 This paper investigated the predictability of four different stock market groups over a period of 10 years. It evaluated various machine learning algorithms such as decision tree, bagging, random forest, eta boost, gradient boosting, extreme gradient boosting, XG boost, artificial neural networks, ANN, recurrent neural networks, RNN, and long short-term memory, LSTM. The results showed that LSTM was the most accurate algorithm for predicting the stock market group's values. Additionally, it found that eta boost, gradient boosting, and XG boost had similar accuracy levels. This article was authored by M. Navipour, P. Nyeri, H. Jabbani, and others.