Recent advancements have underscored the potential of large language model (LLM)-based agents in financial decision-making. Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent datasets for assessing agent performance. To tackle these issues, we introduce \textsc{InvestorBench}, the first benchmark specifically designed for evaluating LLM-based agents in diverse financial decision-making contexts. InvestorBench enhances the versatility of LLM-enabled agents by providing a comprehensive suite of tasks applicable to different financial products, including single equities like stocks, cryptocurrencies and exchange-traded funds (ETFs). Additionally, we assess the reasoning and decision-making capabilities of our agent framework using thirteen different LLMs as backbone models, across various market environments and tasks. Furthermore, we have curated a diverse collection of open-source, multi-modal datasets and developed a comprehensive suite of environments for financial decision-making. This establishes a highly accessible platform for evaluating financial agents' performance across various scenarios.
@article{arxiv.2412.18174,
title = {INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent},
author = {Haohang Li and Yupeng Cao and Yangyang Yu and Shashidhar Reddy Javaji and Zhiyang Deng and Yueru He and Yuechen Jiang and Zining Zhu and Koduvayur Subbalakshmi and Guojun Xiong and Jimin Huang and Lingfei Qian and Xueqing Peng and Qianqian Xie and Jordan W. Suchow},
journal= {arXiv preprint arXiv:2412.18174},
year = {2024}
}