Recent advancements in large language models (LLMs) have opened new pathways
for many domains. However, the full potential of LLMs in financial investments
remains largely untapped. There are two main challenges for typical deep
learning-based methods for quantitative finance. First, they struggle to fuse
textual and numerical information flexibly for stock movement prediction.
Second, traditional methods lack clarity and interpretability, which impedes
their application in scenarios where the justification for predictions is
essential. To solve the above challenges, we propose Ploutos, a novel financial
LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen
contains multiple primary experts that can analyze different modal data, such
as text and numbers, and provide quantitative strategies from different
perspectives. Then PloutosGPT combines their insights and predictions and
generates interpretable rationales. To generate accurate and faithful
rationales, the training strategy of PloutosGPT leverage rearview-mirror
prompting mechanism to guide GPT-4 to generate rationales, and a dynamic token
weighting mechanism to finetune LLM by increasing key tokens weight. Extensive
experiments show our framework outperforms the state-of-the-art methods on both
prediction accuracy and interpretability.

在金融投资领域，大型语言模型 (Large Language Models，LLMs) 的完全潜力尚未得到充分利用。针对量化金融的典型深度学习方法存在两个主要挑战：一是难以灵活地融合文本和数值信息进行股票运动预测，二是传统方法缺乏清晰度和解释性，这阻碍了其在需要预测的理由至关重要的场景中的应用。为解决以上挑战，我们提出了一种名为 Ploutos 的新型金融 LLM 框架，其中包括 PloutosGen 和 PloutosGPT。PloutosGen 包含多个主要专家，可以分析不同模态数据，如文本和数字，并从不同的角度提供量化策略。然后，PloutosGPT 结合他们的见解和预测，生成可解释的理由。为了生成准确和可信的理由，PloutosGPT 的训练策略利用后视镜提示机制来引导 GPT-4 生成理由，并利用动态令牌加权机制通过增加关键令牌的权重来微调 LLM。广泛的实验证明，我们的框架在预测准确性和解释性方面优于最先进的方法。