Summary of Enhancing Few-shot Stock Trend Prediction with Large Language Models, by Yiqi Deng et al.
Enhancing Few-Shot Stock Trend Prediction with Large Language Models
by Yiqi Deng, Xingwei He, Jiahao Hu, Siu-Ming Yiu
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed model leverages Large Language Models (LLMs) to predict stock trends in a few-shot setting, addressing the scarcity of labeled data. By introducing an ‘Irrelevant’ category and predicting individual news instead of merged news, the denoising-then-voting approach removes noise and mitigates input length limits. This method achieves high accuracy in predicting S&P 500, CSI-100, and HK stock trends, outperforming standard few-shot counterparts by around 7%, 4%, and 4%. The proposed model also performs on par with state-of-the-art supervised methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to help investors make informed decisions by predicting future market movements. Instead of relying on labeled data, it uses Large Language Models (LLMs) to forecast stock trends. This approach is faster and cheaper than traditional methods. The proposed method removes noise from news articles and limits the amount of information LLMs need to process. As a result, it can accurately predict stock prices for several major markets. |
Keywords
» Artificial intelligence » Few shot » Supervised
