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.

Large language models (LLMs) fine-tuned on multimodal financial data have demonstrated impressive reasoning capabilities in various financial tasks. However, they often struggle with multi-step, goal-oriented scenarios in interactive financial markets, such as trading, where complex agentic approaches are required to improve decision-making. To address this, we propose FLAG-Trader, a unified architecture integrating linguistic processing (via LLMs) with gradient-driven reinforcement learning (RL) policy optimization, in which a partially fine-tuned LLM acts as the policy network, leveraging pre-trained knowledge while adapting to the financial domain through parameter-efficient fine-tuning. Through policy gradient optimization driven by trading rewards, our framework not only enhances LLM performance in trading but also improves results on other financial-domain tasks. We present extensive empirical evidence to validate these enhancements.

Despite the promise of large language models based agent framework in stock trading task, their capabilities for comprehensive analysis and multiple different financial assets remain largely unexplored, such as cryptocurrency trading. To evaluate the capabilities of LLM-based agent framework in cryptocurrency trading, we introduce an LLMs-based financial shared task featured at COLING 2025 FinNLP-FNP-LLMFinLegal workshop, named Agent-based Single Cryptocurrency Trading Challenge. This challenge includes two cryptocurrencies: BitCoin and Ethereum. In this paper, we provide an overview of these tasks and datasets, summarize participants’ methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing cryptocurrency trading challenges. To the best of our knowledge, the Agent-based Single Cryptocurrency Trading Challenge is one of the first challenges for assessing LLMs in the financial area. In consequence, we provide detailed observations and take away conclusions for future development in this area.