RESEARCH ARTICLE
Forecasting multi-frequency intraday exchange rates using deep learning models
Muhammad Arslan,
Ahmed Imran Hunjra,
Wajid Shakeel Ahmed,
Younes Ben Zaied,
Muhammad Arslan
Department of Economics and Finance, Foundation University Islamabad, Islamabad, Pakistan
Search for more papers by this authorAhmed Imran Hunjra
Rabat Business School, International University of Rabat, Rabat, Morocco
Search for more papers by this authorWajid Shakeel Ahmed
Department of Management Sciences, COMSATS University Islamabad, Islamabad, Pakistan
Search for more papers by this authorCorresponding Author
Younes Ben Zaied
EDC Pairs Business School, Paris, France
Correspondence
Younes Ben Zaied, EDC Pairs Business School, Paris, France.
Email: [email protected]
Search for more papers by this authorMuhammad Arslan,
Ahmed Imran Hunjra,
Wajid Shakeel Ahmed,
Younes Ben Zaied,
Muhammad Arslan
Department of Economics and Finance, Foundation University Islamabad, Islamabad, Pakistan
Search for more papers by this authorAhmed Imran Hunjra
Rabat Business School, International University of Rabat, Rabat, Morocco
Search for more papers by this authorWajid Shakeel Ahmed
Department of Management Sciences, COMSATS University Islamabad, Islamabad, Pakistan
Search for more papers by this authorCorresponding Author
Younes Ben Zaied
EDC Pairs Business School, Paris, France
Correspondence
Younes Ben Zaied, EDC Pairs Business School, Paris, France.
Email: [email protected]
Search for more papers by this authorFirst published: 15 February 2024
Abstract
This paper examines the behavior of currencies' intraday exchange rates with mainly focuses on predicting these behaviors through deep learning models. The time series data are used in this study and comprise intraday exchange rate data for seven volatile currencies, recorded at two different frequency intervals: 1 h and 30 min. The data cover the time frame from January 1, 2018, to December 31, 2020. Firstly, wavelet maximal overlap discreet wavelet transform (MODWT) type “haar” is applied in order to identify the noise representing the volatile trend. Then learning models are applied to historical data that includes support vector regression (SVR), recurrent neural network (RNN), and long short-term memory (LSTM). The main findings of the study provide strong evidence that the deep learning technique (i.e., LSTM) outperforms the other compatible models. This has been confirmed by the statistical measures for accuracy purposes. This study intends to address complex trends exhibited by volatile behavior of intraday exchange rates through non-conventional learning techniques for the time of need-based for the currency traders and other stakeholders.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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