Abstract
Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance.
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Notes
More detailed features of these algorithms will be presented in the methodology section.
Semantically, we note that the terms variable and feature are identical. The former tends to be used in statistics and the latter tends to be used in computer science.
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Jabeur, S.B., Mefteh-Wali, S. & Viviani, JL. Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Ann Oper Res 334, 679–699 (2024). https://doi.org/10.1007/s10479-021-04187-w
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DOI: https://doi.org/10.1007/s10479-021-04187-w