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.
Abstract (translated)
近年来，在大型语言模型（LLMs）领域的发展为许多领域带来了新的途径。然而，LLMs在金融投资领域的全部潜力仍然没有被充分发掘。对于典型的深度学习为基础的量化金融方法，有两种主要挑战。首先，它们在将文本和数值信息灵活融合以进行股票运动预测方面遇到困难。其次，传统方法缺乏清晰度和可解释性，这阻碍了它们在需要预测正当性的场景中的应用。为解决上述挑战，我们提出了Ploutos，一种新型的金融LLM框架，由PloutosGen和PloutosGPT组成。PloutosGen包含多个专家，可以从文本和数值等多种数据形式中分析数据，并提供不同角度的定量策略。然后，PloutosGPT结合它们的见解和预测，生成可解释的合理性。为了生成准确和忠实的合理性，PloutosGPT的训练策略利用了后视镜提示机制来指导GPT-4生成合理性，以及动态词重置机制，通过增加关键单词权重来微调LLM。大量实验证明，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。
URL
https://arxiv.org/abs/2403.00782
