Forecasting Foreign Currency Exchange Rate using Convolutional Neural Network

Foreign exchange rate forecasting has always been in demand because it is critical for foreign traders to know how their money will perform against other currencies. Traders and investors are always looking for fresh ways to outperform the market and make more money. As a result, economists, researchers and investors have done a number of studies in order to forecast trends and facts that influence the rise or fall of the exchange rate (ER). In this paper, a new Convolutional Neural Network (CNN) model with a random forest regression layer is used for future closing price prediction. The intended model has been tested using three major currency pairs: Australian Dollar against the Japanese Yen (AUD/JPY), the New Zealand Dollar against the US Dollar (NZD/USD) and the British Pound Sterling against the Japanese Yen (GBP/JPY). As a proof-of-concept, the forecast is made for 1 month, 2 months, 3 months, 4 months, 5 months, 6 months and 7 months utilizing data from January 2, 2001 to May 31, 2020 for AUD/JPY and GBP/JPY and data from January 1, 2003 to May 31, 2020 for NZD/USD. Furthermore, when compared the performance of the suggested model with the Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP) and Linear Regression (LR) models and found that the proposed CNN with Random Forest model surpasses all models. The suggested model's prediction performance is assessed using R2, MAE, RMSE performance measures. The proposed model's average R2 values for three currency pairs from one to seven months are 0.9616, 0.9640 and 0.9620, demonstrating that it is the best model among them. The study's findings have ramifications for both policymakers and investors in the foreign exchange market.

@article{Panda2022,
  title     = {Forecasting Foreign Currency Exchange Rate using Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Manaswinee Madhumita Panda and Surya Narayan Panda and Prasant Kumar Pattnaik},
  doi       = {10.14569/IJACSA.2022.0130272},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130272}
}