# Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning

arXiv: https://arxiv.org/abs/2303.16149 · HTML: https://mlsanity.com/paper/2303.16149/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 98 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Towards a Rigorous Evaluation of XAI Methods on Time Series](https://mlsanity.com/paper/1909.07082/) (2019)
- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://mlsanity.com/paper/2101.02118/) (2021)
- [The Inadequacy of Shapley Values for Explainability](https://mlsanity.com/paper/2302.08160/) (2023)
- [Explainable boosted linear regression for time series forecasting](https://mlsanity.com/paper/2009.09110/) (2020)
- [Parameter Efficient Deep Probabilistic Forecasting](https://mlsanity.com/paper/2112.02905/) (2021)
- [Model Transparency and Interpretability : Survey and Application to the Insurance Industry](https://mlsanity.com/paper/2209.00562/) (2022)
- [Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans](https://mlsanity.com/paper/2009.10990/) (2020)
- [An Investigation of the Impact of COVID-19 Non-Pharmaceutical Interventions and Economic Support Policies on Foreign Exchange Markets with Explainable AI Techniques](https://mlsanity.com/paper/2111.14620/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting](https://mlsanity.com/paper/2407.15909/) (2024)
- [EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods](https://mlsanity.com/paper/2408.13214/) (2024)
- [Predictive economics: Rethinking economic methodology with machine learning](https://mlsanity.com/paper/2510.04726/) (2025)
- [Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability](https://mlsanity.com/paper/2606.15058/) (2026)
- [Forecasting Intraday USD/CAD Exchange Rate with News-Derived Monetary-Policy Signals](https://mlsanity.com/paper/2609.32773/) (2026)

## Abstract (arXiv)

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada's main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model's decisions, which are supported by theoretical considerations.