.
- انتشار مقاله: 03-07-1399
- نویسندگان: Seyyed Mohammad Hosseini,Esfandyar Malekian
- مشاهده
- جایگاه : پژوهشی
- مجله: International Journal of Finance and Managerial Accounting
- نوع مقاله: Journal Article
- کلمات کلیدی: Artificial Neural Network,Genetic Algorithm,Cumulative motion algorithm of particles,stock price risk
- چکیده:
- چکیده انگلیسی: One of the most important methods of opacity accounting information by management is to accelerate the identification of good news versus delaying the identification of bad news on profits, but there is always a final level of accumulation of bad news in the company, and by reaching that its final level, these bad news will be released, which will lead to a Stock Price Crash Risk. In fact, stock price collapse is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. Accordingly, the first purpose of this research is to model the Stock Price Crash Risk of the listed companies at the Tehran Stock Exchange by using an optimal algorithm The cumulative particles and comparison with the results of logistic regression model. To this, a hypothesis was developed for the study of this issue and the data of 101 listed companies of Tehran Stock Exchange for the period between 2010 and 2014 were analyzed. First, 14 independent variables were introduced as inputs of the combined genetic algorithm and artificial neural network, which was considered as a feature selection method, and 7 optimal variables were selected. Then, using particle cumulative algorithm and logistic regression, predicted The Crashs. To calculate the Stock Price Crash Risk, a Stock Price Crash Period criterion has been used. In The Second Stage, the particle algorithm was used as a feature selection, and this time, to calculate the Crash risk, the NCSKEW criterion was used. Finally, the optimal variables were entered into the Ant Colony algorithm and the results were compared with the multivariable regression. In the second step, MSE and MAE were used to compare the results. The results of the research show that the particle Swarm Optimization and Ant colony are more able than traditional regression (lojestic and multivariable) to predict the Crashs. Therefore, the research hypothesises are confirmed.
- انتشار مقاله: 29-08-1396
- نویسندگان: Esfandyar Malekian,Hossein Fakhari,Jamal Ghasemi,Serveh Farzad
- مشاهده