Forecasting AUD/USD Exchange Rates Using LSTM and Macroeconomic Indicators

Zimu Li

2025

Abstract

The foreign exchange (forex) market, with its daily trading volume exceeding $7.5 trillion, plays a pivotal role in global economic stability and cross-border transactions. However, the decentralized and volatile nature of forex markets poses significant challenges for risk management, particularly due to after-hours fluctuations and nonlinear interactions between macroeconomic factors. Traditional linear models, such as Autoregressive Integrated Moving Average (ARIMA) and linear regression, often fail to capture these complexities, necessitating advanced predictive frameworks. This study proposes a bidirectional Long Short-Term Memory (LSTM) model integrated with macroeconomic indicators to forecast AUD/USD exchange rates. Utilising historical forex data (2014–2024) and features including interest rate differentials, commodity prices (crude oil, copper), and GDP growth, the model was trained to minimize Mean Squared Error (MSE) and evaluated using rolling Root Mean Squared Error (RMSE) and volatility clustering analysis. Results demonstrate the LSTM’s superiority, achieving a test Root Mean Squared Error (RMSE) of 0.0087 and Mean Absolute Percentage Error (MAPE) of 1.24%, outperforming ARIMA (RMSE=0.0121) and linear regression (RMSE=0.0143). Critical features identified via Random Forest highlight commodity prices (32% importance) and interest rates (24%) as dominant predictors. The findings validate LSTM’s capability to model nonlinear market dynamics, offering firms a robust tool for hedging and algorithmic trading. Limitations include reliance on historical data and computational intensity, suggesting future integration of real-time sentiment analysis for enhanced adaptability.

Paper Citation

in Harvard Style

Li Z. (2025). Forecasting AUD/USD Exchange Rates Using LSTM and Macroeconomic Indicators. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 538-543. DOI: 10.5220/0013701600004670

in Bibtex Style

@conference{icdse25,
author={Zimu Li},
title={Forecasting AUD/USD Exchange Rates Using LSTM and Macroeconomic Indicators},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={538-543},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013701600004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Forecasting AUD/USD Exchange Rates Using LSTM and Macroeconomic Indicators
SN - 978-989-758-765-8
AU - Li Z.
PY - 2025
SP - 538
EP - 543
DO - 10.5220/0013701600004670
PB - SciTePress