Investigating the usefulness of Relief and correlation variable selection methods in predicting and identifying optimal predictor variables affecting aggressive tax policy

Empirical evidence shows that companies are trying to reduce or defer their income taxes. This study examines aggressive tax policy as a tool for tax reduction. The present study seeks to provide empirical evidence to compare the performance of linear methods such as correlation-based methods and relief with other models before reducing the variables in predicting the criteria of bold tax policy of companies listed on the Tehran Stock Exchange. The usefulness of correlation and relief methods for selecting optimal predictor variables was also evaluated and compared. For this purpose, data related to 108 companies for the period between 1389 to 1397 were reviewed and tested using the panel data method. The results indicate that there is a significant difference between estimating all research variables and using variable reduction methods. The results also show that the usefulness of Relief method in modeling the factors affecting aggressive tax policy is more than the correlation method. In addition, the results showed that the uncertainty of macroeconomic variables has a positive and significant effect and factors of audit quality, product market competition, and the characteristics of CEOs have a significant negative effect on aggressive tax policy.