Integrating Ensemble Machine Learning with Technical Indicators for Superior FX Volatility Prediction

Forecasting stock market volatility is crucial for effective risk management and informed trading. This paper analyzes the efficacy of ensemble machine learning approaches in predicting stock market volatility. Using a comprehensive dataset of various stocks, we integrate models such as Random Forests, Gradient Boosting Machines, and Stacked Generalization. Rigorous feature engineering and preprocessing optimize the dataset for model ingestion. The ensemble framework combines the strengths of individual models through bagging, boosting, and stacking to harness diverse learning techniques and reduce biases. We incorporate traditional technical analysis indicators, including moving averages (MA), MACD, RSI, and Supertrend, alongside LSTM for improved accuracy. The ensemble model is evaluated using crossvalidation and metrics such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and median absolute percentage error (MDAPE). Our results show that the ensemble model consistently outperforms individual models and existing deep learning approaches in forecasting volatility across multiple stocks. Visualizations demonstrate the model’s ability to capture volatility patterns and align with actual market behavior. The findings highlight key features influencing volatility and underscore the potential of combining ensemble learning with traditional technical analysis to enhance predictive accuracy and reliability. This research advances the understanding of ensemble techniques in financial forecasting, offering practical applications in the stock market.