Assessing the role of company life cycle in optimizing accruals quality predictive models

The main purpose of this study is to evaluate the role of company life cycle in optimization of accrual quality prediction models. The statistical population of the study is 2008-2019, which includes a random sample of 180 company observations that were divided into three stages of life cycle using Dickinson's (2011) model variables. Multivariate regression technique with cross-sectional data was used to test the hypotheses. Then, using the original accrual quality measurement models, the error values of each model were compared with the error values obtained from the life cycle adjusted models. The results show that the coefficients of determination in the modified Casink model are not significantly increased compared to the original model, but in the other models, the coefficient of determination increases significantly compared to the original model, indicating that the values estimated by the modified models are approx. It is appropriate to real values and predicts and identifies up to a few percent of accruals quality or cash flow difference and net profit compared to the original and prior models, so that in addition to the Casink model, in addition to the life cycle, it increases the predictive power models have been made.