InvestorBench: Benchmark for LLM financial decision-making tasksIn recent research, agents based on large language models (LLM) have demonstrated excellent decision-making capabilities in complex and open environments, covering multiple application scenarios such as financial transactions (Zhang et al. (2024b); Guo et al. (2024); Eigner and Händler (2024); Wang et al. (2024)). However, for the specific transaction decision-making needs in the financial field, how to build a multi-modal LLM agent framework suitable for a variety of financial tasks still faces great challenges. This is mainly due to the high volatility and diversity of financial markets: intelligent agents not only need to capture high-timeliness core trading signals, but also continue to make high-quality decisions in an environment with mixed information modalities and rapidly changing market conditions.> **Figure 1: Schematic diagram of the overall architecture of the InvestorBench framework. **

InvestorBench: Benchmark for LLM financial decision-making tasksIn recent research, agents based on large language models (LLM) have demonstrated excellent decision-making capabilities in complex and open environments, covering multiple application scenarios such as financial transactions (Zhang et al. (2024b); Guo et al. (2024); Eigner and Händler (2024); Wang et al. (2024)). However, for the specific transaction decision-making needs in the financial field, how to build a multi-modal LLM agent framework suitable for a variety of financial tasks still faces great challenges. This is mainly due to the high volatility and diversity of financial markets: intelligent agents not only need to capture high-timeliness core trading signals, but also continue to make high-quality decisions in an environment with mixed information modalities and rapidly changing market conditions. Figure 1: Schematic diagram of the overall architecture of the InvestorBench framework. In addition, as application scenarios expand from a single target to multiple financial tasks, the influencing factors in financial decision-making will also become complex and diverse depending on the type of tasks. For example, individual stock trading needs to focus on company- and industry-level fundamentals and financial report data (Yi et al. (2022)); while cryptocurrency trading is more likely to be driven by real-time news…