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

arXiv:2403.00782v1 Announce Type: cross
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 …

abstract arxiv challenges cs.ai cs.cl deep learning domains finance financial investments language language model language models large language large language model large language models llms prediction q-fin.st quantitative stock struggle type