Abstract
genetic programming and artificial neural networks are
employed to forecast two different exchange rates, US
dollar/Japanese Yen and US dollar/Taiwan dollar.
Extended forecasts (that go beyond one-step-ahead)
obtained using the computational techniques were
compared with naive random walk predictions of the two
exchange rates. Sixteen-step-ahead forecasts obtained
using genetic programming outperformed the one- and
sixteen-step-ahead random walk US dollar/Taiwan dollar
exchange rate predictions. Further, sixteen-step-ahead
forecasts of the wavelet-transformed US dollar/Japanese
Yen exchange rate also using genetic programming
outperformed the sixteen-step-ahead random walk
predictions of the exchange rate.
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