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WP39 v1.0: full pipeline run (phases 2-7), results, figures, manuscript PDF, referee reports

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 2, 2026) parent 9c1ba37 Browse files

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modified .gitignore +7 −0
@@ -33,3 +33,10 @@ logs/
33 33
34 34 # macOS
35 35 .DS_Store
36 +
37 +# WP39 additions
38 +artifacts/evaluation/preds/
39 +artifacts/models/
40 +artifacts/logs/*.jsonl
41 +artifacts/logs/*.out
42 +*.pyc
modified LIMITATIONS.md +12 −0
@@ -29,3 +29,15 @@
29 29 ## To be completed as results arrive
30 30 - (Phase 3) achieved sample sizes, base rates, coverage gaps by year/size.
31 31 - (Phase 5) which placebo/stress tests failed or were inconclusive.
32 +
33 +## Completed from results (2026-11-02)
34 +- Achieved sample: 3,796 firms, 102,522 firm-quarters (2013–2026); the sample starts in 2013 because eight prior quarters
35 + are needed for the seasonal expectation; 2020 lies in the validation period, so the COVID subgroup is not part of the test.
36 +- The logistic-family confidence interval around the 1-minute increment (±0.0027) is comparable to, not narrower than,
37 + the pre-registered MDE (0.0025); the precise-null claim rests on the boosted family (±0.0006) and on the consistency of
38 + the sign across horizons, targets and subgroups.
39 +- The announcement-window CAR coefficient on the incremental 1-minute component is positive and nominally significant
40 + (t = 2.9); it is one of several H5 tests, has the "wrong" sign for an information story, and is reported, not exploited.
41 +- The placebo differential on shuffled labels is of the same order (−0.0004) as the primary LightGBM differential
42 + (+0.0016), so differences of this size should be read as zero.
43 +- Delisted-firm stress tests and as-originally-reported specifications were not run (data not available from the vendor).
modified PROGRESS.md +46 −0
@@ -20,3 +20,49 @@
20 20 - Owner review pending; nothing in Phases 2–5 depends on test-period data until the Phase 3 gate.
21 21
22 22 ## Phase 2 — Data acquisition and verification — IN PROGRESS
23 +- 2026-11-02 13:31 — `01a_extract_node_tables.sh`: entities 5,451 · earnings_events 364,783 · corporate_actions 249,340 (raw format
24 + `firstrate:stock:<SYM>` / `<SYM>_DIVS`, loader adapted). `00_probe`: health ok, 67 datasets, schema v1 ok, no earnings route.
25 +- 13:33–13:50 — `01_audit_universe`: 5,451 entities → 5,018 with ≥ 8 quarters → **4,568 eligible** (1m coverage ≥ 250 sessions);
26 + CIK matched 5,073, SIC 5,029 (SEC submissions fetched with a 6-thread, 8 req/s limiter after a sequential first attempt
27 + at ~1 req/s was restarted); 2,522 firms have former names (ticker-reuse audit input). `results/universe_audit.json`.
28 +- 13:50–14:01 — `02_build_pit_panel`: first run hit the API rate limit (HTTP 429, 600 req/min) with 6 unthrottled threads →
29 + client now has a process-wide token bucket (`WP39_RATE`, default 6 req/s) and honours `Retry-After`; rerun succeeded:
30 + 13,052,739 facts (all revision 1), **194,817 firm-quarters**, release match ok 172,328 (88.5 %), lag > 30 d 15,242,
31 + no release 6,020, filing before release 1,227; release timing after_close 76,752 / before_open 66,719 / unknown 51,346;
32 + base rates: adverse op-inc 31.2 %, adverse NI 32.7 %, ROA decline 29.4 %, OCF negative 25.9 %, liquidity 47.7 %,
33 + |S| ≥ 0.5 % 61.4 %; S_opinc quartiles −0.87 % / 0.00 % / +0.82 % (heavy tails, winsorised in continuous models).
34 + Design freeze hash written to `artifacts/manifests/design_freeze.json` before any model fit.
35 +- 14:01 — `03_build_features --workers 8` (WP39_RATE=1 per process) running: streaming export → features → windows.
36 +- 14:01–14:59 — `03_build_features`: 4,568/4,568 symbols ok, 0 errors (58 min, 8 workers, ≈1.3 symbols/s); raw exports
37 + deleted after use; `~/wp39-cache/features` 5.1 GB + `windows` 5.9 GB. `04_build_labels` (28 s): event sample
38 + **481,159** observations (3,796 firms, 102,522 firm-quarters; h = 20: 97,788, base rate 30.1 %), calendar sample 258,480
39 + firm-months; cascade in `results/table_sample_cascade_event.csv`. **Phase 2 gate passed**: zero known forward-looking
40 + features (tests + live as-of equivalence), coverage gaps explained (2013 start = 8 prior quarters required).
