PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management

01 摘要

Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM) remains poorly benchmarked. Existing benchmarks exhibit two gaps: they are often equity-only and ignore cross-asset correlations; they fail to evaluate the complete PM decision pipeline. We introduce PortBench, a benchmark spanning six heterogeneous asset classes from 2015 to 2025. PortBench comprises a static QA dataset of 6,269 questions across seven task templates and a dynamic five-stage allocation pipeline. To evaluate these layers, we introduce two metrics: a dual-layer correlation score for inter-class hedging and intra-class concentration, and CEPS, which quantifies how reasoning errors compound across pipeline stages. We further evaluate under three stress windows and three risk profiles, and support real-time evaluation to mitigate pretraining contamination on historical markets. Across ten frontier LLMs, strong financial QA performance fails to translate into superior portfolio performance: only 32.5\% of 120 evaluations beat equal weighting on Sharpe across four market periods. Our source code is available at \href{https://github.com/AgenticFinLab/por

02 关键点

1. Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM) remains poorly benchmarked.
2. Existing benchmarks exhibit two gaps: they are often equity-only and ignore cross-asset correlations; they fail to evaluate the complete PM decision pipeline.
3. We introduce PortBench, a benchmark spanning six heterogeneous asset classes from 2015 to 2025.