# 🕰️ The Invisible Balance Sheet **UQO Working Paper No. 39** — *Can Intraday Financial Markets Detect Corporate Deterioration Before Accounting Disclosures?* [-b31b1b?logo=latex&logoColor=white)](paper/main.pdf) [](pyproject.toml) [](DATA_AUDIT.md) [](docs/REPLICATION.md) [](https://uqo.ca) [](mailto:simon-pierre.boucher@uqo.ca) > **TL;DR** — Do one-minute markets see a firm's forthcoming accounting deterioration before daily markets do? On a > strictly point-in-time panel of 3,796 U.S. firms and 102,522 firm-quarters (2013–2026) built from 6.7 billion raw > 1-minute bars and quarterly statements with release dates, we forecast an adverse operating-income surprise 20 trading > days before the earnings release with nested information sets of equal capacity. Fundamentals do most of the work, > daily OHLCV adds a clear increment (Δ log loss −0.0068 with boosted trees), and 1-minute families add nothing: > +0.0013 [−0.0013, +0.0041] (logistic) and +0.0016 [+0.0010, +0.0023] (trees) on an untouched 2021–2026 test period. > The null holds at five horizons, for five accounting targets, a volatility placebo, unseen firms, every refit year and > every subgroup; the signal is already in prices at the cutoff and has no factor-adjusted economic value. > **A precise null**: the minute-by-minute tape adds noise, not foresight — on survivors (see Limitations). --- ## 📌 Status | Item | Value | |---|---| | Version | **1.0 (November 2026)** — built autonomously on 2026-11-02 under the owner's brief (`CLAUDE.md`); phases 0–7 complete | | SSRN | not yet submitted | | Journal | target: JAR / JFE / RFS (finance–accounting interface); internal referee reports in `docs/REFEREE_REPORTS.md` list the v1.1 revisions | | Code & data | reproducible from `scripts/` against FinData OS (private FirstRate Data lakehouse); raw records not redistributed | ## 🎯 What this paper does Anticipation is not the question — prices lead earnings at daily horizons and we reproduce it. The question is the **marginal information value of sampling frequency**: holding the universe, calendar, cutoff, horizon, feature families and tuning budget fixed, do 30-minute, 5-minute and 1-minute OHLCV bars add forecasting content about the accounting outcome beyond point-in-time fundamentals and daily bars? Targets are accounting surprises against a seasonal random walk with drift (never prices); the reveal date is the earnings press release; the information set at cutoff `c` holds bars with session ≤ `c` and facts with `available_at ≤ c` only, with twelve leakage guards as unit tests. ``` S_{i,q} = (X_{i,q} − E_c[X_{i,q}]) / TA_{i,q−1}, D_{i,q} = 1[S_{i,q} ≤ −0.5 %] E_c[X_q] = X_{q−4} + ¼ Σ_{k=0..3} (X_{q−1−k} − X_{q−5−k}) (quarters available at c only) ``` ## 🏆 Headline results (test period 2021–2026, h = 20, N = 53,667) | Object | Estimate | |---|---| | Base rate of adverse surprise | 31.5 % | | Log loss: base rate / fundamentals / + daily (LightGBM) | 0.628 / 0.494 / 0.487 (AUC 0.797 / 0.803) | | Daily increment LB2 − LB1 | −0.0068 [−0.0095, −0.0042] | | **1-minute increment** B5 − B2 (logistic) | **+0.0013 [−0.0013, +0.0041]**, GW p = 0.52 | | **1-minute increment** LB5 − LB2 (LightGBM) | **+0.0016 [+0.0010, +0.0023]** (a deterioration) | | 30-min / 5-min increments (LightGBM) | +0.0017 / +0.0021 | | Pre-registered MDE (80 % power) | 0.0025 | | Horizon gradient (H4) | none (Spearman −0.5, p ≈ 0.35) | | Panel: surprise on LFDS_B2 / incremental LFDS_B5−B2 | β = −0.0765 (t = −32) / −0.0129 (t = −1.8) | | Announcement CAR on LFDS_B5 | β ≈ 0 (t = 0.01): signal already in prices | | L/S decile alpha (FF5+MOM), LB5 scores | −0.59 %/mo (t = −1.85) gross, −0.62 % net of 20 bps | ## 🗂 Repository layout ``` invisible-balance-sheet/ # project id wp39_uqo ├── CLAUDE.md · README.md · PROGRESS.md · DECISIONS.md · DATA_AUDIT.md · LIMITATIONS.md ├── configs/ research.yaml (frozen design) · data_contract.yaml · models.yaml ├── docs/ RESEARCH_DESIGN.md · PIT_PROTOCOL.md · NOVELTY_MEMO.md · API_INTEGRATION.md · COMPUTE_BUDGET.md · REPLICATION.md · REFEREE_REPORTS.md ├── literature/ RELATED_WORK_MATRIX.csv · references.bib (94 entries verified on Crossref) ├── src/wp39/ api_client · provenance · calendar · instruments · pit_fundamentals · corporate_actions · ingest · features · labels · samples · splits · models · inference · portfolio · robustness · figures · pipeline ├── scripts/ 00_probe … 09_make_manuscript (+ 01a node extracts) ├── tests/ 29 tests (timestamps/DST, PIT leakage, labels, splits, train-only transforms, corporate actions, ids, reproducibility, API contract incl. live as-of equivalence, features) ├── artifacts/ manifests (hashes, dataset versions, design freeze) · evaluation (csv/json) · logs ├── results/ audits, cascades, metrics, contrasts, regressions ├── paper/ main.tex · sections/ · tables/ (generated) · figures/ (generated) · results_macros.tex (206 macros) · main.pdf └── data/ README only (no licensed records committed) ``` ## 🚀 Quick start ```bash uv venv -p 3.12 .venv && uv pip install -e ".