CNY EX Rate Prediction Based on LSTM and Machine Learning Methods

Jiaqi Lu

2024

Abstract

The foreign exchange market is volatile and unpredictable and the foreign exchange rate is challenging to forecast in almost all the regions. With the maturity of the foreign exchange market, more and more traders make transactions on foreign exchange products. The ability to estimate this foreign exchange rate has therefore become crucial in the financial market. In this study, machine learning methods are used to predict the exchange rate of the Chinese yuan (CNY). The feature inputs include three categories, which is respectively technical features, commodity features, and forex features. The technical features include some powerful technical factors. The commodity features include gold price, oil price, and stock index. The forex features include some frequently traded currency. The models include Linear Regression, Lasso Regression, Ridge Regression, long short-term memory (LSTM), Random Forest, and XG-Boost. In conclusion, this study finds that the Long Short-Term Memory model has the best performance and the tech features are the best inputs for predicting the CNY exchange rate.

Paper Citation

in Harvard Style

Lu J. (2024). CNY EX Rate Prediction Based on LSTM and Machine Learning Methods. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 453-458. DOI: 10.5220/0012818800004547

in Bibtex Style

@conference{icdse24,
author={Jiaqi Lu},
title={CNY EX Rate Prediction Based on LSTM and Machine Learning Methods},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={453-458},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012818800004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - CNY EX Rate Prediction Based on LSTM and Machine Learning Methods
SN - 978-989-758-690-3
AU - Lu J.
PY - 2024
SP - 453
EP - 458
DO - 10.5220/0012818800004547
PB - SciTePress