Abstract: Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian market. This study applies Back Propagation (BP) neural networks and Empirical Mode Decomposition (EMD) based neural networks for three Asian stock markets. In this study, an EMD-based neural network ensemble learning paradigm is proposed for three Asian stock market indices forecasting. For this purpose, three forecasting models are introduced in this study as follow: ANNs, the combination of EMD and ANNs, the combination of ANNs and EMD with parallel data input, the highest price, the lowest price and the close price. The three Asian stock market indices series are first decomposed into a finite and often small number of Intrinsic Mode Functions (IMFs) by introducing EMD function. Then a three-layer feed-forward neural network model is used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with a neural network to formulate an ensemble output for the three Asian stock market indices, respectively. The present experimental results show the superiority of the third model, compared to the previous two models.