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

Yuxuan Zhao, Sijia Chen and Ningxin Su

Papers from arXiv.org

Abstract: Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM) remains poorly evaluated. Existing benchmarks exhibit two gaps: they are often equity-only and ignore cross-asset correlations; they provide limited insight into where errors arise and how they affect subsequent decisions. We introduce PortBench, a benchmark with market data spanning six 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 portfolio evaluation. Dynamic evaluation uses a class-level correlation score for allocation and CEPS to summarize reference-based stage scores. We further evaluate under three stress windows and three risk profiles, and support real-time evaluation on newly arriving market data. Evaluating ten LLMs reveals a disconnect: high financial QA scores do not ensure consistent portfolio outperformance. LLMs concentrate exposure in correlated assets, and their portfolios achieve higher Sharpe than equal weighting in 54.2\% of 120 evaluations across four market periods. Our source code is available at \href{https://github.com/AgenticFinLab/portbench}{this https URL}.

Date: 2026-05, Revised 2026-10
New Economics Papers: this item is included in nep-rmg
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