Artificial Intelligence in Exchange Rate Prediction
0 viewsAbstract
Forecasting exchange rates has historically challenged traditional linear econometric models, which often struggle to outperform random walk hypotheses, particularly during periods of high market volatility. This study investigates whether Artificial Intelligence (AI) and machine learning models can improve predictive accuracy over traditional econometric methods in international finance. Utilizing monthly macroeconomic data (interest rate and inflation differentials, GDP growth, and trade balances) for the USD/EUR exchange rate from 2010 to 2024, this research compares the performance of the Autoregressive Integrated Moving Average (ARIMA) model against three AI-driven approaches: Artificial Neural Networks (ANN), Random Forest Regression, and Long Short-Term Memory (LSTM) networks. Predictive performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The empirical results demonstrate that all evaluated AI-based models significantly outperform the traditional ARIMA framework. Notably, the LSTM network achieved the highest forecasting accuracy, reducing prediction error by approximately 18–22% compared to ARIMA and demonstrating particular robustness during global shocks such as the COVID-19 pandemic. These findings indicate that deep learning models are highly effective at capturing complex, non-linear time dependencies in foreign exchange markets. Ultimately, while challenges regarding model interpretability remain, integrating AI provides substantial value for multinational corporations, portfolio managers, and central banks in optimizing currency risk management, portfolio diversification, and monetary policy design.
Keywords
Artificial Intelligence, Deep Learning, Econometric Modeling, Exchange Rate Forecasting, Foreign Exchange (FX) Markets, Long Short-Term Memory (LSTM), Predictive Analytics, Time-Series Analysis