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AI Research
arXiv CS.AI·May 28·6 min read
PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management
TL;DR
Portfolio management systems struggle to evaluate large language models (LLMs) on realistic investment tasks because existing benchmarks ignore how asset prices move together (correlation). PortBench was built as a full-pipeline benchmark that incorporates correlation-aware data and end-to-end evaluation of LLM-driven portfolio decisions.
✦ Why It Matters
Engineers can now rigorously evaluate LLM-based portfolio systems against realistic market dynamics before deployment.
Key Takeaways
- Incorporate cross-asset correlation in portfolio evaluations.
- Utilize PortBench to benchmark LLMs in financial tasks.
- Assess model performance under historical stress conditions.
- Focus on avoiding catastrophic drawdowns in portfolio strategies.
How It Works
PortBench consists of two layers: a static QA dataset with correlation-based questions and a dynamic allocation pipeline. The static layer tests models on their understanding of asset correlations, while the dynamic layer simulates the full PM decision cycle, allowing for a comprehensive evaluation of model performance.