Financial Market Directional Forecasting With Stacked Denoising Autoencoder

TL;DR

This paper introduces a deep learning approach using stacked denoising autoencoders to improve the accuracy of predicting the daily CSI 300 stock index, outperforming traditional shallow models.

Contribution

The paper presents a novel application of stacked denoising autoencoders for financial market prediction, demonstrating superior performance over existing shallow methods.

Findings

Deep learning model outperforms traditional methods in prediction accuracy.

Stacked denoising autoencoder effectively captures high-level features.

Significant advantage shown in predicting the CSI 300 index.

Abstract

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.