Predicting Foreign Exchange EUR/USD direction using machine learning

Kevin Cedric Guyard, Michel Deriaz
{"title":"Predicting Foreign Exchange EUR/USD direction using machine learning","authors":"Kevin Cedric Guyard, Michel Deriaz","doi":"arxiv-2409.04471","DOIUrl":null,"url":null,"abstract":"The Foreign Exchange market is a significant market for speculators,\ncharacterized by substantial transaction volumes and high volatility.\nAccurately predicting the directional movement of currency pairs is essential\nfor formulating a sound financial investment strategy. This paper conducts a\ncomparative analysis of various machine learning models for predicting the\ndaily directional movement of the EUR/USD currency pair in the Foreign Exchange\nmarket. The analysis includes both decorrelated and non-decorrelated feature\nsets using Principal Component Analysis. Additionally, this study explores\nmeta-estimators, which involve stacking multiple estimators as input for\nanother estimator, aiming to achieve improved predictive performance.\nUltimately, our approach yielded a prediction accuracy of 58.52% for one-day\nahead forecasts, coupled with an annual return of 32.48% for the year 2022.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.04471","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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.
利用机器学习预测外汇欧元/美元走向
外汇市场是投机者的重要市场,具有交易量大、波动性高的特点。准确预测货币对的方向性走势对于制定合理的金融投资策略至关重要。本文对各种机器学习模型进行了比较分析,以预测外汇市场中欧元/美元货币对的每日方向性走势。分析包括使用主成分分析法的装饰相关和非装饰相关特征集。此外,本研究还探索了meta估计器,其中涉及将多个估计器作为另一个估计器的输入进行堆叠,以达到提高预测性能的目的。最终,我们的方法得出了58.52%的提前一天预测准确率,以及2022年32.48%的年回报率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信