geometrybase
5b9835c289
Request: Run the scheduled HK IPO analyst refresh as of 2026-06-23T15:00:19Z, refresh online archive facts first, rebuild the analysis dataset, write the latest Chinese broad candidate report, mirror it to reports/README.md, and preserve stage discipline. Changes: - Refreshed HKEX current-listing pages, VBKR/Jieli T0.95 market heat, ipohk external history, A/H quote evidence, and current HKEX document searches. - Archived official HKEX allotment-result PDFs and extracted text for 02335 and 06106; parsed official T1 demand into ipo_demand without copying market heat into official fields. - Rebuilt analysis_model_v0_dataset.csv and refreshed sync/source snapshots. - Updated reports/2026-06-23_latest_ipo_candidates_analysis.md and mirrored the same content to reports/README.md, including current ranking, fundamentals, unresolved-D1 risk/reward table, closed/waiting names, 30-day review, guardrails, and sources. Verification: - git diff --check - Rebuilt analysis dataset for 2026-06-23T15:00:19Z - Python check that reports/README.md matches the dated report and required new facts are present - Python check that 15:00Z heat has 8 ipo_market_heat rows and current actionable names have no official ipo_demand rows - Python check that 02335 and 06106 official T1 fields match HKEX allotment results - Python check that 77 source refs archived at 2026-06-23T15:00:19Z use repo-relative paths, files exist, and hashes match Next useful context: - 02335 and 06106 now have official T1 demand, but D1/T2 remain data_gap until listing-day evidence is archived. - 00901 Yahoo D1 fetch still returns 404; ipohk remains only a third-party cross-check.
5.7 KiB
HK IPO Analysis Model v0
- Model version:
ipo_score_v0 - Analysis as of:
2026-06-23T15:00:19Z - Rule file:
rules/ipo_score_v0.yaml - Dataset:
data/snapshots/analysis_model_v0_dataset.csv
What This Model Does
This is the first analyst model built from the downloaded archive. It creates a repeatable feature table, scores each IPO using stage-safe rules, and calibrates the score buckets against archived D1 sell outcomes. It is intentionally transparent: the output includes every score component and the archived source paths used for each ticker.
The model is built for a short IPO allocation trade: sell in T2 grey market when reliable executable data exists, or sell on D1 otherwise. It does not use grey-market data in v0 because T2 currently has no approved reproducible source. It also does not use post-listing returns as inputs; D1 is the primary sell label, while D5/D20/D60 are review labels only.
Data Inventory
- IPO rows scored: 312
- Rows with D1 labels: 278
- Rows with structured T1 demand fields: 297
- Rows with prospectus source path: 312
- Rows with allotment source path: 297
- Rows with offer size: 312
- Rows with public oversubscription: 287
- Rows with international oversubscription: 282
- Rows with market heat snapshots: 19
- Rows with T0.5 margin heat snapshots: 5
- Rows with T0.95 late-order heat snapshots: 14
- Rows with T0.5 margin heat and D1 labels: 3
- Rows with T0.95 late-order heat and D1 labels: 0
- Rows matched to external ipohk history: 102
- Rows with external final oversubscription: 95
- Rows with external final oversubscription and D1 labels: 86
- Rows pending T1 structure: 15 (00668, 01191, 01688, 01956, 02272, 02667, 02672, 02697, 03661, 03952, 06228, 06715, 06915, 09630, 09637)
- T1 field-level blanks: public oversubscription 10, international oversubscription 15, valid applications 6, successful applications 18
T0 Calibration
T0 uses only prospectus-stage structure: offer size, initial public offer percentage, minimum subscription amount, offer price band, and over-allotment availability.
| Bucket | N | D1 positive | D1 >= 10% | Avg D1 return | Median D1 return |
|---|---|---|---|---|---|
| t0_1_to_4 | 60 | 63.3% | 40.0% | 9.6 | 3.1 |
| t0_5_to_7 | 107 | 73.8% | 52.3% | 42.6 | 14.1 |
| t0_gte_8 | 75 | 76.0% | 48.0% | 28.8 | 9.8 |
| t0_lt_1 | 36 | 58.3% | 33.3% | 12.8 | 2.3 |
T1 Calibration
T1 adds allotment-stage demand: public subscription, international placing demand, valid application count, application success rate, and HK public offer reallocation.
| Bucket | N | D1 positive | D1 >= 10% | Avg D1 return | Median D1 return |
|---|---|---|---|---|---|
| total_0_to_9 | 68 | 58.8% | 30.9% | 3.3 | 0.2 |
| total_10_to_17 | 29 | 55.2% | 34.5% | 13.9 | 1.5 |
| total_18_to_25 | 49 | 75.5% | 51.0% | 31.3 | 13.4 |
| total_gte_26 | 64 | 93.8% | 87.5% | 86.9 | 78.1 |
| total_lt_0 | 68 | 61.8% | 23.5% | 0.4 | 1.0 |
T0.5 Market Heat
T0.5 uses archived subscription-period margin heat snapshots. T0.95 is the near-deadline subset that is still actionable before the user's order cutoff. These are non-official live signals and are kept separate from T1 allotment demand. The current archive is not yet a historical training set: it has too few rows and no D1 labels for calibration.
- Total market heat rows: 19
- T0.5 margin rows: 5
- T0.5 rows with D1 labels: 3
- T0.95 late-order heat rows: 14
- T0.95 rows with D1 labels: 0
External Final Heat Proxy
The ipohk history archive adds final public oversubscription, one-lot win rate, grey-market return, and first-day return where available. These fields are useful for coverage checks and post-hoc calibration, but they are not T0.5 inputs because they are final or near-final history.
- External history rows matched into this dataset: 102
- Matched rows with final oversubscription: 95
- Matched rows with final oversubscription and D1 labels: 86
| Bucket | N | D1 positive | D1 >= 10% | Avg D1 return | Median D1 return |
|---|---|---|---|---|---|
| external_os_1000x_to_5000x | 34 | 94.1% | 79.4% | 60.7 | 56.7 |
| external_os_100x_to_1000x | 21 | 61.9% | 38.1% | 8.8 | 4.2 |
| external_os_10x_to_100x | 7 | 28.6% | 14.3% | -23.0 | -21.9 |
| external_os_gte_5000x | 18 | 83.3% | 72.2% | 101.7 | 89.7 |
| external_os_lt_10x | 6 | 50.0% | 16.7% | 4.7 | -4.1 |
Current Read
After the T1 demand text backfill, the strongest v0 T1 bucket is total_gte_26 with 64 historical D1 observations and a 93.8% D1 positive rate. The model is most useful after allotment results are available; T0 is a watchlist filter rather than a final subscription call.
The high-conviction bucket remains clearly differentiated, but the middle and low score buckets are still not monotonic. This refresh keeps the v0 score formula unchanged and updates empirical calibration only; future rule changes should come from reviewed prediction cards rather than overfitting this historical sample.
Usage
- Run
scripts/build_analysis_dataset.pyafter archivist updates the database. - Use
t0_scorefor prospectus-stage watchlisting. - Use
total_score,decision_band, andcalibrated_d1_positive_ratefor T1-stage subscription cards. - Frame live decisions around a T2 or D1 sell, not long-term holding.
- Treat D5/D20/D60 columns as review labels only, never as prediction inputs or holding targets.
Known Gaps
- T1 is structurally complete for listed rows; residual field-level NULLs remain when the archived source does not explicitly state a demand field.
- Industry and issuer fundamentals are not sufficiently structured for model input.
- T2 grey-market signal is blocked pending an approved source.
- Extreme D1 returns should be audited before they drive rule changes.