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What Language Models Add to Healthcare-Equity Rankings, and What They Could Have Known — arXiv:2610.38056

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.38056
License (as archived): CC0-1.0
Archived: 2026-11-01T02:58:46.532Z
SHA-256 of the archived text: 0fc9b7a394d2a7ed12d4d6a29e79e992eec74ce4c152c429a4ac0fb1a004c3c9
Stable URL of this page: https://metraipolis.com/documento/dc_ef36e2d2f0ee

How to cite

What Language Models Add to Healthcare-Equity Rankings, and What They Could Have Known — arXiv:2610.38056 — arXiv (en), https://arxiv.org/abs/2610.38056 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ef36e2d2f0ee (Metraipolis archive dc_ef36e2d2f0ee, sha256 0fc9b7a394d2a7ed12d4d6a29e79e992eec74ce4c152c429a4ac0fb1a004c3c9)

BibTeX

@misc{dcef36e2d2f0ee,
  title = {What Language Models Add to Healthcare-Equity Rankings, and What They Could Have Known — arXiv:2610.38056},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.38056},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ef36e2d2f0ee; sha256 0fc9b7a394d2a7ed12d4d6a29e79e992eec74ce4c152c429a4ac0fb1a004c3c9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.38056. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In healthcare equities, much of what moves a single name arrives as text: filings, trial and regulatory records, news and analyst commentary. We evaluate how screened non-market features and language-model representations improve market-based equity rankings for US healthcare firms over 2020-2025. Adding balance-sheet and attention features improves the resulting hybrid's price- and liquidity-neutral rank information coefficient (IC) at one, three and twelve months. At twelve months, neutral IC rises from 0.063 to 0.093, a 48% increase, while raw IC rises from 0.289 to 0.298. Language models contribute most clearly at three months: among company-months with scientific-text coverage, a current embedding model raises the hybrid's neutral IC from 0.046 to 0.056. This increment is nominally significant (t = 2.8; paired 95% bootstrap interval [+0.006, +0.015]) and remains exploratory. A larger post hoc gain occurs in months containing trial or regulatory records, consistent with text complementing prices and financial statements at that horizon. Across all text, pre-2020 and current representations yield small positive three-month increments in both neutral and raw IC but no established twelve-month gain. Much of their ranking performance survives within-company time shuffling, consistent with a persistent company component. Rater-cutoff comparisons find negligible pre-cutoff advantages for current models over the pre-2020 rater at one and three months that do not shrink after the cutoffs; twelve-month hindsight attribution remains inconclusive. At twelve months, balance-sheet signals lead after rallies and in volatile markets, while attention leads after declines. Retrospective state-dependent blending improves on a static blend without an established advantage over the full hybrid. Results apply to a survivor-only sector sample and remain unadjusted for the full exploratory search.

Tema degli esploratori: economia dei servizi IA (elenco arXiv q-fin.CP, nuovi lavori del giorno).
Categorie arXiv: Computational Finance (q-fin.CP).
Pagina del lavoro: https://arxiv.org/abs/2610.38056 — 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 2026-11-01T02:24:43.878Z dalla pagina https://arxiv.org/list/q-fin.CP/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).