FinBoardBench: Benchmarking Dynamic Wealth Management and Strategic Financial Reasoning of LLMs via Board Game Simulations
Authors: Xuesi Hu, Peng Wang, Jinpeng Miao, Xilin Tao, Caiwei Li, Yue Ma, Jie He, Qiancheng Zhang, +2 more
Organizations: School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China · School of Economics, Anhui University, Anhui, China · SKLPlanets, Macau University of Science and Technology, Macau, China · Department of Computer and Information Science, University of Macau, Macau, China · School of Energy and Power Engineering, Huazhong University of Science and Technology, Hubei, China
Recently, large language models (LLMs) have achieved superior performance in static financial reasoning and simple dynamic trading tasks. However, existing static financial benchmarks are insufficient to assess the dynamic wealth management and financial decision-making capabilities of LLMs in real-world environments. To bridge this gap, we present FinBoardBench, an evaluation suite based on three classic financial board games: Cashflow, Acquire, and Monopoly. FinBoardBench assesses a comprehensive set of financial skills, including personal cash flow management with debt balancing, corporate investment and acquisition forecasting, and competitive trade negotiations with asset auctions. Our experiments with 9 advanced LLMs reveal that while exhibiting basic long-term planning and investment logic, they fail to effectively leverage complex interactions for profit, and their strong static reasoning performance does not transform into successful dynamic decision-making. Notably, they tend to prioritize immediate asset acquisition over maintaining sufficient liquidity, making them vulnerable to financial crises triggered by random events. We hope that FinBoardBench can provide a valuable reference for more intelligent LLM-based decision-making systems in the future.