---
title: Deep Learning for Commodity Risk Management
url: https://www.ml-quant.com/papers/repec/wly-jfutmk-v-44-y-2024-i-6-p-879-900/
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:wly:jfutmk:v:44:y:2024:i:6:p:879-900
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Ffut.22497%3Bh%3Drepec%3Awly%3Ajfutmk%3Av%3A44%3Ay%3A2024%3Ai%3A6%3Ap%3A879-900
featured: 2024-05-28
citations: unknown
topic: Risk, Credit & Banking
---


# Deep Learning for Commodity Risk Management

A new deep learning strategy for financial hedging has been created, offering improved risk management and an average annual economic benefit of 1.21 million CNY for a typical Chinese aluminum firm.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Ffut.22497%3Bh%3Drepec%3Awly%3Ajfutmk%3Av%3A44%3Ay%3A2024%3Ai%3A6%3Ap%3A879-900
- Identifier: RePEc:wly:jfutmk:v:44:y:2024:i:6:p:879-900
- Released: 2024-05-28
- First featured: Quant Letter No. 51 (2024-05-28): https://www.ml-quant.com/issues/2024-05-28/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

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