Summary of Llmfactor: Extracting Profitable Factors Through Prompts For Explainable Stock Movement Prediction, by Meiyun Wang et al.
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
by Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 This paper introduces a novel framework called LLMFactor, which leverages Large Language Models (LLMs) to identify factors influencing stock movements. The approach employs Sequential Knowledge-Guided Prompting (SKGP) to extract factors related to stock market dynamics, providing clear explanations for complex temporal changes. Unlike previous methods relying on keyphrases or sentiment analysis, LLMFactor focuses on directly extracting factors affecting stock prices from news articles. The framework predicts stock movement by leveraging historical stock prices in textual format and background knowledge. Evaluation across four benchmark datasets from the U.S. and Chinese stock markets demonstrates LLMFactor’s superiority over existing state-of-the-art methods and its effectiveness in financial time-series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to use Large Language Models (LLMs) to predict how stock prices will change. Right now, these models are very good at understanding text, but they struggle when dealing with time-series data like stock prices. The new framework, called LLMFactor, uses a different approach that helps the model understand what’s important for predicting stock movements. Instead of just looking at words and sentiment, it looks at specific factors that affect stock prices, like news articles about companies. This makes the predictions more accurate and understandable. The paper tested this approach on four big datasets from the U.S. and China and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Prompting » Time series
