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 and 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, 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.

Financial misinformation poses a significant threat to investment decisions and market stability. Recently, the application of Large Language Models (LLMs) for detecting financial misinformation has gained considerable attention within the natural language processing (NLP) community. The Financial Misinformation Detection (FMD) challenge @ Coling 2025 serves as a valuable platform for collaboration and innovation. This paper presents our solution to FMD challenge. Our approach involves using search engines to retrieve the summarized high-quality information as supporting evidence and designing a financial domain-specific chain-of-thought to enhance the reasoning capabilities of LLMs. We evaluated our method on both commercial closed-source LLMs (GPT-family) and open-source models (Llama-3.1-8B and QWen). The experimental results domonstrate that the proposed method improves veracity prediction performance. However, the quality of the generated explanations remains relatively poor. In the paper, we present the experimental findings and provides an in depth analysis of these results.