arXiv:2605.27887v1 Announce Type: new Abstract: LLMs have shown strong performance across diverse financial tasks, yet portfolio management (PM), a critical financial decision-making task, remains poorly benchmarked. Existing benchmarks exhibit two main gaps: they ignore cross-asset correlation structures, thereby failing to distinguish genuinely diversified portfolios from concentrated ones, and fail to evaluate the complete PM decision pipeline in real-world scenarios. We introduce PortBench, a benchmark spanning six heterogeneous asset classes over ten years. PortBench consists of two compl
LLMs have demonstrated significant capabilities in various financial tasks, pushing the need for more robust and critical benchmarking in specialized domains like portfolio management.
This benchmark addresses key limitations in evaluating LLM performance for portfolio management by considering cross-asset correlations and real-world scenarios, which is crucial for the responsible deployment of AI in finance.
The introduction of PortBench provides a standardized and more comprehensive method for assessing LLMs in portfolio management, enabling better differentiation of effective models and accelerating their real-world application in finance.
- · AI developers
- · Financial institutions
- · Quantitative analysts
- · Early adopters of AI in finance
- · Ineffective AI models
- · Traditional portfolio managers (without AI tools)
Improved performance and accuracy of LLM-driven portfolio management systems due to better benchmarking.
Increased adoption of AI in complex financial decision-making, potentially leading to more efficient markets.
The benchmark could become a widely accepted industry standard, fostering a competitive ecosystem for financial AI model development.
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Read at arXiv cs.AI