Enhancing stock timing predictions based on multimodal architecture: Leveraging large language models (LLMs) for text quality improvement
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) like GPT-4 improve stock timing predictions by filtering online investor comments. A multimodal approach integrating analyzed comments with financial data enhances forecasting accuracy.
Area Of Science
- Financial Technology
- Natural Language Processing
- Computational Finance
Background
- Online investor sentiment analysis faces challenges with data quality, redundancy, and authenticity.
- Traditional quantitative methods often overlook qualitative insights from social media.
- Accurate stock timing predictions are crucial for investment decision-making.
Purpose Of The Study
- To enhance stock timing predictions using large language models (LLMs) for analyzing online investor comments.
- To develop and evaluate a multimodal architecture integrating LLM-processed sentiment with financial data.
- To assess the efficacy of GPT-4 in filtering and analyzing unstructured financial commentary.
Main Methods
- Utilized GPT-4 to filter and analyze investor comments from Chinese bank data.
- Developed a multimodal architecture combining filtered comment data with stock prices and technical indicators.
- Compared GPT-4 filtering against four baseline models and evaluated performance using financial metrics.
Main Results
- GPT-4 significantly improved key financial metrics, including profit-loss ratio, win rate, and excess return rate.
- The proposed multimodal architecture outperformed baseline models in stock timing prediction.
- Effective preprocessing of comment data by LLMs enhanced the integration with quantitative financial information.
Conclusions
- Large language models, particularly GPT-4, offer a powerful tool for improving financial forecasting accuracy.
- The multimodal architecture provides a robust framework for integrating qualitative sentiment data with quantitative financial analysis.
- The methodology demonstrates potential for broader application in diverse financial markets, aiding investor decision support.
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