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IJMLC 2021 Vol.11(1): 61-67 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1015

Development of a Model for Predicting the Direction of Daily Price Changes in the Forex Market Using Long Short-Term Memory

Watthana Pongsena, Prakaidoy Sitsayabut, Nittaya Kerdprasop, and Kittisak Kerdprasop

Abstract—Forex is the largest global financial market in the world. Traditionally, fundamental and technical analysis are strategies that the Forex traders often used. Nowadays, advanced computational technology, Artificial Intelligence (AI) has played a significant role in the financial domain. Various applications based on AI technologies particularly machine learning and deep learning have been constantly developed. As the historical data of the Forex are time-series data where the values from the past affect the values that will appear in the future. Several existing works from other domains of applications have proved that the Long-Short Term Memory (LSTM), which is a particular kind of deep learning that can be applied to modeling time series, provides better performance than traditional machine learning algorithms. In this paper, we aim to develop a powerful predictive model targeting to predicts the daily price changes of the currency pairwise in the Forex market using LSTM. Besides, we also conduct an extensive experiment with the intention to demonstrate the effect of various factors contributing to the performance of the model. The experimental results show that the optimized LSTM model accurately predicts the direction of the future price up to 61.25 percent.

Index Terms—Long short-term memory, time-series data analytic, foreign exchange market, forex.

Watthana Pongsena, Nittaya Kerdprasop, and Kittisak Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000 Thailand (e-mail: pongsena@hotmail.com, nittaya.k@sut.ac.th, kerdpras@sut.ac.th).
Prakaidoy Ditsayabut is with the School of Biotechnology, Suranaree University of Technology, Nakhon Ratchasima 30000 Thailand (e-mail: prakaidoy_sut@hotmail.com).

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Cite: Watthana Pongsena, Prakaidoy Sitsayabut, Nittaya Kerdprasop, and Kittisak Kerdprasop, "Development of a Model for Predicting the Direction of Daily Price Changes in the Forex Market Using Long Short-Term Memory," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 61-67, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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