Papers › Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend...
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu
Stock trend prediction plays a critical role in seeking maximized profit from stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of stock market. Exploding information on Internet together with advancing development of natural language processing and text mining techniques have enable investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness and comprehensiveness of online content related to stock market varies drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our approach.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Stock Market Prediction | Astock | HAN Stock | Accuray | 57.35 | #16 of 17 | Archive leaderboard | report |
| Stock Market Prediction | Astock | HAN Stock | F1-score | 56.61 | #16 of 17 | Archive leaderboard | report |
| Stock Market Prediction | Astock | HAN Stock | Precision | 58.41 | #16 of 17 | Archive leaderboard | report |
| Stock Market Prediction | Astock | HAN Stock | Recall | 57.20 | #16 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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