The impact of the breakdown of bad news indicators on conditional conservatism in accruals-based models

One of the new issues in the field of conservatism is to examine the impact of bad news on conditional conservatism. Separating bad news indicators gives new insights on accruals and improves accrual models. The purpose of this study is to investigate the effect of separation of these indices on conditional conservatism in accrual-based models in comparison with the effect of general indices on these modelss. For this purpose, data of 144 companies were collected from Tehran Stock Exchange for a period of 11 years from 1385 to 1395. And relationships between research variables using panel data and The generalized least squares method is analyzed. In this study, the results of Alan et al. (2013), Ball and Shivakumar (2006) and Byzalov and Basu (2016) models were compared. The results of the comparison showed that separating bad news indices improves accrual-based models to predict the relationship of these indices with conditional conservatism. Also, other findings of this study based on the results of Basilo and Basu model showed that sales decrease, decrease in number of employees and cash flows this year have a direct and significant relationship with conditional conservatism, while decrease of cash flows of the previous year and the next year did not have a significant relationship with conditional conservatism.