41 +
42 +## Phase 3 — Descriptives and baselines (2026-11-02 15:00–15:01) — GATE PASSED
43 +- `05_train_baselines` (30 s): B0–B5 logistic (C tuned on validation only), ridge continuous target, firm hold-out,
44 + walk-forward OOF 2016–2026 (86,791 rows per set). Validation log loss h=20: B1 0.5938 → B2 0.5691 → B3 0.5695 /
45 + B4 0.5688 / B5 0.5706. Test set opened only after this gate (results in Phase 4/5).
46 +
47 +## Phase 4 — Advanced models (15:01–15:30) — GATE PASSED
48 +- `06_train_advanced` (29 min): LightGBM, 24-trial equal budget per information set (LB1–LB5, LB5all), secondary targets,
49 + horizon ladder with a fixed configuration, firm hold-out, walk-forward OOF (event + calendar). Validation: LB1 0.5447 →
50 + LB2 0.5214 → LB3 0.5234 / LB4 0.5239 / LB5 0.5245 / all 0.5259.
51 +- `07_evaluate`: first run failed on a dtype mismatch (Int32/Int64 `fold`), fixed, rerun ok. **Test period 2021–2026,
52 + 53,667 obs**: Δ log loss B5−B2 = +0.0013 [−0.0013, +0.0041] (GW p = 0.52); LB5−LB2 = +0.0016 [+0.0010, +0.0023]
53 + (p = 0.0008, a deterioration); LB2−LB1 = −0.0068 [−0.0095, −0.0042]; AUC LB2 0.803, LB5 0.801. H1 and H2 rejected.
54 + H4: no horizon gradient (Spearman −0.5, p = 0.39/0.33). Secondary targets, firm hold-out, continuous target: same null.
55 + MDE (80 % power, 7 validation quarters): 0.0025.
56 +
57 +## Phase 5 — Robustness and interpretation (15:30–15:58)
58 +- `08_run_robustness`: first run stuck 28 min in a row-wise pandas loop (CAR window) → vectorised in Polars, rerun in 25 s.
59 + Placebo (shuffled labels, 3 draws): AUC 0.50, Δ ≈ −0.0004 (noise floor). Subgroups: no gain anywhere; deterioration
60 + smallest for small/illiquid/loss firms. Panel: LFDS_B2 β = −0.0765 (t = −32.2); incremental d_lfds β = −0.0129
61 + (t = −1.76); interactions opposite to H3 (less informative for small and low-leverage firms). H5: CAR on LFDS5 β ≈ 0
62 + (t = 0.01); d_lfds +0.060 (t = 2.9, sign opposite to a deterioration signal); portfolios L/S alpha −0.59 %/mo (t = −1.85)
63 + gross, −0.62 % net for LB5; −0.49 % (t = −1.64) for LB2 → no economic value.
64 +
65 +## Phase 6 — Manuscript (15:58–16:10)
66 +- `09_make_manuscript`: 15 LaTeX tables, 9 figures (validated palette), 206 macros in `paper/results_macros.tex`
67 + (fixes: NaN subgroup in figure, LaTeX escaping of math labels, Unicode in bib). `paper/main.pdf` compiled with
68 + `latexmk` on the laptop: 33 pages, 0 undefined references/citations.
