ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
This work aims to improve stock trend forecasting accuracy for investors and financial institutions by providing cleaner feature extraction.
This paper addresses the challenge of extracting clean features from noisy stock data for trend forecasting. The proposed Augmented Disentanglement Distillation (ADD) framework significantly improves stock trend forecasting performance and actual investment income through backtesting on Chinese stock market data.
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.