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geometrybase a2ec016769 Add selected T0 horizontal IPO report

Request:
- Combine the currently selected T0 IPO reports into one cross-sectional analysis report.

Changes:
- Add a Chinese horizontal T0 report comparing 01392, 02335, 06067, 06106, and 06132.
- Rank the selected IPOs by the current T0 model and short-exit discipline focused on T2/D1 selling.
- Backfill 02335's Chinese company name from its Chinese HKEX prospectus and archive the source PDF plus extracted text.
- Refresh the v0 analysis dataset and sync-state snapshots at 2026-06-15T18:20:00Z.

Verification:
- .venv/bin/python -m py_compile scripts/build_analysis_dataset.py scripts/generate_ipo_report.py scripts/extract_pdf_text.py scripts/update_sync_state.py
- Python sqlite3 PRAGMA integrity_check returned ok and foreign_key_check returned zero rows.
- Confirmed 02335 Chinese source_ref, extracted text manifest row, and selected horizontal report content.
- git diff --cached --check

Next useful context:
- Untracked PDF exports of individual reports and the horizontal report were left out of this focused commit.

2026-06-15 15:17:06 +00:00

3.8 KiB

Raw Blame History

HK IPO Analysis Model v0

Model version: ipo_score_v0
Analysis as of: 2026-06-15T18:20:00Z
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: 297
Rows with D1 labels: 273
Rows with structured T1 demand fields: 291
Rows with prospectus source path: 297
Rows with allotment source path: 291
Rows with offer size: 297
Rows with public oversubscription: 281
Rows with international oversubscription: 277
Rows pending T1 structure: 6 (01392, 02335, 06067, 06106, 06132, 06675)
T1 field-level blanks: public oversubscription 10, international oversubscription 14, 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	105	73.3%	51.4%	40.1	13.2
t0_gte_8	72	76.4%	47.2%	28.6	9.6
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	59	94.9%	88.1%	86.7	80.0
total_lt_0	68	61.8%	23.5%	0.4	1.0

Current Read

After the T1 demand text backfill, the strongest v0 T1 bucket is total_gte_26 with 59 historical D1 observations and a 94.9% 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.py after archivist updates the database.
Use t0_score for prospectus-stage watchlisting.
Use total_score, decision_band, and calibrated_d1_positive_rate for 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.

3.8 KiB Raw Blame History