The impact of macroeconomic indicators on ETF: A grey relational analysis-machine learning approach

This study examines the predictive relationship between macroeconomic indicators and the performance of utility and equity ETFs using a hybrid machine learning (ML) framework. Six variables, FDI, GDP, GDP growth rate, industrial production, net exports, and population, were analyzed. A hybrid approach combining Grey Relational Analysis (GRA) and Random Forest (RF)-based feature engineering was used to enhance forecasting accuracy. Five (ML) models (RF, DT, KNN, NB, and SVM) were tested under two specifications: baseline variables versus RF-selected features. Results show that the hybrid models consistently outperformed baseline models across [Formula: see text], RMSE, and MAE, with the support vector machine delivering the best performance. Robustness was confirmed across multiple data splits and validated using Diebold–Mariano tests. The findings highlight key macroeconomic drivers of ETF returns and demonstrate the benefits of integrating feature selection with ensemble learning.