Application of Machine Learning and Deep Learning Algorithms to the Prediction of Stock Market Trends

The stock market's inherent volatility poses ongoing challenges for stock traders, as it is subject to a multitude of circumstances that exert influence on its behavior. This research aims to mitigate the risk associated with forecasting stock market trends through the utilization of deep learning and machine learning techniques. Eleven machine learning models were utilized in this study: random forest, logistic regression, xgboost, naive Bayes, K-nearest neighbors, decision tree and support vector classifier and extreme gradient boosting. Additionally, two powerful deep learning techniques: recurrent neural networks (RNN) and long short term memory (LSTM) were used. From the Tehran Stock Exchange, four market groups were chosen for the experimental estimates. Petroleum, non-metallic minerals, basic metals, and diversified financials are all part of these categories.