FORECASTING DAILY FOREIGN EXCHANGE RATE IN INDIA WITH ARTIFICIAL NEURAL NETWORK

Chakradhara Panda () and V. Narasimhan
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Chakradhara Panda: Department of Economics, University of Hyderabad, India
V. Narasimhan: Department of Economics, University of Hyderabad, India

The Singapore Economic Review (SER), 2003, vol. 48, issue 02, 181-199

Abstract: This study compares the efficiency of a non-linear model called artificial neural network with linear autoregressive and random walk models in the one-step-ahead prediction of daily Indian rupee/US dollar exchange rate. We find that neural network and linear autoregressive models outperform random walk model in in-sample and out-of-sample forecasts. The in-sample forecasting of neural network is found to be better than that of linear autoregressive model. As far as out-of-sample forecasting is concerned, the results are mixed and we do not find a "winner" model between neural network and linear autoregressive model. However, neural network is able to improve upon the linear autoregressive model in terms of sign predictions. In addition to this, we also find that the number of input nodes has greater impact on neural network's performance than the number of hidden nodes.

Keywords: Feedforward neural network; Training; Error backpropagation; In-sample prediction; Out-of-sample prediction (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0217590803000712

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