Paper: arXiv 2610.38056

Authors: Haotong Fu, Xiyue Zhang, Jack Rose, Ding Sun, Erich Scheller, Eric Rose

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a robust empirical evaluation of LLMs in equity ranking, utilizing detailed statistical methods and a clear backtesting framework. While the mathematical derivations are not explicitly shown in the excerpt, the methodology implies a solid quantitative foundation. The novelty lies in the specific application and comparative analysis of LLMs within a hybrid ranking system for healthcare equities.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate LM Impact on Healthcare-Equity Rankings] --> B{Key Methodology Steps};
    B --> C[Data/Inputs: Screened Non-Market Features, LM Representations, Filings, Trial/Regulatory Records, News, Analyst Commentary, Balance-Sheet Data];
    C --> D[Computational Processes: Hybrid Model Development, Price- & Liquidity-Neutral Rank Information Coefficient (IC) Calculation, Embedding Models, State-Dependent Blending];
    D --> E{Key Findings/Outcomes};
    E --> F[Outcome 1: Balance-sheet & Attention Features improve hybrid's neutral IC (48% increase at 12m)];
    E --> G[Outcome 2: LMs contribute most at 3m (neutral IC from 0.046 to 0.056 for scientific-text coverage)];
    E --> H[Outcome 3: Larger post hoc gain in months with trial/regulatory records];
    E --> I[Outcome 4: Pre-2020 & current LMs yield small positive 3m IC increments, no established 12m gain];
    E --> J[Outcome 5: Balance-sheet signals lead after rallies/volatile markets; attention after declines];