Authors :
Ikenna Ukabuiro; Agomah Stella
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/muvermr2
Scribd :
http://tinyurl.com/5awknw5b
DOI :
https://doi.org/10.5281/zenodo.10453255
Abstract :
Foreign exchange prediction is of important
interest to investors and individual traders in financial
industries in other to maximize profits and reduces
losses. However owing to some factors and the non-
linearity of the FX markets especially in a developing
economy like Nigeria, generating suitable, accurate and
appropriate FX predictions becomes difficult for the
traders of the market. This study utilized models that
include various machine learning algorithm over a trend
analysis and pattern of its prediction. The model results
on the currency pair of United States(USD) over Nigeria
Naira (NGN) using Root Mean Squared Error (RMSE),
Mean Absolute Error(MAE), Mean Square Error
(MSE), and R-square (R2) showed GRU performed
better in predicting the trend and we therefore
considered it best fit for the forecast. The result showed
high prediction over ANN and LSTM, with RMSE,
MAE, MSE, and R2 values of 0.112, 0.075, 0.013, and
0.969.
Keywords :
Forex, ANN, LSTM, GRU MAE, MSE.
Foreign exchange prediction is of important
interest to investors and individual traders in financial
industries in other to maximize profits and reduces
losses. However owing to some factors and the non-
linearity of the FX markets especially in a developing
economy like Nigeria, generating suitable, accurate and
appropriate FX predictions becomes difficult for the
traders of the market. This study utilized models that
include various machine learning algorithm over a trend
analysis and pattern of its prediction. The model results
on the currency pair of United States(USD) over Nigeria
Naira (NGN) using Root Mean Squared Error (RMSE),
Mean Absolute Error(MAE), Mean Square Error
(MSE), and R-square (R2) showed GRU performed
better in predicting the trend and we therefore
considered it best fit for the forecast. The result showed
high prediction over ANN and LSTM, with RMSE,
MAE, MSE, and R2 values of 0.112, 0.075, 0.013, and
0.969.
Keywords :
Forex, ANN, LSTM, GRU MAE, MSE.
