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: ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University)
Salma Mefteh-Wali: ESSCA - ESSCA – École supérieure des sciences commerciales d'Angers = ESSCA Business School
Jean-Laurent Viviani: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique, UR - Université de Rennes

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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)
Date: 2021
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Citations: View citations in EconPapers (34)

Published in Annals of Operations Research, 2021, ⟨10.1007/s10479-021-04187-w⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03331805

DOI: 10.1007/s10479-021-04187-w

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