Abstract :

The Nature Of Stock Market Movement Has Always Been Ambiguous For Investors Because Of Various Influential Factors. This Study Aims To Significantly Reduce The Risk Of Trend Prediction With Machine Learning And Deep Learning Algorithms. Four Stock Market Groups, Namely Diversified Financials, Petroleum, Non-metallic Minerals And Basic Metals From Tehran Stock Exchange, Are Chosen For Experimental Evaluations. This Study Compares Nine Machine Learning Models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), EXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression And Artificial Neural Network (ANN)) And Two Powerful Deep Learning Methods (Recurrent Neural Network (RNN) And Long Short-term Memory (LSTM). Ten Technical Indicators From Ten Years Of Historical Data Are Our Input Values, And Two Ways Are Supposed For Employing Them. Firstly, Calculating The Indicators By Stock Trading Values As Continuous Data, And Secondly Converting Indicators To Binary Data Before Using. Each Prediction Model Is Evaluated By Three Metrics Based On The Input Ways. The Evaluation Results Indicate That For The Continuous Data, RNN And LSTM Outperform Other Prediction Models With A Considerable Difference. Also, Results Show That In The Binary Data Evaluation, Those Deep Learning Methods Are The Best; However, The Difference Becomes Less Because Of The Noticeable Improvement Of Models’ Performance In The Second Way. As Extension We Are Added LSTM, Linear Regression , Lasso Regression, Ridge Regressor, Xgboost, Voting Regression, Decision Tree, Random Forest, SVM, Stacking Regression, Adaboost, SGDregressor, Adaboost, Catboost, LightBoost, Voting Regression-[ Catboost , Lightboost ], Stacking Regressor - [ Catboost , Lightboost ] Are Used And Algorithms All Are Giving High Prediction Accuracies Compare To Existing Ones. Index Terms – Machine Learning, Deep Learning, Stock Prediction.

Published:

07-7-2025

Issue:

Vol. 25 No. 7 (2025)

Page Nos:

73-83

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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