Exploring the Predictive Power of Machine Learning Algorithms on Daily Gold Prices

Abstract

Gold, a highly valued and significant investment asset, is subject to various influences including global economic conditions and geopolitical events. Recent advancements in machine learning have shown promising results in predicting financial time series, including gold prices. This study evaluates machine learning algorithms (Linear/Ridge/LASSO Regression, Decision Trees, Random Forest, XGBoost, SVM) for gold price forecasting. A comparative analysis of these algorithms reveals that tree-based machine learning techniques, specifically decision trees, random forest, and XGBoost, outperform other algorithms. Among them, random forest exhibits the highest R2 value (R2 = 0.99) and the lowest values for RMSE (1.38), MSE (1.89), and MAE (0.95). XGBoost and decision trees both achieve an R2 of 0.99 and obtain RMSE values of 1.51 and 1.76, MSE values of 2.28 and 3.09, and MAE values of 1.08 and 1.14, respectively. These findings suggest that tree-based machine learning models may be more suitable for predicting gold prices compared to traditional approaches.