2024/12/24 by Haohang Li, Yupeng Cao, Li, Haohang +26 · 14 citations
Business, Management and Accounting · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Computational Engineering #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #FinTech, Crowdfunding, Digital Finance #Finance #Financial Distress and Bankruptcy Prediction #Insurance and Financial Risk Management #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2412.18174
openalex publication_date 2024/12/24 · openalex created_date 2024/12/26 · openalex updated_date 2026/07/28
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 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.