Comparative Analysis of Exchange Rate Forecasting Techniques: Emphasis on Machine Learning Algorithms for Pakistan

The exchange rate is crucial because it can influence a country’s economy. It helps brokers and traders in operational decisions that help reduce risk and maximize profits. Many methods of forecasting currency exchange rates exist. The present study focused on different methodologies, including Box-Jenkins, Holt’s practice, artificial neural networks, Facebook Prophet Model, and Multilayer Perceptron (MLP) for predicting exchange rates. The performance of these techniques is evaluated based on small mean squared error, mean absolute error, and mean absolute percentage error. The results revealed that MLP outperformed all the models. It is a promising method to forecast the exchange rate of Pakistan because it gives a minor forecast error. In addition, the predicted values using MLP are very close to the actual values. The experimental results and time series plot revealed that the exchange rate of Pakistan will slightly increase in the upcoming months. It is concluded that the present study will help to determine the aggregate demand for domestic currency in the coming months. It is also helpful for the government and policymakers. However, understanding exchange rates is essential for anyone involved in international business and finance.