University of TehranAccounting and Auditing Review2645-802016220091122Comparing the Power of Cash Flows and Accruals
in Predicting Future Cash FlowsComparing the Power of Cash Flows and Accruals
in Predicting Future Cash Flows19972FAHeidar MirfakhraddiniMahmoud MoeinaddinAlireza EbrahimpourJournal Article19700101Prediction is an important part of the decision-making process, because decision making reflects what will happen in the future. In economic decision making, financial prediction is a important activity. Cash flow prediction is required in various economic decisions, because the cash flows are the basis for dividends, interest payments and repayment of debt. With respect to theoretical literature and conducted researches, three regression models are constructed namely earnings, cash flows, accrual components and cash flows models for predicting future cash flows. The empirical results show that past earnings, cash flows, cash flow and accrual components of earnings can be used to predict future cash flows of Tehran listed companies. But there are not different predictive powers between three prediction models. Furthermore, additional year lags of accounting data can improve the predictive power of the model.Prediction is an important part of the decision-making process, because decision making reflects what will happen in the future. In economic decision making, financial prediction is a important activity. Cash flow prediction is required in various economic decisions, because the cash flows are the basis for dividends, interest payments and repayment of debt. With respect to theoretical literature and conducted researches, three regression models are constructed namely earnings, cash flows, accrual components and cash flows models for predicting future cash flows. The empirical results show that past earnings, cash flows, cash flow and accrual components of earnings can be used to predict future cash flows of Tehran listed companies. But there are not different predictive powers between three prediction models. Furthermore, additional year lags of accounting data can improve the predictive power of the model.https://acctgrev.ut.ac.ir/article_19972_1454c3c214f95d9c61df9da185ba59aa.pdf