Year: 2025
Type: conference-paper
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 explainability, 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 explainable rationales. To generate accurate and faithful rationales, the training strategy of PloutosGPT leverages a rearview-mirror prompting mechanism to guide GPT-4 to generate rationales and a dynamic token weighting mechanism to finetune LLM by detecting and emphasizing key tokens in rationales. Extensive experiments show our framework outperforms the state-of-the-art methods on both prediction accuracy and explainability.
Authors: Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4410636618