Document Type : Original Article


1 Department of Accounting, Urmia Branch, Islamic Azad University, Urmia, Iran

2 Associate Professor, Department of Accounting, Urmia Branch, Islamic Azad University, Urmia, Iran

3 Department of Accounting, Urmia University, Urmia, Iran



Today, choosing the right model for determining the portfolio of investment in financial assets is one of the important issues of attention of analysts and capital market activists, and investing in a portfolio consisting of mutual investment funds is the same. With this statement, the purpose of the article is to evaluate and compare net assets value (return) of Federation of Asian and European Stock Exchanges (FEAS) member countries with using support machine models in comparison with statistical models. The statistical and sample population included the data of 39 selected traded funds, members of FEAS, from 12 selected countries (including Iran) between 2014 and 2021.

The data related to the mentioned funds were classified and analyzed using spss-modeler, rapid miner, and weka software, and were tested with 24 support machine methods and 11 statistical methods and the results showed that the prediction accuracy of statistical models is lower than that of support machine models. To find out the significance of this difference, the Mann-Whitney test was used. Also the results show that at the 95% confidence level, it can be claimed that the prediction accuracy of machine learning models is higher than statistical models. The average rating of machine learning models was (20.86) which was much higher than statistical models (10.85).