Constructing a Portfolio Optimization Benchmark Framework for Evaluating Large Language Models
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摘要
This paper proposes a benchmark framework that generates 9,500 mathematically well-defined portfolio-optimization multiple-choice problems to evaluate LLMs' quantitative decision-making in asset allocation, comparing GPT-4, Gemini 1.5 Pro, and Llama 3.1-70B across objectives (variance, return, Sharpe, MDD, CVaR), constraint types, and distractor-generation methods, and finds model-specific strengths (GPT: risk objectives; Gemini: return-focused) and shared limitations on multi-criteria optimization such as Sharpe and CVaR [page::0][page::5].
