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: email@example.com, firstname.lastname@example.org, email@example.com).
Prakaidoy Ditsayabut is with the School of Biotechnology, Suranaree University of Technology, Nakhon Ratchasima 30000 Thailand (e-mail: firstname.lastname@example.org).
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).