[dev]" export FINDATA_BASE_URL=http://127.0.0.1:8600/api/v1 WP39_CACHE=~/wp39-cache # on the FinData node bash scripts/01a_extract_node_tables.sh && for s in 00 01 02 03 04 05 06 07 08 09; do .venv/bin/python scripts/${s}_*.py; done pytest -q # WP39_LIVE=1 adds the live API tests cd paper && make # latexmk → main.pdf ``` Measured wall-times (Mac Studio M3 Ultra, 28 cores): universe audit 17 min (SEC), PIT panel 8 min, features 58 min (8 workers), sample 30 s, baselines 30 s, LightGBM 29 min, evaluation 1 min, robustness 25 s, manuscript 10 s. ## 🗃 Data FinData OS (`findata.spboucher.ai/api/v1`), the owner's private lakehouse of FirstRate Data: raw 1-minute bars (2000–2026, 16,195 symbols incl. 8,069 delisted), quarterly fundamentals (5,451 entities, 67 metrics, filing dates 2012–2026), 364,783 earnings-release events (node extract), splits/dividends (node extract). Public: SEC `company_tickers.json` + `submissions` (CIK/SIC), Kenneth French daily factors. Universe cascade: 5,451 → 5,018 (≥ 8 quarters) → 4,568 (1-minute coverage); event sample 481,159 observations after admissibility and exclusions (`results/table_sample_cascade_event.csv`). Full audit: `DATA_AUDIT.md`. ## 🔬 Methodology 1. Clocks and cutoffs (`samples.py`, `calendar.py`): `c = A_q − h`, h ∈ {40, 20, 10, 5, 1}; `c < min(A_q, F_q)`, `c > A_{q−1}`. 2. PIT fundamentals (`pit_fundamentals.py`): local as-of join on `available_at`, verified live against the API. 3. Labels (`labels.py`): seasonal-random-walk-with-drift surprises, primary and secondary targets, symmetric placebo. 4. Features (`features.py`): same 19 families from 1m/5m/30m buckets of the regular session, daily block, split/dividend-adjusted returns. 5. Ladder (`models.py`): B0–B5 logistic (C on validation), LB1–LB5 LightGBM (24-trial equal budget), ridge for the continuous target. 6. Validation (`splits.py`): chronological with purge/embargo, expanding-window annual refits, 20 % firm hold-out. 7. Inference (`inference.py`): block bootstrap by calendar quarter, Giacomini–White, Holm, two-way clustered panels. 8. Robustness and pricing (`robustness.py`, `portfolio.py`): shuffled labels, subgroups, CAR regressions, decile sorts with FF5+MOM and costs. ## 🖼 Figures & tables inventory | Figure | Content | |---|---| | `fig01_timeline` | event-time design and no-leakage rule (schematic) | | `fig02_coverage` | firms and adverse rate by year | | `fig03_prevalence` | conditional prevalence by size, illiquidity, leverage, release timing | | `fig04_frequency_gains` | Δ log loss vs B2 for 30m/5m/1m/all, logistic and LightGBM, 95 % block-bootstrap CIs | | `fig05_reliability` | calibration deciles | | `fig06_signal_evolution` | AUC and Δ by horizon (H4) | | `fig07_heterogeneity` | Δ by pre-specified subgroup (H3) | | `fig08_rolling` | annual refits: AUC and Δ by year | | `fig09_economic` | decile returns and alphas | Tables (`paper/tables/*.tex`): universe and sample cascades, summary statistics, information sets, headline metrics, paired contrasts, horizon ladder, secondary targets, firm hold-out, panel regressions, subgroups, placebo, continuous target, economic value, CAR regressions. ## ⚠️ Limitations - Accounting panel conditioned on firms listed in November 2026 (no delisted entities from the vendor): survivorship. - `available_at = filing_date + 1` is a vendor policy; all facts are revision 1 (current values): PIT proxy, no as-originally-reported history. - OHLCV bars only: no quotes, trades or order flow; bar statistics are not interpreted as informed trading. - Logistic-family CI (±0.0027) is comparable to the MDE (0.0025); the precise-null claim rests mainly on the boosted family (±0.0006). - No neural/text models (owner instruction to minimise AI use). Full list: `LIMITATIONS.md`; planned v1.1 revisions: `docs/REFEREE_REPORTS.md`. ## 📝 Citation ```bibtex @techreport{boucher2026invisible, author = {Boucher, Simon-Pierre}, title = {The Invisible Balance Sheet: Can Intraday Financial Markets Detect Corporate Deterioration Before Accounting Disclosures?}, institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives}, type = {Working Paper}, number = {39}, year = {2026}, month = {November} } ``` ## 👤 Author & contact **Simon-Pierre Boucher** — Département des sciences administratives, Université du Québec en Outaouais (UQO), Gatineau, QC. 📧 [simon-pierre.boucher@uqo.ca](mailto:simon-pierre.boucher@uqo.ca) ## 🗒 Notes - Built in one autonomous Claude Code session on 2026-11-02 (phases 0–7 of `CLAUDE.md`); gates requiring owner approval are recorded as self-approved under the autonomous mandate (DECISIONS D-010) and remain open to veto. - Directory name follows the series convention (`finance/`); project id and spbgit repository are `wp39_uqo`. - Data attribution: FirstRate Data via FinData OS (private license; no raw records in this repository).