---
title: Exchange Rates Forecasting with Interpretable Machine Learning
url: https://www.ml-quant.com/papers/repec/taf-apeclt-v-30-y-2023-i-15-p-2052-2059/
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:taf:apeclt:v:30:y:2023:i:15:p:2052-2059
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F13504851.2022.2089621%3Bh%3Drepec%3Ataf%3Aapeclt%3Av%3A30%3Ay%3A2023%3Ai%3A15%3Ap%3A2052-2059
featured: 2023-08-30
citations: unknown
topic: Macro-Finance & Rates
---


# Exchange Rates Forecasting with Interpretable Machine Learning

The Light Gradient Boosting Machine model has been found to be the most effective at predicting 12 exchange rates due to its ability to extract short-term information and robustness on small datasets.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F13504851.2022.2089621%3Bh%3Drepec%3Ataf%3Aapeclt%3Av%3A30%3Ay%3A2023%3Ai%3A15%3Ap%3A2052-2059
- Identifier: RePEc:taf:apeclt:v:30:y:2023:i:15:p:2052-2059
- Released: 2023-08-30
- First featured: Quant Letter No. 14 (2023-08-30): https://www.ml-quant.com/issues/2023-08-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Macro-Finance & Rates

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