added README.md +163 −0
@@ -0,0 +1,163 @@
1 +<!-- Author: Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca -->
2 +# 🕰️ The Invisible Balance Sheet
3 +
4 +**UQO Working Paper No. 39** — *Can Intraday Financial Markets Detect Corporate Deterioration Before Accounting Disclosures?*
5 +
6 +[![Paper](https://img.shields.io/badge/paper-PDF%20(33%20p.)-b31b1b?logo=latex&logoColor=white)](paper/main.pdf)
7 +[![Python](https://img.shields.io/badge/python-3.12%2B%20·%20uv-3776AB?logo=python&logoColor=white)](pyproject.toml)
8 +[![Data](https://img.shields.io/badge/data-FinData%20OS%20·%206.7%20bn%201--min%20bars%20·%204%2C568%20firms-4051b5)](DATA_AUDIT.md)
9 +[![Reproducible](https://img.shields.io/badge/reproducible-10%20scripts%20·%2029%20tests%20·%20frozen%20design-2e7d32)](docs/REPLICATION.md)
10 +[![Institution](https://img.shields.io/badge/UQO-D%C3%A9pt.%20des%20sciences%20administratives-16365c)](https://uqo.ca)
11 +[![Author](https://img.shields.io/badge/author-Simon--Pierre%20Boucher-16365c)](mailto:simon-pierre.boucher@uqo.ca)
12 +
13 +> **TL;DR** — Do one-minute markets see a firm's forthcoming accounting deterioration before daily markets do? On a
14 +> strictly point-in-time panel of 3,796 U.S. firms and 102,522 firm-quarters (2013–2026) built from 6.7 billion raw
15 +> 1-minute bars and quarterly statements with release dates, we forecast an adverse operating-income surprise 20 trading
16 +> days before the earnings release with nested information sets of equal capacity. Fundamentals do most of the work,
17 +> daily OHLCV adds a clear increment (Δ log loss −0.0068 with boosted trees), and 1-minute families add nothing:
18 +> +0.0013 [−0.0013, +0.0041] (logistic) and +0.0016 [+0.0010, +0.0023] (trees) on an untouched 2021–2026 test period.
19 +> The null holds at five horizons, for five accounting targets, a volatility placebo, unseen firms, every refit year and
20 +> every subgroup; the signal is already in prices at the cutoff and has no factor-adjusted economic value.
21 +> **A precise null**: the minute-by-minute tape adds noise, not foresight — on survivors (see Limitations).
22 +
23 +---
24 +
25 +## 📌 Status
26 +
27 +| Item | Value |
28 +|---|---|
29 +| Version | **1.0 (November 2026)** — built autonomously on 2026-11-02 under the owner's brief (`CLAUDE.md`); phases 0–7 complete |
30 +| SSRN | not yet submitted |
31 +| Journal | target: JAR / JFE / RFS (finance–accounting interface); internal referee reports in `docs/REFEREE_REPORTS.md` list the v1.1 revisions |
32 +| Code & data | reproducible from `scripts/` against FinData OS (private FirstRate Data lakehouse); raw records not redistributed |
33 +
34 +## 🎯 What this paper does
35 +
36 +Anticipation is not the question — prices lead earnings at daily horizons and we reproduce it. The question is the
37 +**marginal information value of sampling frequency**: holding the universe, calendar, cutoff, horizon, feature families
38 +and tuning budget fixed, do 30-minute, 5-minute and 1-minute OHLCV bars add forecasting content about the accounting
39 +outcome beyond point-in-time fundamentals and daily bars? Targets are accounting surprises against a seasonal random
40 +walk with drift (never prices); the reveal date is the earnings press release; the information set at cutoff `c` holds
41 +bars with session ≤ `c` and facts with `available_at ≤ c` only, with twelve leakage guards as unit tests.
42 +
43 +```
44 +S_{i,q} = (X_{i,q} − E_c[X_{i,q}]) / TA_{i,q−1}, D_{i,q} = 1[S_{i,q} ≤ −0.5 %]
45 +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)
46 +```
47 +
48 +## 🏆 Headline results (test period 2021–2026, h = 20, N = 53,667)
49 +
50 +| Object | Estimate |
51 +|---|---|
52 +| Base rate of adverse surprise | 31.5 % |
53 +| Log loss: base rate / fundamentals / + daily (LightGBM) | 0.628 / 0.494 / 0.487 (AUC 0.797 / 0.803) |
54 +| Daily increment LB2 − LB1 | −0.0068 [−0.0095, −0.0042] |
55 +| **1-minute increment** B5 − B2 (logistic) | **+0.0013 [−0.0013, +0.0041]**, GW p = 0.52 |
56 +| **1-minute increment** LB5 − LB2 (LightGBM) | **+0.0016 [+0.0010, +0.0023]** (a deterioration) |
