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PortBench addresses a critical gap in AI finance by demonstrating that strong language capabilities do not automatically translate to superior investment performance across diverse asset classes. The study reveals that only 32.5% of frontier LLMs outperform simple equal weighting strategies when evaluated on a full pipeline involving cross-asset correlations and risk profiles. This benchmark provides essential metrics for assessing the reliability of agentic systems in real-world financial decision-making environments.
PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management
arXiv:2605.27887v4 Announce Type: replace Abstract: 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/portbench}{this https URL}.
Abstract: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{this https URL}{this https URL}.
Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM)
arXiv:2605.27887 [cs.AI]
(or arXiv:2605.27887v4 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.27887 Focus to learn more arXiv-issued DOI via DataCite
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