Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network

Table 5

Robust tests for comparisons with different subsamples.

	1-day	5-day	10-day	20-day	30-day
Panel A: MLP model
MLP(3)	49.76	56.98
MLP(5)	45.96	56.98
MLP(5,3)		55.56
MLP(6,4)	49.92
Panel B: EMD-MLP model
EMD(-1)-MLP(3)
EMD(-1)-MLP(5,3)		56.98
EMD(-2)-MLP(3)
EMD(-2)-MLP(5,3)
EMD(-3)-MLP(3)
EMD(-3)-MLP(5,3)
Panel C: EMD-MLP model
EMD(0)-MLP(3)
EMD(0)-MLP(5,3)
EMD(-1)-MLP(3)
EMD(-1)-MLP(5,3)
EMD(-2)-MLP(3)
EMD(-2)-MLP(5,3)

Consider the CNY from January 2, 2006, to December 21, 2015, with a total of 2584 observations. For training the neural network models, three-fourths of the observations are randomly assigned to the training dataset and the remainder is used as the testing dataset. This table compares the forecasting performance, in terms of , for the MLP, EMD-MLP, and EMD-MLP models. We report as percentage for l-day ahead predictions where and 30. Moreover, we examine the ability of all models to predict the direction of change by the DAC test [40]. , , and denote statistical significance at 1%, 5%, and 10%, respectively. For each length of prediction, we mark the maximum as bold.