57 +| 30-min / 5-min increments (LightGBM) | +0.0017 / +0.0021 |
58 +| Pre-registered MDE (80 % power) | 0.0025 |
59 +| Horizon gradient (H4) | none (Spearman −0.5, p ≈ 0.35) |
60 +| Panel: surprise on LFDS_B2 / incremental LFDS_B5−B2 | β = −0.0765 (t = −32) / −0.0129 (t = −1.8) |
61 +| Announcement CAR on LFDS_B5 | β ≈ 0 (t = 0.01): signal already in prices |
62 +| L/S decile alpha (FF5+MOM), LB5 scores | −0.59 %/mo (t = −1.85) gross, −0.62 % net of 20 bps |
63 +
64 +## 🗂 Repository layout
65 +
66 +```
67 +invisible-balance-sheet/ # project id wp39_uqo
68 +├── CLAUDE.md · README.md · PROGRESS.md · DECISIONS.md · DATA_AUDIT.md · LIMITATIONS.md
69 +├── configs/ research.yaml (frozen design) · data_contract.yaml · models.yaml
70 +├── docs/ RESEARCH_DESIGN.md · PIT_PROTOCOL.md · NOVELTY_MEMO.md · API_INTEGRATION.md · COMPUTE_BUDGET.md · REPLICATION.md · REFEREE_REPORTS.md
71 +├── literature/ RELATED_WORK_MATRIX.csv · references.bib (94 entries verified on Crossref)
72 +├── src/wp39/ api_client · provenance · calendar · instruments · pit_fundamentals · corporate_actions · ingest · features · labels · samples · splits · models · inference · portfolio · robustness · figures · pipeline
73 +├── scripts/ 00_probe … 09_make_manuscript (+ 01a node extracts)
74 +├── tests/ 29 tests (timestamps/DST, PIT leakage, labels, splits, train-only transforms, corporate actions, ids, reproducibility, API contract incl. live as-of equivalence, features)
75 +├── artifacts/ manifests (hashes, dataset versions, design freeze) · evaluation (csv/json) · logs
76 +├── results/ audits, cascades, metrics, contrasts, regressions
77 +├── paper/ main.tex · sections/ · tables/ (generated) · figures/ (generated) · results_macros.tex (206 macros) · main.pdf
78 +└── data/ README only (no licensed records committed)
79 +```
80 +
81 +## 🚀 Quick start
82 +
83 +```bash
84 +uv venv -p 3.12 .venv && uv pip install -e ".[dev]"
85 +export FINDATA_BASE_URL=http://127.0.0.1:8600/api/v1 WP39_CACHE=~/wp39-cache # on the FinData node
86 +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
87 +pytest -q # WP39_LIVE=1 adds the live API tests
88 +cd paper && make # latexmk → main.pdf
89 +```
90 +Measured wall-times (Mac Studio M3 Ultra, 28 cores): universe audit 17 min (SEC), PIT panel 8 min, features 58 min
91 +(8 workers), sample 30 s, baselines 30 s, LightGBM 29 min, evaluation 1 min, robustness 25 s, manuscript 10 s.
92 +
93 +## 🗃 Data
94 +
95 +FinData OS (`findata.spboucher.ai/api/v1`), the owner's private lakehouse of FirstRate Data: raw 1-minute bars
96 +(2000–2026, 16,195 symbols incl. 8,069 delisted), quarterly fundamentals (5,451 entities, 67 metrics, filing dates
97 +2012–2026), 364,783 earnings-release events (node extract), splits/dividends (node extract). Public: SEC
98 +`company_tickers.json` + `submissions` (CIK/SIC), Kenneth French daily factors. Universe cascade: 5,451 → 5,018 (≥ 8
99 +quarters) → 4,568 (1-minute coverage); event sample 481,159 observations after admissibility and exclusions
100 +(`results/table_sample_cascade_event.csv`). Full audit: `DATA_AUDIT.md`.
101 +
102 +## 🔬 Methodology
103 +
104 +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}`.
105 +2. PIT fundamentals (`pit_fundamentals.py`): local as-of join on `available_at`, verified live against the API.
106 +3. Labels (`labels.py`): seasonal-random-walk-with-drift surprises, primary and secondary targets, symmetric placebo.
107 +4. Features (`features.py`): same 19 families from 1m/5m/30m buckets of the regular session, daily block, split/dividend-adjusted returns.
108 +5. Ladder (`models.py`): B0–B5 logistic (C on validation), LB1–LB5 LightGBM (24-trial equal budget), ridge for the continuous target.
109 +6. Validation (`splits.py`): chronological with purge/embargo, expanding-window annual refits, 20 % firm hold-out.
110 +7. Inference (`inference.py`): block bootstrap by calendar quarter, Giacomini–White, Holm, two-way clustered panels.
111 +8. Robustness and pricing (`robustness.py`, `portfolio.py`): shuffled labels, subgroups, CAR regressions, decile sorts with FF5+MOM and costs.
