FinVis-GPT: A Multimodal Large Language Model for Financial Chart
  Analysis
Ziao Wang Yuhang Li Junda Wu Jaehyeon Soon Xiaofeng Zhang
In this paper, we propose FinVis-GPT, a novel multimodal large language model
(LLM) specifically designed for financial chart analysis. By leveraging the
power of LLMs and incorporating instruction tuning and multimodal capabilities,
FinVis-GPT is capable of interpreting financial charts and providing valuable
analysis. To train FinVis-GPT, a financial task oriented dataset was generated
for pre-training alignment and instruction tuning, comprising various types of
financial charts and their corresponding descriptions. We evaluate the model
performance via several case studies due to the time limit, and the promising
results demonstrated that FinVis-GPT is superior in various financial chart
related tasks, including generating descriptions, answering questions and
predicting future market trends, surpassing existing state-of-the-art
multimodal LLMs. The proposed FinVis-GPT serves as a pioneering effort in
utilizing multimodal LLMs in the finance domain and our generated dataset will
be release for public use in the near future to speedup related research.