{"title":"利用机器学习预测外汇欧元/美元走向","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":"{\"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}","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}
Predicting Foreign Exchange EUR/USD direction using machine learning
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.