Machine Learning Methods for Predicting the Price of Exchange-Traded Funds

Abstract

This study applies machine learning algorithms to the field of quantitative finance. By employing both Random Forest and Extreme Gradient Boosting (XGBoost) models to predict price movements of nine different Exchange-traded funds (ETFs) from the US, it assesses the practical performance of machine learning in ETFs’ price forecasting, thereby assisting investors and institutions in better evaluating future ETF trends. The ETFs’ price data used in this research are sourced from the US ETF Prices datasets on Kaggle. Technical indicators such as Bollinger Bands, Relative Strength Index (RSI), and Moving Average (MA) were incorporated into the models through feature engineering. The performance of both models was evaluated across different time windows and ETF products. Comparative analysis revealed that both Random Forest and XGBoost perform well within the 5 to 200-day forecasting horizon. The results indicate that larger sample sizes positively impact the goodness-of-fit of the Random Forest model, while excessively large samples may lead to degraded performance in XGBoost. In conclusion, while machine learning algorithms show strong promise in predicting ETF price movements, practitioners should still integrate market experience, sentiment analysis, and multi-factor evaluation to comprehensively assess ETF performance.