112 +
113 +## 🖼 Figures & tables inventory
114 +
115 +| Figure | Content |
116 +|---|---|
117 +| `fig01_timeline` | event-time design and no-leakage rule (schematic) |
118 +| `fig02_coverage` | firms and adverse rate by year |
119 +| `fig03_prevalence` | conditional prevalence by size, illiquidity, leverage, release timing |
120 +| `fig04_frequency_gains` | Δ log loss vs B2 for 30m/5m/1m/all, logistic and LightGBM, 95 % block-bootstrap CIs |
121 +| `fig05_reliability` | calibration deciles |
122 +| `fig06_signal_evolution` | AUC and Δ by horizon (H4) |
123 +| `fig07_heterogeneity` | Δ by pre-specified subgroup (H3) |
124 +| `fig08_rolling` | annual refits: AUC and Δ by year |
125 +| `fig09_economic` | decile returns and alphas |
126 +
127 +Tables (`paper/tables/*.tex`): universe and sample cascades, summary statistics, information sets, headline metrics,
128 +paired contrasts, horizon ladder, secondary targets, firm hold-out, panel regressions, subgroups, placebo, continuous
129 +target, economic value, CAR regressions.
130 +
131 +## ⚠️ Limitations
132 +
133 +- Accounting panel conditioned on firms listed in November 2026 (no delisted entities from the vendor): survivorship.
134 +- `available_at = filing_date + 1` is a vendor policy; all facts are revision 1 (current values): PIT proxy, no as-originally-reported history.
135 +- OHLCV bars only: no quotes, trades or order flow; bar statistics are not interpreted as informed trading.
136 +- Logistic-family CI (±0.0027) is comparable to the MDE (0.0025); the precise-null claim rests mainly on the boosted family (±0.0006).
137 +- No neural/text models (owner instruction to minimise AI use). Full list: `LIMITATIONS.md`; planned v1.1 revisions: `docs/REFEREE_REPORTS.md`.
138 +
139 +## 📝 Citation
140 +
141 +```bibtex
142 +@techreport{boucher2026invisible,
143 + author = {Boucher, Simon-Pierre},
144 + title = {The Invisible Balance Sheet: Can Intraday Financial Markets Detect Corporate Deterioration Before Accounting Disclosures?},
145 + institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives},
146 + type = {Working Paper},
147 + number = {39},
148 + year = {2026},
149 + month = {November}
150 +}
151 +```
152 +
153 +## 👤 Author & contact
154 +
155 +**Simon-Pierre Boucher** — Département des sciences administratives, Université du Québec en Outaouais (UQO), Gatineau, QC.
156 +📧 [simon-pierre.boucher@uqo.ca](mailto:simon-pierre.boucher@uqo.ca)
157 +
158 +## 🗒 Notes
159 +
160 +- Built in one autonomous Claude Code session on 2026-11-02 (phases 0–7 of `CLAUDE.md`); gates requiring owner
161 + approval are recorded as self-approved under the autonomous mandate (DECISIONS D-010) and remain open to veto.
162 +- Directory name follows the series convention (`finance/<slug>`); project id and spbgit repository are `wp39_uqo`.
163 +- Data attribution: FirstRate Data via FinData OS (private license; no raw records in this repository).
added artifacts/evaluation/advanced_fit_log.json +1218 −0
@@ -0,0 +1,1218 @@
1 +{
2 + "fits": [
3 + {
4 + "design": "event",
5 + "h": 20,
6 + "importance_gain": {
7 + "f_accruals": 0.0255,
8 + "f_capex_assets": 0.0311,
9 + "f_cash_assets": 0.0402,
10 + "f_current_ratio": 0.0357,
11 + "f_gross_margin": 0.0288,
12 + "f_int_cover": 0.0183,
13 + "f_leverage": 0.0248,
14 + "f_log_assets": 0.0495,
15 + "f_log_mcap": 0.0311,
16 + "f_loss": 0.0007,
17 + "f_net_issuance": 0.0266,
18 + "f_ni_assets": 0.0308,
19 + "f_ocf_assets": 0.0235,
20 + "f_op_margin": 0.0051,
21 + "f_rd_rev": 0.0007,
22 + "f_rev_growth": 0.0332,
23 + "f_roa": 0.0069,
24 + "hist_adverse_rate": 0.0571,
25 + "info_age_td": 0.0257,
26 + "last_surprise": 0.3155,
27 + "mean_surprise4": 0.1891
28 + },
29 + "model": "LB1",
30 + "n_features": 21,
31 + "params": {
32 + "bagging_fraction": 1.0,
