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In this paper, we propose FinVis-GPT, a novel multimodal large language model(LLM) specifically designed for financial chart analysis. By leveraging thepower of LLMs and incorporating instruction tuning and multimodal capabilities,FinVis-GPT is capable of interpreting financial charts and providing valuableanalysis. To train FinVis-GPT, a financial task oriented dataset was generatedfor pre-training alignment and instruction tuning, comprising various types offinancial charts and their corresponding descriptions. We evaluate the modelperformance via several case studies due to the time limit, and the promisingresults demonstrated that FinVis-GPT is superior in various financial chartrelated tasks, including generating descriptions, answering questions andpredicting future market trends, surpassing existing state-of-the-artmultimodal LLMs. The proposed FinVis-GPT serves as a pioneering effort inutilizing multimodal LLMs in the finance domain and our generated dataset willbe release for public use in the near future to speedup related research.

FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis