| Autor: |
Li, Haohang, Cao, Yupeng, Yu, Yangyang, Javaji, Shashidhar Reddy, Deng, Zhiyang, He, Yueru, Jiang, Yuechen, Zhu, Zining, Subbalakshmi, Koduvayur, Xiong, Guojun, Huang, Jimin, Qian, Lingfei, Peng, Xueqing, Xie, Qianqian, Suchow, Jordan W. |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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