Prediction of the Iranian Stock Market Using Elliott Wave Oscillations and Relative Strength Index with the Aid of Machine Learning
Keywords:
Trend prediction, Technical analysis, Elliott Wave Theory, Classification algorithms, Machine learning, Iranian stock marketAbstract
The present study aimed to predict the trend of the Iranian stock market using the Elliott Wave Oscillator and Relative Strength Index within machine learning algorithms. This quantitative study was conducted using the daily data of the Tehran Stock Exchange Total Index from April 30, 2011, to September 21, 2024. Data were collected from the Iran Financial Information Processing Center and underwent preprocessing procedures including noise removal, normalization, and labeling into buy, sell, and hold categories. Technical signals were extracted through the Elliott Wave Oscillator and Relative Strength Index (RSI). Subsequently, the processed dataset was applied to four machine learning classification algorithms, including Decision Tree, Naïve Bayes, Support Vector Machine, and K-Nearest Neighbors. Seventy percent of the data were used for training and thirty percent for testing the models. Model performance was evaluated using accuracy, precision, and recall indicators. The findings demonstrated that identifying Elliott Waves in the Tehran Stock Exchange Index is feasible and that combining the Elliott Wave Oscillator with RSI effectively detects buy, sell, and hold positions. The machine learning algorithms also showed satisfactory performance in predicting market trends. Among the examined models, the Support Vector Machine and Decision Tree algorithms outperformed the other methods and achieved prediction accuracies above 90 percent. The results further indicated that integrating technical indicators with intelligent learning methods significantly improved forecasting capability and reduced prediction errors. The findings suggest that Elliott Wave Theory combined with the Relative Strength Index and machine learning algorithms can serve as an effective framework for predicting the Iranian stock market trend. This integrated approach may assist investors and traders in identifying market turning points, managing investment risk, and making more accurate trading decisions while supporting the development of intelligent financial forecasting systems.
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Copyright (c) 2025 Mohammad Javad Mahmoudi (Corresponding author); Seyedeh Melika Lajevardi, Parviz Yari (Author)
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