The goal of this study is to appraise the ability of two variables, Operating cash flows and accruals in predicting Operating cash flows and Operating Profit with time lags. The study period is from 2005 to 2010.The regression results for hypothesizes show that there is a significant relationship between future Operating cash flows and cash component of Operating Profit, and current year shows the highest relationship in relation to all the lags. Also, the results reveal that, there is a significant relationship between future Operating cash flows and accrual component of Operating Profit, and two years lags show the highest relationship in relation to other lags and current year. Furthermore, The results indicate that there is a significant relationship between future Operating Profit and, cash and accrual components, and four years lags show the highest relationship in relation to other time lags and current year. So, recording to this research, the best predicting of Operating Profit and Operating Cash Flows will be received on these time lags.

Earnings per Share (Eps) is an important factor in estimating the stock price of thr company……, in Gordon Model, the value of companys is a founction of dividend. Here we have investigated the comparison between the power of Profit and Operating cash flow in earnings Per Share of the company. We took the Time series of 2010-2015anda number of 89 companies from amongst the listeed companies of the Tehran Stock Exchange. We also selected some effectine factors on dividevt, including the size of the company, debt ratio, current assets and last year dividend, as the control variables. The data structure is an integration of 527 expriments. Normal regression and extended regression showed that Operating cash flow does not have the capacity of estimate the EPS and the results indicate that there is no significant relationship between the sizeof the company, Operating cash flow, current assete and dividend but there is a meaningful linear relationship between earmings Per share, debt ratio, last year and current year dividends.

One of the most common criterion based on value which has been considered in recent years, is called EVA. This study has analyzed the ability of EVA to present information related to the firm's value and tries to compare it with some other criterion such as operational Profit and not Profit. In fact this paper is aimed to determine the correlation coefficient between the economic values added, net Profit and Operating firm's market values in Tehran stock exchange.This research is an empirical one, which investigates the relation between variables by using regression method and Pearson's correlation coefficient. The methodology of this research is past event study.Population of this study includes all Iranian firms listed in Tehran stock exchange, except investing and financial firms. We use the data of 77 firms over the period 1380-1386 selected from Tehran stock exchange. The results of this study has shown that although the coordination of EVA with market value is remarkable, but compared with operational and net Profit, it is less capable.

The main purpose of this paper is to examine the relationship between financing variables, the ratio of research and development costs to Operating Profit and financial crisis in companies admitted to the Tehran Stock Exchange. The statistical population of the research is accepted by the companies in the 5 years period from 1391 to 1395 in the Tehran Stock Exchange and the research samples are from 181 companies. The research data were compiled through the new software and financial statements of the companies. In this research, multivariate linear regression model was used to test the hypotheses. The statistical method used in this paper is the panel data method. The results of the research indicate that the ratio of financial leverage to the ratio of R & D investment to Operating Profit is also affected by the ratio of R & D to Operating Profit on the criteria of the corporate financial crisis based on Springerty model, and the ratio of the cost of research and Operational development is effective on the financial crisis criteria of the company based on the Zimsky model, as well as the impact of the cost of research and development on the Operating Profit on the financial crisis criteria of the company based on the Altman model of 1983 and 1986 and the modified Kurdistan model was not approved.

The agency theory from the economic perspective indicates that a rational economic man is after self-interest maximization. Therefore, to control the opportunistic behavior of the agent, independent directors should be appointed to the board. However, stewardship theory by considering the high-level needs of Maslow’, s hierarchy of needs and organizational psychology suggests that trust in managers leads to better performance. Since human behavior is influenced by variety of needs. The purpose of this study is to investigate the effect of board independence on financial performance by developing a theory of a combination of the above two theories. Balanced panel data of 124 corporations from the population of companies listed on the Tehran Stock Exchange during the years 2011-2019 is collected. The test of hypotheses is conducted by nonlinear feasible generalized least squares regression method with the help of software Stata done. According to the findings of the study, no significant effect is found between board independence and operational cash flows. However, there is a quadratic significant effect with a downward concussion between the number of non-executive directors and net income. It can be stated that combining the economic perspective of agency theory and the psychological perspective of stewardship theory on board independence has a positive effect on firm Profitability. In other words, the research findings confirm that the effectiveness of board consists of executive managers and the majority of non-executive members. Therefore, this study suggests a level of board independence that improves corporate Profitability.

Accounting information provides a system through which intraorganizational information is transferred to capital markets and allows more control of management activities by investors. Information provided by financial reports can be useful in decision making when it is reliable and relevant. The statistical population of this study is all firms listed in Tehran Stock Exchange and the sample selected by systematic removal method, consists of 105 firms listed during the period from 2007 to 2016. To test the variables relation, the regression analysis method based on panel data model is used. Results show that the quality of accruals, Profit sustainability, Profit forecast and Profit smoothing are effective on Operating cash flow.

The purpose of this study is to investigate the ability of past earnings, compared to past cash flows, to predict future cash flows. A sample of 69 companies was selected, during the years 2004 to 2017, and regression models were applied to analyze the data, using cross-sectional data and time series analysis. Also, in this study, Markov random methods are used to predict future cash flows. For this purpose, transfer probability matrices were formed and after examining the basic assumptions of the model, the transfer process was estimated and the findings were extracted. The results of the study show that in predicting future Operating cash flows, the ability to explain past Operating cash flows has been greater than past Operating Profits. Findings in this method also indicate the superiority of the cash flow forecasting power over Operating Profit and loss. Other results show that although accruals, as well as their breakdown into smaller components, lack the predictive power of future cash flows, by adding models from past cash flows, they acquire the power of prediction. In short, the findings indicate the prominent role of cash flow, compared to Profit and loss, in predicting future cash flows.

