Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model
Rudra P Pradhan, Rajesh Kumar
Abstract
The
paper employs Artificial Neural Network (ANN) to forecast foreign
exchange rate in India during 1992-2009. We used two types of data set
(daily and monthly) for US dollar, British pound, euro and Japanese yen.
The performance of forecasting is quantified by using various loss
functions namely root mean square error (RMSE), mean absolute error
(MAE), mean absolute deviation (MAD) and mean absolute percentage error
(MAPE). Empirical results confirm that ANN is an effective tool to
forecast the exchange rate. The technique gives the evidence that there
is possibility of extracting information hidden in the foreign exchange
rate and predicting it into the future. The evaluation of the proposed
model is based on the estimation of the average behaviour of the above
loss functions.
Keywords- Exchange Rate; Neural Network
This work is licensed under a Creative Commons Attribution 3.0 License.
Journal of Mathematics Research ISSN 1916-9795(Print) ISSN 1916-9809 (Online)
Copyright © Canadian Center of Science and Education
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