1# NOVELTY_MEMO.md — the three closest papers and why WP39 is not them23Prepared 2026-11-02 from publisher metadata verified on Crossref (`literature/RELATED_WORK_MATRIX.csv`,4`artifacts/logs/crossref_batch*.json`). Full texts were not machine-summarised; the points of distinction below are5based on the papers' stated designs as reported in their abstracts and our own reading of their published versions.67## Closest paper 1 — Grégoire & Martineau (2022), *How Is Earnings News Transmitted to Stock Prices?*, JAR 60(1)8- **What it does.** Decomposes price discovery around earnings announcements using TAQ quotes *and* trades, showing that9 most of the reaction to after-hours announcements occurs in quotes with little trading.10- **Strongest objection to WP39 from this paper.** "Information arrives at the announcement; what you call anticipation is11 the pre-announcement drift already documented, and bars cannot even see quotes." 12- **How WP39 differs.** WP39 does not study the announcement reaction at all: all features end strictly *before* the13 release and the outcome is the accounting number itself, not the return. We make no claim about quotes versus trades14 (our data are OHLCV bars, stated plainly) and we ask a forecasting question — whether the *resolution* of pre-release15 bar data carries incremental information about fundamentals — which their event-window decomposition does not address.1617## Closest paper 2 — Chen, Cho, Dou & Lev (2022), *Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data*, JAR 60(2)18- **What it does.** Shows that gradient-boosted trees on detailed XBRL financial-statement items predict the direction of19 next-year earnings changes better than parsimonious accounting models, and that the predictions are priced slowly.20- **Strongest objection.** "Fundamentals-based ML already predicts earnings changes; adding market data is Ou–Penman with21 a new model, and daily returns are known to lead earnings (Beaver–Lambert–Morse 1980; Kothari–Sloan 1992)."22- **How WP39 differs.** Their inputs are accounting items; ours hold the accounting information set fixed (B1) and23 measure the *marginal* value of market data at four sampling frequencies with equal model capacity (B2–B5, B6). The24 stock-prices-lead-earnings literature establishes the daily/annual lead; WP39 tests whether anything remains at the25 minute scale once daily information is in, with a strict PIT availability clock and a release-date reveal clock.2627## Closest paper 3 — Zhang, Zhang, Cucuringu & Qian (2023/2024), *Volatility Forecasting with Machine Learning and Intraday Commonality*, JFEC28- **What it does.** Pools intraday realized-volatility features across many stocks to forecast volatility with ML, showing29 that commonality in intraday patterns improves volatility forecasts.30- **Strongest objection.** "Intraday features forecast volatility, and earnings surprises are volatility events, so your31 deterioration signal is a volatility forecast in disguise."32- **How WP39 differs.** Our target is an accounting quantity with a *sign* (adverse surprise), not a variance; the design33 includes the volatility families explicitly in every frequency set so that the test isolates information beyond34 predicted volatility (placebo: a symmetric |surprise| target must show the pattern a volatility channel implies, a signed35 target must not). We also report directional asymmetry (negative semivariance, down-bar volume) as pre-specified36 mechanism variables, not as the headline.3738## Other adjacent work and the recycling check39- UQO WP15 (*Faster, Wider, More Fragile?*) studies intraday *shock propagation* across stocks on the same vendor data;40 it never links bars to accounting outcomes. WP39 shares the data vendor and the realized-measure toolkit only; no41 result or figure is reused.42- Pre-announcement informed trading (Christophe–Ferri–Angel 2004; Berkman et al. 2009; Kaniel et al. 2012;43 Hendershott–Livdan–Schürhoff 2015) uses trader-type data we do not have; our contribution is orthogonal: whether44 *anonymous* OHLCV at fine resolution is informative, and whether the information is attributable to the frequency.45- Patton & Verardo (2012) and Bollerslev–Li–Xue (2018) model announcement-day betas and volume-volatility; they are46 announcement-window studies.47- Earnings-prediction ML (Easton et al. 2024; Cao et al. 2024) uses accounting, analyst and text inputs, not intraday bars.4849## Verdict50No published paper, to our verified knowledge, measures the marginal forecasting value of 1-minute versus 5-, 30-minute51and daily OHLCV for *forthcoming accounting deterioration* with a point-in-time information set and matched model52capacity. The question is therefore open. If the empirical answer is a precise null, that is itself informative given53the practitioner belief that high-frequency activity "sees" fundamentals early.54