Forecasting currency exchange rates via Feedforward Backpropagation Neural Network

Abstract

The latest globalization trends resulted in increasingly interdependent economies of nations and multinational firms. This may leave companies operating internationally at the mercy of the volatility in currency exchange rates. Forecasting these exchange rates became very important in international trade and commerce, as it involves key decisions of foreign investment, forward contracts and expanding business to new horizons. This research paper describes a Feedforward Backpropagation Neural Network (FBNN) model and its application to currency exchange rate forecasting. A study of FBNN model is conducted for forecasting exchange rates between Indian rupee and US dollar, based on previous data of inflation, real interest rates, gross domestic product (GDP), current account balances, government budget balances and debts of both countries. The weights used in neural networks were optimized using gradient descent and backpropagation method. Models with different hidden neuron layers were developed by comparing the actual exchange rates with forecasted monthly exchange rates from January 2001 to December 2014. The most effective model was then used to simulate exchange rates for the year 2015. The FBNN model with ten neurons in the hidden layer has the least Mean average percentage error (MAPE) value of 1.32% and is considered to be most impressive model.