---
title: Forecasting Gold Price
url: https://www.ml-quant.com/papers/repec/spr-annopr-v-334-y-2024-i-1-d-10-1007-s10479-021-04187-w/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:spr:annopr:v:334:y:2024:i:1:d:10.1007_s10479-021-04187-w
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10479-021-04187-w%3Bh%3Drepec%3Aspr%3Aannopr%3Av%3A334%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10479-021-04187-w
featured: 2024-03-20
citations: unknown
topic: Econometrics & Forecasting
---


# Forecasting Gold Price

The paper suggests using the eXtreme Gradient Boosting (XGBoost) machine learning model and Shapley additive explanations (SHAP) for accurate forecasting and interpretation of gold price fluctuations, surpassing other advanced models.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10479-021-04187-w%3Bh%3Drepec%3Aspr%3Aannopr%3Av%3A334%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10479-021-04187-w
- Identifier: RePEc:spr:annopr:v:334:y:2024:i:1:d:10.1007_s10479-021-04187-w
- Released: 2024-03-20
- First featured: Quant Letter No. 41 (2024-03-20): https://www.ml-quant.com/issues/2024-03-20/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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