Leveraging on Synthetic Data Generation Techniques to Train Machine Learning Models for Tenaga Nasional Berhad Stock Price Movement Prediction

Abstract

This study employs machine learning models to explore stock price prediction for Tenaga Nasional Berhad (TNB), Malaysia’s primary electricity provider. It addresses the limitations of previous studies by incorporating various input variables, including the stock market, technical, financial, and economic data. This study also tackles the issue of imbalanced class distribution due to small datasets of stock market data by generating synthetic data using Synthetic Minority Over-Sampling Technique (SMOTE) and Generative Adversarial Network-Synthetic Minority Over-Sampling Technique (GAN-SMOTE) techniques. The performance of four classifier models (random forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)) is evaluated without any synthetic data and with synthetic data generated. The SMOTE-ANN model is the best-performing model, exhibiting superior accuracy of 93%, F1-Score of 92%, precision of 90%, recall of 94%, and specificity of 92%. Overall, this research provides valuable insights into TNB stock price movements, offers a solution for imbalanced class distribution, and identifies the top-performing model for predicting TNB stock price movement. These findings are relevant to investors, analysts, and organisations in the utility sector.