Prediction of Exchange Rates using Neural Networks and Performance by Friedman’s Test

Abstract

Forecasting of exchange rates plays a pivotal role in global trade, stocks and making the policies of exports and imports. USD exchange rates used widely for many business areas. In this paper an attempt is made to predict INR/USD exchange rates using Feed forward Neural Networks and Box-Jenkins methodology. The forecasting performance of the developed models were tested using error measures like MAE, MAPE and RMSE. The results shows FFNN model has better model than ARIMA model. The predicted exchange rates would vary between 83.06 and 83.28 for the out sample and this variation is exchange rates would help the business people and also for framing the government policies in the future.