Abstract
The foreign exchange market is a global financial market that is influenced by economic, political, and psychological factors that are interconnected in complex ways. This complexity makes the foreign exchange market a difficult time-series prediction. At the end of 2019, the world was faced with the COVID-19 pandemic that has not only affected public health but also the foreign exchange market, which makes the trading behaviour affected. Long Short-Term Memory network (LSTM) is a type of recurrent neural network (RNN) that can solve long-term dependencies and is suitable to be a financial time-series model. This study implemented the LSTM model to predict the foreign exchange rate at a timeframe of 1 hour and daily in 2020 to get the best hyperparameter based on the RMSE evaluation results. Furthermore, with the obtained hyperparameters, the prediction result of 2020 was then compared with the 2018 and 2019 prediction results. The best RMSE result was obtained in 1-hour timeframe and when 2020’s RMSE result was compared to 2018’s and 2019’s RMSE result, the prediction of 2019 gave the best RMSE result. The LSTM model is able to achieve good results in the 2020 prediction, proven by the RMSE result which is 0.00135.
Highlights
Neural network methods are a prime candidate for foreign exchange rate prediction because of their ability to handle non-linear, noisy, and complex data[5]
It has been observed that Long Short-Term Memory network (LSTM) can be a financial time-series prediction model even if it is necessary to examine again what hyperparameters are for LSTM to obtain optimal results
This study focuses on applying the LSTM model to a daily and 1-hour timeframe market to predict the EUR/USD exchange rate and analyze the impact of the COVID-19 pandemic on the model’s performance by comparing it with 2 previous years
Summary
The a global foreign financial exchange market rate is that very is by vola far tile more complicated because it is influ as compa enced by red the time and psychology of market participants which is based on economic and political factors. These influences make foreign exchange rate a difficult prediction of time-series data. This study focuses on applying the LSTM model to a daily and 1-hour timeframe market to predict the EUR/USD exchange rate and analyze the impact of the COVID-19 pandemic on the model’s performance by comparing it with 2 previous years. It can help traders or investors to make a prediction model of the foreign exchange rate during the COVID-19 pandemic they can make the right decisions in this difficult time
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More From: International Journal on Information and Communication Technology (IJoICT)
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