Predicting Foreign Exchange EUR/USD direction using machine learning

TL;DR

This study compares machine learning models for predicting daily EUR/USD currency pair directions, utilizing PCA and stacking techniques, achieving 58.52% accuracy and a 32.48% annual return.

Contribution

It introduces a comprehensive comparison of ML models with PCA and stacking for FX prediction, improving understanding of predictive strategies.

Findings

Achieved 58.52% prediction accuracy for EUR/USD direction

Attained an annual return of 32.48% in 2022

Demonstrated effectiveness of meta-estimators in FX prediction

Abstract

The Foreign Exchange market is a significant market for speculators, characterized by substantial transaction volumes and high volatility. Accurately predicting the directional movement of currency pairs is essential for formulating a sound financial investment strategy. This paper conducts a comparative analysis of various machine learning models for predicting the daily directional movement of the EUR/USD currency pair in the Foreign Exchange market. The analysis includes both decorrelated and non-decorrelated feature sets using Principal Component Analysis. Additionally, this study explores meta-estimators, which involve stacking multiple estimators as input for another estimator, aiming to achieve improved predictive performance. Ultimately, our approach yielded a prediction accuracy of 58.52% for one-day ahead forecasts, coupled with an annual return of 32.48% for the year 2022.