Financial Market Directional Forecasting With Stacked Denoising Autoencoder
This addresses stock market prediction for financial analysts, but it is incremental as it applies an existing deep learning method to a specific financial dataset.
The paper tackled stock market direction forecasting by applying a stacked denoising autoencoder (SDAE) to predict the daily CSI 300 index, showing it outperforms back propagation networks and support vector machines with significant advantages in evaluation.
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.