  Stock trend forecasting has become a popular research direction that attracts
widespread attention in the financial field. Though deep learning methods have
achieved promising results, there are still many limitations, for example, how
to extract clean features from the raw stock data. In this paper, we introduce
an \emph{Augmented Disentanglement Distillation (ADD)} approach to remove
interferential features from the noised raw data. Specifically, we present 1) a
disentanglement structure to separate excess and market information from the
stock data to avoid the two factors disturbing each other's own prediction.
Besides, by applying 2) a dynamic self-distillation method over the
disentanglement framework, other implicit interference factors can also be
removed. Further, thanks to the decoder module in our framework, 3) a novel
strategy is proposed to augment the training samples based on the different
excess and market features to improve performance. We conduct experiments on
the Chinese stock market data. Results show that our method significantly
improves the stock trend forecasting performances, as well as the actual
investment income through backtesting, which strongly demonstrates the
effectiveness of our approach.
