  Forecasting stock market direction is always an amazing but challenging
problem in finance. Although many popular shallow computational methods (such
as Backpropagation Network and Support Vector Machine) have extensively been
proposed, most algorithms have not yet attained a desirable level of
applicability. In this paper, we present a deep learning model with strong
ability to generate high level feature representations for accurate financial
prediction. Precisely, a stacked denoising autoencoder (SDAE) from deep
learning is applied to predict the daily CSI 300 index, from Shanghai and
Shenzhen Stock Exchanges in China. We use six evaluation criteria to evaluate
its performance compared with the back propagation network, support vector
machine. The experiment shows that the underlying financial model with deep
machine technology has a significant advantage for the prediction of the CSI
300 index.
