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Agent Policy-Value Audit: Separating Transition Composition from Event Selection in Financial LLM Agents — arXiv:2610.04040

Source: arXiv
Original: https://arxiv.org/abs/2610.04040
License (as archived): CC0-1.0
Archived: 2026-10-06T05:24:15.752Z
SHA-256 of the archived text: 582f4a51abd6e7e6f0d968d3f46c3d613df928cd23182cd5bef20d57ac8eccef
Stable URL of this page: https://metraipolis.com/documento/dc_3bb522bcd60b

How to cite

Agent Policy-Value Audit: Separating Transition Composition from Event Selection in Financial LLM Agents — arXiv:2610.04040 — arXiv, https://arxiv.org/abs/2610.04040 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3bb522bcd60b (Metraipolis archive dc_3bb522bcd60b, sha256 582f4a51abd6e7e6f0d968d3f46c3d613df928cd23182cd5bef20d57ac8eccef)

BibTeX

@misc{dc3bb522bcd60b,
  title = {Agent Policy-Value Audit: Separating Transition Composition from Event Selection in Financial LLM Agents — arXiv:2610.04040},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.04040},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3bb522bcd60b; sha256 582f4a51abd6e7e6f0d968d3f46c3d613df928cd23182cd5bef20d57ac8eccef},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.04040. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Financial LLM agents are often evaluated by comparing their end-to-end returns with those of a baseline and testing the paired difference against zero. This measures whether deploying the agent changes realized performance, but it does not isolate event-selection skill. An agent that frequently changes positions from flat to long can earn a positive paired return from an upward-drifting event pool even when it selects events at random. We propose the Agent Policy-Value Audit, which holds fixed the observed count of each ordered action-change type and randomly reassigns them across eligible events. The average payoff from these reassignments is the composition benchmark; the difference between observed deployment value and this benchmark is selection value. In semi-synthetic benchmarks based on real earnings-event returns, a zero-centered paired test falsely attributes passive exposure to selection skill in $11.6\%$ of no-skill replications, while the transition-matched audit reduces this rate to $5.3\%$. Applied retrospectively to 723 earnings events at 44 U.S. consumer-facing firms, the audit decomposes the agent's gross deployment value of $+15.2$ bps/event into a $+25.8$ composition benchmark and a $-10.6$ selection value. The agent does not detectably outperform matched random assignments. Financial-agent evaluations should report deployment value separately from event-selection value.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Trading and Market Microstructure (q-fin.TR).
Pagina del lavoro: https://arxiv.org/abs/2610.04040 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:24:05.420Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).