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Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis — arXiv:2609.32746

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.32746
License (as archived): CC0-1.0
Archived: 2026-10-07T20:53:54.039Z
SHA-256 of the archived text: 53d674a96153ffcf3ac696be9574fa927c931fb0046689f6b4f8a7a2406f9aff
Stable URL of this page: https://metraipolis.com/documento/dc_6ff39643fd89

How to cite

Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis — arXiv:2609.32746 — arXiv (en), https://arxiv.org/abs/2609.32746 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6ff39643fd89 (Metraipolis archive dc_6ff39643fd89, sha256 53d674a96153ffcf3ac696be9574fa927c931fb0046689f6b4f8a7a2406f9aff)

BibTeX

@misc{dc6ff39643fd89,
  title = {Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis — arXiv:2609.32746},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.32746},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6ff39643fd89; sha256 53d674a96153ffcf3ac696be9574fa927c931fb0046689f6b4f8a7a2406f9aff},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.32746. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several limitations. Whereas the natural sciences provide objectively correct relationships, financial valuation constitutes a distinct class of symbolic discovery problems, as it admits multiple valid perspectives, operates under non-stationary market conditions, and involves noisy, continuous performance signals. In this work, we propose Multi-Agent Fundamental Analysis with Symbolic Adaptive learning (MUFASA), a hierarchical multi-agent framework for symbolic discovery in finance. MUFASA introduces (1) disentangled equation discovery via specialized agents representing distinct valuation perspectives, (2) a meta-coordinator that performs hierarchical-level reasoning over market context information, and (3) a memory mechanism that reasons over statistical performance summaries (e.g., accuracy, stability, and tail risk) to guide learning under noisy feedback. Experiments across datasets from multiple countries show that MUFASA achieves state-of-the-art performance on the valuation task compared to classical finance methods, financial large language models, and SR approaches, while simultaneously producing interpretable equations, which we share with the community. We also make publicly available the distilled learnings across evolution iterations and context-dependent strategy weights, which might offer useful insights for future research on financial fundamental analysis.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.MA).
Categorie arXiv: Multiagent Systems (cs.MA) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computational Finance (q-fin.CP).
Pagina del lavoro: https://arxiv.org/abs/2609.32746 — 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 2026-10-07T20:24:41.611Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).