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42c18131e8
Request: - Generate an analyst report for HK IPO ticker 06067. Changes: - Archived the official HKEXnews 06067 prospectus PDF and extracted text under project-relative data paths. - Seeded 06067 T0 prospectus facts, source references, sync state, and analysis snapshots. - Generated reports/2026-06-15_06067_T0_prospectus_analysis.md in Simplified Chinese with concrete T0/T1/T2/D1 dates and short-exit T2/D1 discipline. - Updated the HKEX document archiver so over-allotment shares are only recorded when the prospectus supports them, with explicit no-option cases stored as zero. Verification: - Compiled archive_hkex_documents.py, generate_ipo_report.py, build_analysis_dataset.py, extract_pdf_text.py, and update_sync_state.py. - Ran SQLite integrity_check and foreign_key_check. - Verified the archived 06067 PDF hash, extracted-text manifest row, and analysis dataset row. - Ran git diff --check. Next useful context: - 06067 is currently T0_prospectus; T1_allotment is pending for 2026-06-22.
70 lines
3.8 KiB
Markdown
70 lines
3.8 KiB
Markdown
# HK IPO Analysis Model v0
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- Model version: `ipo_score_v0`
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- Analysis as of: `2026-06-15T17:05:00Z`
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- Rule file: `rules/ipo_score_v0.yaml`
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- Dataset: `data/snapshots/analysis_model_v0_dataset.csv`
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## What This Model Does
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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.
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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.
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## Data Inventory
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- IPO rows scored: 296
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- Rows with D1 labels: 273
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- Rows with structured T1 demand fields: 291
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- Rows with prospectus source path: 296
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- Rows with allotment source path: 291
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- Rows with offer size: 296
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- Rows with public oversubscription: 281
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- Rows with international oversubscription: 277
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- Rows pending T1 structure: 5 (01392, 06067, 06106, 06132, 06675)
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- T1 field-level blanks: public oversubscription 10, international oversubscription 14, valid applications 6, successful applications 18
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## T0 Calibration
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T0 uses only prospectus-stage structure: offer size, initial public offer percentage, minimum subscription amount, offer price band, and over-allotment availability.
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| Bucket | N | D1 positive | D1 >= 10% | Avg D1 return | Median D1 return |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| t0_1_to_4 | 60 | 63.3% | 40.0% | 9.6 | 3.1 |
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| t0_5_to_7 | 105 | 73.3% | 51.4% | 40.1 | 13.2 |
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| t0_gte_8 | 72 | 76.4% | 47.2% | 28.6 | 9.6 |
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| t0_lt_1 | 36 | 58.3% | 33.3% | 12.8 | 2.3 |
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## T1 Calibration
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T1 adds allotment-stage demand: public subscription, international placing demand, valid application count, application success rate, and HK public offer reallocation.
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| Bucket | N | D1 positive | D1 >= 10% | Avg D1 return | Median D1 return |
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| --- | ---: | ---: | ---: | ---: | ---: |
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| total_0_to_9 | 68 | 58.8% | 30.9% | 3.3 | 0.2 |
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| total_10_to_17 | 29 | 55.2% | 34.5% | 13.9 | 1.5 |
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| total_18_to_25 | 49 | 75.5% | 51.0% | 31.3 | 13.4 |
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| total_gte_26 | 59 | 94.9% | 88.1% | 86.7 | 80.0 |
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| total_lt_0 | 68 | 61.8% | 23.5% | 0.4 | 1.0 |
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## Current Read
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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.
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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.
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## Usage
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1. Run `scripts/build_analysis_dataset.py` after archivist updates the database.
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2. Use `t0_score` for prospectus-stage watchlisting.
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3. Use `total_score`, `decision_band`, and `calibrated_d1_positive_rate` for T1-stage subscription cards.
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4. Frame live decisions around a T2 or D1 sell, not long-term holding.
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5. Treat D5/D20/D60 columns as review labels only, never as prediction inputs or holding targets.
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## Known Gaps
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- T1 is structurally complete for listed rows; residual field-level NULLs remain when the archived source does not explicitly state a demand field.
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- Industry and issuer fundamentals are not sufficiently structured for model input.
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- T2 grey-market signal is blocked pending an approved source.
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- Extreme D1 returns should be audited before they drive rule changes.
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