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With the advancement of computational power, large language models (LLMs) have rapidly developed in various fields. However, predicting stock price fluctuations remains a significant challenge, mainly due to the following two aspects: To address these challenges, we propose a new framework. We independently trained two models, TechGPT and SentiGPT, to analyze stock price data and text data from community platforms, respectively. By combining the outputs of TechGPT and SentiGPT, we developed a comprehensive model named IntegraGPT. During the training data collection process, we used In-Context Learning to require multiple large language models to generate reasoning rationales, avoiding reliance on a single large language model for reasoning. This approach addresses the issues of insufficient interpretability and model prediction bias.

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Rationale-Driven Predictions for Stock Movements: A Multi-model Integration and Stack Generalization Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Chong-Yi Chong,
  • Hung-Yu Kao

摘要

With the advancement of computational power, large language models (LLMs) have rapidly developed in various fields. However, predicting stock price fluctuations remains a significant challenge, mainly due to the following two aspects: To address these challenges, we propose a new framework. We independently trained two models, TechGPT and SentiGPT, to analyze stock price data and text data from community platforms, respectively. By combining the outputs of TechGPT and SentiGPT, we developed a comprehensive model named IntegraGPT. During the training data collection process, we used In-Context Learning to require multiple large language models to generate reasoning rationales, avoiding reliance on a single large language model for reasoning. This approach addresses the issues of insufficient interpretability and model prediction bias.