Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model

Rudra P Pradhan,Rajesh Kumar

doi:10.5539/jmr.v2n4p111

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

Highlights

Modelling and forecasting a financial variable is a very key activity in the financial markets and certainly useful for various players like practitioners, regulators and policy makers
In order to determine the best structure of the Artificial Neural Network (ANN) model, the sensitivity of the ANN model is examined for the different nodes, which are randomly selected in the hidden layer
The empirical findings suggest that neural network is an advanced method in forecasting exchange rate in India

Summary

Introduction

Modelling and forecasting a financial variable is a very key activity in the financial markets and certainly useful for various players like practitioners, regulators and policy makers. It is established fact that increased volatility of a variable and/or using weak forecasting technique in the financial markets is harmful to economic development due to their adverse impact on international trade and foreign investment (Chang and Foo, 2002). Autoregressive Conditional Heteroskedasticity (ARCH) is another extensive model to capture the volatility issue in the financial market forecasting. Though the above models are very useful in out-of-sample forecast accuracy, present paper uses Artificial Neural Network, an alternative model, to capture the non-linear pattern of time series forecasting. We use this technique for forecasting exchange rate in India. Exchange rate forecasting is one of the main endeavors for researchers and practitioners in the international finance, especially in the floating exchange rate era (Hu et al, 1999)

Methods

Results

Conclusion