Ploutos: Towards interpretable stock movement prediction with financial
  large language model
Hanshuang Tong Jun Li Ning Wu Ming Gong Dongmei Zhang Qi Zhang
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.