Determine Intraday Trading Currency's Trend Framework Evidence From Machine Learning Techniques

Inzamam Khan, Wajid Shakeel Ahmed, Shafqat Shad, Chandan Kumar, Muhammad Usman, Milad Jasemi and Khuarm Shafi
Additional contact information
Inzamam Khan: COMSATS University, Islamabad, Pakistan
Wajid Shakeel Ahmed: COMSATS University, Islamabad, Pakistan
Shafqat Shad: University of Montevallo, USA
Chandan Kumar: Iowa State University, USA
Muhammad Usman: Verizon Inc., USA
Milad Jasemi: University of Montevallo, USA
Khuarm Shafi: COMSATS University, Islamabad, Pakistan

International Journal of Business Intelligence Research (IJBIR), 2024, vol. 15, issue 1, 1-15

Abstract: The purpose of this study is to determine the intraday hourly trading trends of currencies using predictive modeling techniques. The study encompasses two distinct intraday time intervals of 30 minutes and 1 hour, analyzing currencies from 8 different countries. It incorporates the use of wavelets MODWT to identify trends and noise in intraday currency analysis. Three predictive models, namely Support Vector Regression, Recurrent Neural Network, and Long Short-Term Memory, are applied to relative time series data to predict intraday trading currency trends. The study reveals significant noise presence in three currencies based on MODWT analysis. Additionally, it demonstrates that deep learning techniques, such as LSTM, outperform traditional machine learning approaches in accurately predicting intraday currency trends. This study contributes substantially to the theoretical understanding of international finance and provides practical insights for real-time problem-solving in currency markets. Further, this research adds to the discourse on leveraging sophisticated analytical methods within the domain of business intelligence to enhance decision-making processes in organizations operating within dynamic and complex financial environments.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBIR.346977 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jbir00:v:15:y:2024:i:1:p:1-15

Access Statistics for this article

International Journal of Business Intelligence Research (IJBIR) is currently edited by Ana Azevedo

More articles in International Journal of Business Intelligence Research (IJBIR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().