# ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting

arXiv: https://arxiv.org/abs/2012.06289 · HTML: https://mlsanity.com/paper/2012.06289/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction](https://mlsanity.com/paper/2409.08281/) (2024)
- [Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices](https://mlsanity.com/paper/2312.03758/) (2023)
- [Joint Latent Topic Discovery and Expectation Modeling for Financial Markets](https://mlsanity.com/paper/2307.08649/) (2023)
- [Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets](https://mlsanity.com/paper/2609.34510/) (2026)

## Abstract (arXiv)

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 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.