# 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

1. Run `scripts/build_analysis_dataset.py` after archivist updates the database.
2. Use `t0_score` for prospectus-stage watchlisting.
3. Use `total_score`, `decision_band`, and `calibrated_d1_positive_rate` for T1-stage subscription cards.
4. Frame live decisions around a T2 or D1 sell, not long-term holding.
5. 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.