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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923437" target="_blank" >RIV/00216275:25410/25:39923437 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10479-021-04420-6" target="_blank" >https://link.springer.com/article/10.1007/s10479-021-04420-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10479-021-04420-6" target="_blank" >10.1007/s10479-021-04420-6</a>

Result language

angličtina

Original language name

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

Original language 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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50206 - Finance

Project

<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Annals of Operations Research

ISSN

0254-5330

e-ISSN

1572-9338

Volume of the periodical

345

Issue of the periodical within the volume

Neuveden

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

52

Pages from-to

1335-1386

UT code for WoS article

000722818500001

EID of the result in the Scopus database

2-s2.0-85119976997