Ploutos: Towards interpretable stock movement prediction with financial large language model

Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang
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Abstract

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.
Ploutos:利用金融大型语言模型实现可解释的股票走势预测
大型语言模型(LLM)的最新进展为许多领域开辟了新的途径。然而,LLMs 在金融投资领域的全部潜力在很大程度上仍未得到开发。基于深度学习的典型量化金融方法面临两大挑战。其次,传统方法缺乏清晰度和可解释性,这阻碍了它们在预测理由至关重要的场景中的应用。为了解决上述难题,我们提出了 Ploutos,一个由 PloutosGen 和 PloutosGPT 组成的新型金融LLM 框架。PloutosGen 包含多个初级专家,他们可以分析不同的模态数据,如文本和数字,并从不同角度提供量化策略。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、忠实的理由,PloutosGPT 的训练策略利用后视镜提示机制引导 GPT-4 生成理由,并利用动态标记加权机制通过增加关键标记的权重对 LLM 进行微调。广泛的实验表明,我们的框架在预测准确性和可解释性方面都优于最先进的方法。
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