Forecasting gold price with the XGBoost algorithm and SHAP interaction values

Sami Ben Jabeur (), Salma Mefteh-Wali () and Jean-Laurent Viviani ()
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Sami Ben Jabeur: Confluence: Sciences Et Humanités - UCLY, ESDES
Salma Mefteh-Wali: ESSCA School of Management
Jean-Laurent Viviani: University of Rennes 1, CNRS

Annals of Operations Research, 2024, vol. 334, issue 1, No 25, 679-699

Abstract: 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.

Keywords: Gold price; XGBoost; CatBoost; Shapley additive explanations (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 G12 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-021-04187-w

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