用于准确汇率预测的新型混合深度学习方法

IF 2 Q2 BUSINESS, FINANCE Risks Pub Date : 2024-08-30 DOI:10.3390/risks12090139
Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed, Lulwah M. Al-Essa
{"title":"用于准确汇率预测的新型混合深度学习方法","authors":"Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed, Lulwah M. Al-Essa","doi":"10.3390/risks12090139","DOIUrl":null,"url":null,"abstract":"The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.","PeriodicalId":21282,"journal":{"name":"Risks","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction\",\"authors\":\"Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed, Lulwah M. Al-Essa\",\"doi\":\"10.3390/risks12090139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.\",\"PeriodicalId\":21282,\"journal\":{\"name\":\"Risks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/risks12090139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/risks12090139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0

摘要

全球外汇(FX)市场是我们金融体系中一个重要而庞大的组成部分。在这个市场上,企业和投资者既进行投机交易,也进行套期保值。多年来,人们对外汇建模和预测的兴趣与日俱增。最近,机器学习(ML)和深度学习(DL)技术在提高预测准确性方面取得了可喜的成果。在外汇市场规模不断扩大以及 ML 技术不断进步的推动下,我们提出了一个新颖的预测框架,即 MVO-BiGRU 模型,该模型集成了变模分解(VMD)、数据增强、Optuna 优化超参数和双向 GRU 算法,用于月度外汇汇率预测。预防模块中的数据扩增大大增加了数据组合的多样性,有效地减少了过拟合问题,而 Optuna 优化则确保了模型配置的最优化,从而提高了性能。我们的研究成果包括 MVO-BiGRU 模型的开发,以及将其应用于外汇市场所获得的启示。我们的研究结果表明,MVO-BiGRU 模型可以成功避免过度拟合,并在样本外预测方面达到最高准确度,同时在多个评估标准方面优于基准模型。这些发现为在低频时间序列数据上实施 ML 和 DL 模型提供了有价值的见解,因为在低频时间序列数据上,人工数据增强可能具有挑战性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction
The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Risks
Risks Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
3.80
自引率
22.70%
发文量
205
审稿时长
11 weeks
期刊最新文献
Funding Illiquidity Implied by S&P 500 Derivatives Dynamics of Foreign Exchange Futures Trading Volumes in Thailand Automated Machine Learning and Asset Pricing What Drives Banks to Provide Green Loans? Corporate Governance and Ownership Structure Perspectives of Vietnamese Listed Banks Trends and Risks in Mergers and Acquisitions: A Review
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1