摘要：Investing in stocks of the Stock Exchange is one of the lucrative options in the capital market. Stocks, on the one hand lead to the widespread participation of people in ownership, and on the other hand will achieve government’s anti-inflationary goals by attracting liquidity and guiding them in productive and beneficial economic activities. Since the forecast is mainly used to reduce risk and increase profits, the accuracy in prediction is a very important issue. Therefore, the aim of the present study is to evaluate the efficiency of machine learning algorithms in the forecasting of Tehran’s Stock Exchange index. This study, in terms of its purposes, is an applied research study, with a correlational design and uses library research to gather data from Tehran’s Stock Exchange organization through the Rahavard Novin software. The population and statistical samples of the study, is the Tehran Stock Exchange organization during 1385 to 1393. We forecasted Tehran Stock Exchange index using three models of decision- maker tree, Rough Set and logistic regression that are the subset are machine learning algorithm, and using Rosetta and Weka software. Subsequently, we compared the values predicted by these models with the actual values using the paired sample t-test in SPSS software; and finally we compared the superior performance of models using the ANOVA test. Since the hypothesis is confirmed in the study, the machine learning algorithm can be used as a reliable method to predict the Tehran Stock Exchange index
