Work

Predicting Foreign Exchange EUR/USD Direction Using Machine Learning

Year: 2024

Type: conference-paper

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.

Source: International Conference on Machine Learning and Machine Intelligence (MLMI)

DOI: 10.1145/3696271.3696272

Authors: Kevin Cedric Guyard, Michel Deriaz

Institutions: University of Geneva, HES-SO University of Applied Sciences and Arts Western Switzerland, HES-SO Genève

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4403321873

FWCI: 2.082

Cites: 17

Cited by: 3

Related to: 10