The Impact Prediction of Income Tax Standards on Company Performance: A Hybrid Spatial Artificial Intelligence Approach

The objective of this study was to predict the impact of income tax accounting standards on the financial performance of listed companies in selected countries including Iran, Turkey, Iraq, and Gulf Council member states using a combined approach of artificial intelligence and spatial econometrics over the period ۱۹۹۰ to ۲۰۲۳. In this study, various artificial intelligence methods such as artificial neural networks, support vector machines, deep learning, decision trees, random forests, and genetic algorithms were used in combination with spatial modeling. The results show that income tax accounting standards have a significant impact on the financial performance of companies, and the combination of artificial intelligence methods with spatial modeling significantly increases the prediction accuracy. Among the different methods, deep learning combined with spatial modeling showed the best performance. These results highlight the importance of considering spatial dependencies in financial and accounting analyses in the study region, and can be valuable for policy makers, corporate managers, and investors.

کلیدواژه ها:

Income Tax Standards ، Corporate financial performance ، Artificial intelligence ، Spatial Econometrics ، Deep Learning ، AI-Spatial Models Integration