Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
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
- Use PortBench to test LLM portfolio systems on realistic correlated asset data, not isolated assets.
- Evaluate complete workflows from data ingestion through rebalancing, not individual decision steps.
- Measure LLM performance on actual portfolio construction tasks with correlation-aware metrics.
- Identify performance gaps in LLM investment strategies that simpler benchmarks would overlook.
Full Summary
Financial portfolio management requires selecting and weighting assets to balance risk and return. Large language models are increasingly applied to this domain, but evaluating their effectiveness has been limited by benchmarks that treat assets independently and don't test complete workflows.
PortBench addresses this gap by introducing a correlation-aware benchmark that models realistic market behavior where asset prices move together in patterns. The benchmark implements a full pipeline—from market data ingestion through portfolio construction, rebalancing decisions, and performance measurement—rather than isolated subtasks.
By incorporating asset correlation structures and end-to-end evaluation, PortBench enables researchers to measure whether LLMs can make sound portfolio decisions under realistic market conditions. This approach reveals performance gaps that simpler benchmarks would miss.