The present study compared the predictive performance of machine-learning models and statistical models for forecasting Profit and operational cash flow by using a combination of accrual and cash variables. The research method encompassed 3 main stages: data set and variable selection, modeling, and estimation. The study focused on companies listed on the Tehran Stock Exchange (TSE), analyzing data from 184 companies over the period of 2012-2021. The findings indicated that accrual variables exhibited greater explanatory power than cash variables in predicting net Profit and future Operating cash flow. Furthermore, the comparison of machine-learning and statistical models for forecasting net Profit and future Operating cash flow revealed that the artificial intelligence approach exhibited superior capability. Specifically, symbolic regression among the machine-learning models and the probit model among the statistical models demonstrated higher performance. Additionally, the results indicated that certain statistical models outperformed some machine-learning models while, on average, machine-learning models outperformed statistical models.Keywords: Classification, Data Mining, Machine Learning, Net Profit Forecasting, Operating Cash Flow Forecasting. IntroductionIn the current intensely competitive business environment, precise prediction of financial outcomes has emerged as a pivotal element in organizational triumph. Projecting crucial financial indicators, such as net Profit and Operating cash flows, equips businesses with the insight needed to make well-informed choices regarding investment strategies, resource distribution, and comprehensive financial strategizing. The capacity to anticipate future financial performance enables organizations to streamline operations and mitigate risks. Consequently, there is an escalating need for effective forecasting models.This study had two primary objectives: firstly, assessing the predictive capability of accrual and cash variables for forecasting Profit and future cash flows and secondly, comparing the efficacy of statistical models and machine-learning models in predicting net Profit and Operating cash flows. Statistical models seek to scrutinize historical data patterns and underlying relationships to anticipate future financial outcomes. Conversely, machine-learning models have emerged as a potent alternative, employing advanced computational techniques to glean insights from data and make predictions without explicit programming. This research was guided by four hypotheses:First hypothesis: The predictive capability of accrual ‎variables for future net Profit significantly exceeds that of cash variables.‎Second hypothesis: The predictive capacity of accrual ‎variables for future operational cash flow significantly surpasses that of cash variables.‎Third hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting net Profit.‎Fourth hypothesis: Machine-learning models outperform statistical ‎models significantly in predicting operational cash flows.‎ Materials & MethodsThis study utilized the Bourseview software database, Rahavard Novin, and the Codal website for analyzing and drawing conclusions regarding the hypotheses. Additionally, data-mining software, such as Weka, SPM, RapidMiner, SPSS Modeler, and Eureqa, were employed for modeling, while Stata econometric and statistical software was used for the Vuong test, EViews for descriptive statistics, SPSS for mean comparison test, and Excel for data sorting and categorization. Following the application of these specified tools, 184 companies listed on the Tehran Stock Exchange (TSE) were examined. Initially, the study investigated the ability to explain each category of cash and accrual variables for net Profit and future Operating cash flow through special regression estimation of panel data and the Vuong test. Subsequently, the superior model was utilized for modeling and the average performance of the machine-learning models was compared with that of statistical models. FindingsThe significance of Vuong statistic in predicting net Profit at a 1% significance level suggested a notable difference in the explanatory power of the two models with the model of accrual variables demonstrating higher explanatory power than that of the cash flow statement variables. Conversely, the non-significance of the Vuong statistic at the 5% significance level for predicting operational cash flow indicated no significant difference in the explanatory power of the two models. The performance results of both statistical and machine-learning models indicated that the symbolic regression classifier, utilizing the genetic algorithm to predict net Profit, exhibited the best overall performance and provided valuable results in the longitudinal test sample. Following symbolic regression, the linear support vector machine and MARS ranked second and third, respectively, in overall performance. Similarly, the symbolic regression classifier, employing the genetic algorithm to predict Operating cash flow, demonstrated the best overall performance in the longitudinal test samples. After symbolic regression, the deep learning classifier and MARS ranked second and third, respectively, in overall performance. Discussion & ConclusionsIn accordance with testing of the first and second hypotheses of the research, which posited that accrual variables have a greater explanatory capacity for net Profit and future Operating cash flow compared to cash variables, the coefficients of determination of the models were compared after estimating the appropriate panel data approach. The investigation results indicated that accrual variables indeed possessed greater explanatory power for net Profit, thus providing no grounds for rejecting the first hypothesis of the study. However, in the case of Operating cash flow, while the explanatory value of accrual variables surpassed that of cash variables, there was no statistically significant difference in the explanation between accrual and cash variables. Consequently, the second hypothesis of the research was rejected. In accordance with testing of the third and fourth hypotheses of the current study, which posited that machine-learning models outperform statistical models in predicting net Profit and Operating cash flow, the AUC criterion was derived through the implementation of both statistical and machine-learning models. By comparing the success rates of the statistical and machine-learning models, it was observed that the machine-learning models significantly outperformed statistical models in predicting net Profit and operational cash flow. Therefore, there was no basis for rejecting the third and fourth hypotheses of the study.

This paper studies the relation between earning volatility of firms and the future Operating income predictability to define the effect of Operating income volatility on investors’ expectations concerning future incomes. In other words, is there any possibility to predict future income using current volatility incomes? Logically it seems the present trend of companies will continue in future.For this purpose we considered the data of 120 companies which were admitted in Tehran Security Exchange Organization between 2003-2008. The examination was made based on regression analysis.The results indicate the meaningful and reverse relation between current volatility incomes and future Operating income predictability. It also signifies that Operating income predictability will increase In consequence of deducting accrual items from Operating Profits.