Forecasting Exchange Rates via EEMD-Based Neural Networks
Date Issued
2012
Date
2012
Author(s)
Yu, Hsuan
Abstract
In this study, an ensemble empirical mode decomposition (EEMD) based feedforward neural network framework is proposed for exchange rate forecasting. For this purpose, the original exchange rate series is first decomposed
into a finite (and often small) number of intrinsic mode functions (IMFs). Then a 3-layer neural network is used to model each of the selected IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally,the prediction results for all IMFs are combined to formulate an aggregate output of the predicted exchange rate movement. Our empirical results show that this modeling procedure has significantly larger root mean squared errors
(RMSE) than the random walk model. However, sign tests and trading strategy returns suggest that this method indeed has superior predictive ability for directional change.
Subjects
Hilbert-Huang transform
Ensemble EMD
Neural networks
Exchange rate forecasting
Directional forecast accuracy test
Market timing
Type
thesis
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