33 + "best_iteration": 658,
34 + "feature_fraction": 0.5,
35 + "lambda_l2": 10.0,
36 + "learning_rate": 0.01,
37 + "min_child_samples": 200,
38 + "num_leaves": 63
39 + },
40 + "tag": "chrono",
41 + "target": "y_adverse_opinc",
42 + "trials": 24,
43 + "val_score": 0.5446817024655795
44 + },
45 + {
46 + "design": "event",
47 + "h": 20,
48 + "importance_gain": {
49 + "f_accruals": 0.0214,
50 + "f_capex_assets": 0.0227,
51 + "f_cash_assets": 0.0263,
52 + "f_current_ratio": 0.0178,
53 + "f_gross_margin": 0.0234,
54 + "f_int_cover": 0.0155,
55 + "f_leverage": 0.0188,
56 + "f_log_assets": 0.0248,
57 + "f_log_mcap": 0.0145,
58 + "f_loss": 0.0001,
59 + "f_net_issuance": 0.0189,
60 + "f_ni_assets": 0.0266,
61 + "f_ocf_assets": 0.0175,
62 + "f_op_margin": 0.0049,
63 + "f_rd_rev": 0.0005,
64 + "f_rev_growth": 0.0248,
65 + "f_roa": 0.003,
66 + "hist_adverse_rate": 0.0375,
67 + "info_age_td": 0.0231,
68 + "last_surprise": 0.2557,
69 + "mean_surprise4": 0.1608,
70 + "wd_amihud60": 0.0084,
71 + "wd_bars20": 0.0124,
72 + "wd_intraday20": 0.0101,
73 + "wd_kurt60": 0.0131,
74 + "wd_log_dollar_vol60": 0.0105,
75 + "wd_maxdd60": 0.0345,
76 + "wd_overnight20": 0.0179,
77 + "wd_parkinson20": 0.016,
78 + "wd_ret20": 0.0117,
79 + "wd_ret60": 0.0227,
80 + "wd_skew60": 0.0137,
81 + "wd_vol20": 0.0146,
82 + "wd_vol60": 0.0241,
83 + "wd_volratio20": 0.0132,
84 + "wd_volratio5": 0.016,
85 + "wd_zeroret60": 0.0023
86 + },
87 + "model": "LB2",
88 + "n_features": 37,
89 + "params": {
90 + "bagging_fraction": 0.8,
91 + "best_iteration": 752,
92 + "feature_fraction": 0.9,
93 + "lambda_l2": 10.0,
94 + "learning_rate": 0.01,
95 + "min_child_samples": 100,
96 + "num_leaves": 63
97 + },
98 + "tag": "chrono",
99 + "target": "y_adverse_opinc",
100 + "trials": 24,
101 + "val_score": 0.521392195867179
102 + },
103 + {
104 + "design": "event",
105 + "h": 20,
106 + "importance_gain": {
107 + "f_accruals": 0.0148,
108 + "f_capex_assets": 0.017,
109 + "f_cash_assets": 0.0222,
110 + "f_current_ratio": 0.0127,
111 + "f_gross_margin": 0.0175,
112 + "f_int_cover": 0.0112,
113 + "f_leverage": 0.0122,
114 + "f_log_assets": 0.0191,
115 + "f_log_mcap": 0.0103,
116 + "f_loss": 0.0,
117 + "f_net_issuance": 0.0129,
118 + "f_ni_assets": 0.0239,
119 + "f_ocf_assets": 0.0119,
120 + "f_op_margin": 0.0051,
121 + "f_rd_rev": 0.0001,
122 + "f_rev_growth": 0.0192,
123 + "f_roa": 0.0029,
124 + "hist_adverse_rate": 0.038,
125 + "info_age_td": 0.0194,
126 + "last_surprise": 0.282,
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added artifacts/evaluation/metrics_all.csv +208 −0
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added artifacts/evaluation/panel_regressions.csv +59 −0
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added artifacts/evaluation/placebo_shuffled.csv +4 −0
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added artifacts/evaluation/portfolios_LB2.csv +1291 −0
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added artifacts/evaluation/portfolios_LB5.csv +1291 −0
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added artifacts/evaluation/power.json +7 −0
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added artifacts/evaluation/reliability.csv +41 −0
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added artifacts/evaluation/secondary_targets.csv +13 −0
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added artifacts/evaluation/subgroups.csv +37 −0
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added artifacts/evaluation/walkforward_by_year.csv +111 −0
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added artifacts/evaluation/walkforward_contrasts.csv +25 −0
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added artifacts/manifests/bars_manifest.jsonl +11543 −0
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