جستجوی مقالات مرتبط با کلیدواژه "earnings per share" در نشریات گروه "مالی"

Earnings and earnings per share information are metrics that are considered important by many users; Therefore, companies try to attract investors with the most accurate forecast of earnings per share. On the other hand, despite the various methods of forecasting earnings, accurate forecasting of earnings per share in the financial field is not easy and most researchers are trying to determine the best way to forecast earnings; Therefore, the main purpose of this study is to investigate the accuracy of support vector machine based on genetic algorithm compared to common linear methods in forecasting earnings per share. For this purpose, samples consisting of 100 companies listed on the Tehran Stock Exchange during the years 2008-2019 have been studied. In order to achieve the objectives of the research, first by studying previous research in the field of earnings forecasting, 14 financial ratios affecting earnings forecasting have been selected. Then, in order to provide a model for predicting the profitability of companies, a combined model of support vector machine based on genetic algorithm, support vector machine and linear regression is compared. The results showed that the hybrid model of support vector based on genetic algorithm is much better in predicting the trend of earnings per share and has a higher accuracy compared to the model of support vector based on kernel functions and linear regression method. Thus, with the development of the support vector machine model based on the genetic algorithm, the model training error is reduced to 0.036 and the accuracy of the model is increased up to 75%.

This study will provide a new approach to cost behavior analysis. Also, by completing past models, examine the behavior of costs when sales decrease (cost stickiness) and sales increase (cost positive signal). In addition, in this study, costs are categorized in terms of function (COGS and SG&A) and nature (payroll, service and depreciation expenses), and examined the impact of them on the earnings forecast accuracy. Fixed effect data model and OLS regression were applied to test the research hypotheses. The sample of the research is 95 companies listed in Tehran Stock Exchange, during 2014-2019. The results show when sales decrease there is a negative and significant relationship between the stickiness of the total cost, COGS and services costs with earnings forecast accuracy. However, no significant relationship was found between the stickiness of operating and depreciation costs and earnings forecast accuracy. Additionally, the results show when sales increase there is a positive and significant relationship between the positive signal of COGS with earnings forecast accuracy. On the other hand, there is no significant relationship between the positive signal of total costs, SG&A, payrolls, services and depreciation costs with earnings forecast accuracy. When sales decrease, the behavior of total costs, COGS and services cause to reduce earnings forecast accuracy and when sales increase, the positive signal of COGS cause to reduce earnings forecast accuracy. Therefore managers and financial analysts need to carefully consider the behavior of them and apply cost stickiness and positive signals in their forecast in order to improve earnings forecast accuracy.