A comparative analysis of gold price forecasting efficiency using model averaging over regression trees and random forest

Abstract

This research aims to compare the forecasting performance of gold prices using two tree-averaging methods: Multi-Model Tree Averaging and K-Fold Tree Averaging. Both methods employ weighting schemes, namely Equal Weight and AIC Weight. The study also compares these methods with Random Forest to evaluate forecasting accuracy and stability. The data used are monthly secondary data on economic variables, such as inflation rates and interest rates in Thailand, the United States, China, and India, spanning from January 2014 to July 2024. The results show that Random Forest achieves the most suitable performance in terms of R², RMSE, and MAPE in both training and testing datasets, demonstrating superior accuracy and stability. Multi-Model Tree Averaging and K-Fold Tree Averaging rank slightly lower in performance, with AIC Weight consistently outperforming Equal Weight in all scenarios. This study highlights the potential of diverse forecasting methods and provides recommendations for selecting appropriate techniques for predicting gold prices and other highly volatile assets.