Summary of Fingpt: Enhancing Sentiment-based Stock Movement Prediction with Dissemination-aware and Context-enriched Llms, by Yixuan Liang et al.
FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs
by Yixuan Liang, Yuncong Liu, Boyu Zhang, Christina Dan Wang, Hongyang Yang
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Trading and Market Microstructure (q-fin.TR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to financial sentiment analysis, leveraging large language models (LLMs) to predict short-term stock price movements. The authors acknowledge the limitations of current methods, which only consider news content and neglect its dissemination. To address this, they incorporate news dissemination breadth, contextual data, and explicit instructions into LLM prompts. This leads to a 8% improvement in prediction accuracy compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict how stock prices will change based on what’s happening with companies in the news. Right now, most computers just look at the news articles themselves and don’t think about how many people are reading them or what they mean for the company’s future. The authors want to change this by adding more information to the computer’s instructions so it can better understand the news and make more accurate predictions. |
