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Deep learning-based exchange rate prediction during the COVID-19 pandemic

Mohammad Abedin, Mahmudul Hasan Moon, M. Kabir Hassan, Petr Hajek

Annals of Operations Research

Swansea University Author: Mohammad Abedin

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DOI (Published version): 10.1007/s10479-021-04420-6

Abstract

This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies agains...

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Published in: Annals of Operations Research
ISSN: 0254-5330 1572-9338
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa64234
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first_indexed 2023-09-20T10:13:56Z
last_indexed 2023-09-20T10:13:56Z
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spelling v2 64234 2023-08-31 Deep learning-based exchange rate prediction during the COVID-19 pandemic 4ed8c020eae0c9bec4f5d9495d86d415 Mohammad Abedin Mohammad Abedin true false 2023-08-31 BAF This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic. Journal Article Annals of Operations Research Springer Science and Business Media LLC 0254-5330 1572-9338 Bagging ridge, Bi-LSTM, COVID-19, Deep learning, Machine learning, Exchange rate forecasting 26 11 2021 2021-11-26 10.1007/s10479-021-04420-6 http://dx.doi.org/10.1007/s10479-021-04420-6 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University This article was supported by the scientific research project of the Czech Sciences Foundation Grant No. 19-15498S. 2024-04-11T12:57:31.6913045 2023-08-31T17:36:42.6576783 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Mohammad Abedin 1 Mahmudul Hasan Moon 2 M. Kabir Hassan 3 Petr Hajek 4
title Deep learning-based exchange rate prediction during the COVID-19 pandemic
spellingShingle Deep learning-based exchange rate prediction during the COVID-19 pandemic
Mohammad Abedin
title_short Deep learning-based exchange rate prediction during the COVID-19 pandemic
title_full Deep learning-based exchange rate prediction during the COVID-19 pandemic
title_fullStr Deep learning-based exchange rate prediction during the COVID-19 pandemic
title_full_unstemmed Deep learning-based exchange rate prediction during the COVID-19 pandemic
title_sort Deep learning-based exchange rate prediction during the COVID-19 pandemic
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Mohammad Abedin
Mahmudul Hasan Moon
M. Kabir Hassan
Petr Hajek
format Journal article
container_title Annals of Operations Research
publishDate 2021
institution Swansea University
issn 0254-5330
1572-9338
doi_str_mv 10.1007/s10479-021-04420-6
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1007/s10479-021-04420-6
document_store_str 0
active_str 0
description This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic.
published_date 2021-11-26T12:57:28Z
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score 11.037319

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