Modeling of Minimizing Uncertainty Based on Accounting Data Quality Proxies

According to the information perspectives of accounting, the main function of accounting is to provide information and to reduce uncertainty. Therefore, accounting capacity of reducing uncertainty determines the accounting quality (AQ) and so, we are attempting to find the best combinations of AQ Proxies whose trading off can reduce uncertainty. The purpose of this paper is to explore how AQ Proxies could reduce uncertainty. Tree analysis (structures of if and then) is employed since this analysis could empirically address both the trade-off of Proxies and the importance of each measure in reducing uncertainty. Also, traditional statistics tests are employed. The Decision Tree analysis creates a tree-based classification model. Our findings suggest that there are at least three interaction paths through which accounting quality Proxies could reduce uncertainty. In contrast to previous researches which have found it difficult to investigate the trade-offs of AQ Proxies; this paper shows that it is possible to address it and in this way, it could prepare a number of rules of thumb to reduce uncertainty through AQ Proxies. These findings have different implications for policy makers, audit committees, and investors.