Hong Kong IPO win-rate forecast

Compare one-lot win rate, A-tail, B-head and top-hammer expected allotment. The main reference combines public-offer supply, clawback, demand and Pool A/B patterns; official announcements remain definitive.

Win-rate output is research only. Official allotment announcements are definitive.

How to read this page

Look at each IPO’s one-lot hit rate first, then A-tail and B-head average allotment. Those three numbers are the ones most people use.
“Most likely allotment pattern” explains how the current numbers were estimated. The detail page can compare other possible patterns.
An extra-watch note means the main reference is less certain, and names one pattern to compare first. Open the detail page for that pattern’s numbers; it does not change the figures here.

View the full free informational assessment / forecast

Sign in to view predicted one-lot hit rates, AI reference scores, grey-market and first-day performance values, and other core fields. Content is general information and education only, not investment advice.

Candidates

Samples

80/80

Time-weighted rel. error?

23.3%

P90 rel. error?

77.9%

These figures summarise recent listed samples and show overall model error. They are not the win rate of any single IPO.

IPOs you can check now

TRANSWARP

06727Model basis: Strong basisExtra watch

Compare on the detail page

One-lot hit rate?

Average lots allotted when applying for 1 lot. Closer to 100% means a one-lot application is more likely to get stock.

A-tail avg. allotted?

B-head avg. allotted?

Largest Pool A tier, usually the full small-application size. Smallest Pool B tier, just after entering the large-application pool. Public offer lots 28,020 lots

Most likely allotment pattern

Larger Pool A applications get less extra allotment；Pool B allotment falls more sharply with size

Diminishing + SteepLikelihood Sign in to view

The detail page can compare other likely patterns.

Extra watch

The main reference is less certain. After this note, compare first:

Pool A is closer to allotment by application size；Pool B allotment is similar across tiers

Proportional + Flat

This is not a new main reference. It is the pattern most worth checking first after this note.

Latest listed-sample review

For listed samples, compare the forecast with the official result. Samples with extra-watch notes historically missed the main reference more often.

EXCELLAND ROBOT

03231人工智能/機器人Sep 9

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

28.9%

Error 18.1%

Official one-lot hit rate

24.5%

A tail

Predicted A-tail allotted

7.015

Error 1981.1%

Official A-tail allotted

0.337

B head

Predicted B-head allotted

39.403

Error 62.0%

Official B-head allotted

24.324

LONGSYS

09976半導體Sep 8

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

29.1%

Error 921.0%

Official one-lot hit rate

2.9%

A tail

Predicted A-tail allotted

3.476

Error 67.1%

Official A-tail allotted

10.559

B head

Predicted B-head allotted

10.225

Error 26.8%

Official B-head allotted

13.965

MEDCAPTAIN

02041醫療健康Sep 7

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

7.6%

Error 51.3%

Official one-lot hit rate

5.0%

A tail

Predicted A-tail allotted

1.682

Error 54.3%

Official A-tail allotted

3.68

B head

Predicted B-head allotted

6.33

Error 31.2%

Official B-head allotted

9.201

SHEIN-W

00625消費Sep 1

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

37.7%

Error 104.6%

Official one-lot hit rate

18.4%

A tail

Predicted A-tail allotted

24.731

Error 86.5%

Official A-tail allotted

183.174

B head

Predicted B-head allotted

168.27

Error 34.9%

Official B-head allotted

258.304

INGENIC

03223半導體Aug 25

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

4.7%

Error 1686.7%

Official one-lot hit rate

0.3%

A tail

Predicted A-tail allotted

0.233

Error 78.1%

Official A-tail allotted

1.063

B head

Predicted B-head allotted

0.407

Error 20.2%

Official B-head allotted

0.338

NASN TECH

02261人工智能/機器人Aug 7

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

5.7%

Error 28.3%

Official one-lot hit rate

8.0%

A tail

Predicted A-tail allotted

0.64

Error 12.6%

Official A-tail allotted

0.732

B head

Predicted B-head allotted

1.554

Error 22.3%

Official B-head allotted

ZJ INNOLIGHT

03308其他Jul 30

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

9.9%

Error 85.9%

Official one-lot hit rate

70.0%

A tail

Predicted A-tail allotted

4.189

Error 39.6%

Official A-tail allotted

B head

Predicted B-head allotted

3.51

Error 41.5%

Official B-head allotted

NEXCHIP

02249半導體Jul 10

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

8.2%

Error 2.7%

Official one-lot hit rate

8.0%

A tail

Predicted A-tail allotted

1.264

Error 57.9%

Official A-tail allotted

B head

Predicted B-head allotted

5.781

Error 15.6%

Official B-head allotted

BEFAR GROUP

06745新材料Jul 10

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

7.3%

Error 45.4%

Official one-lot hit rate

5.0%

A tail

Predicted A-tail allotted

1.552

Error 22.4%

Official A-tail allotted

B head

Predicted B-head allotted

8.104

Error 15.8%

Official B-head allotted

RIGOL

00537工業製造Jul 9

View win-rate detail

One-lot hit rate

Predicted one-lot hit rate

11.2%

Error 39.5%

Official one-lot hit rate

8.0%

A tail

Predicted A-tail allotted

2.199

Error 232.8%

Official A-tail allotted

0.661

B head

Predicted B-head allotted

13.894

Error 98.5%

Official B-head allotted

Stock	Predicted one-lot hit rate	Official one-lot hit rate	Predicted A-tail allotted	Official A-tail allotted	Predicted B-head allotted	Official B-head allotted
EXCELLAND ROBOT 03231人工智能/機器人Sep 9	28.9% Error 18.1%	24.5%	7.015 Error 1981.1%	0.337	39.403 Error 62.0%	24.324	View win-rate detail
LONGSYS 09976半導體Sep 8	29.1% Error 921.0%	2.9%	3.476 Error 67.1%	10.559	10.225 Error 26.8%	13.965	View win-rate detail
MEDCAPTAIN 02041醫療健康Sep 7	7.6% Error 51.3%	5.0%	1.682 Error 54.3%	3.68	6.33 Error 31.2%	9.201	View win-rate detail
SHEIN-W 00625消費Sep 1	37.7% Error 104.6%	18.4%	24.731 Error 86.5%	183.174	168.27 Error 34.9%	258.304	View win-rate detail
INGENIC 03223半導體Aug 25	4.7% Error 1686.7%	0.3%	0.233 Error 78.1%	1.063	0.407 Error 20.2%	0.338	View win-rate detail
NASN TECH 02261人工智能/機器人Aug 7	5.7% Error 28.3%	8.0%	0.64 Error 12.6%	0.732	1.554 Error 22.3%	2	View win-rate detail
ZJ INNOLIGHT 03308其他Jul 30	9.9% Error 85.9%	70.0%	4.189 Error 39.6%	3	3.51 Error 41.5%	6	View win-rate detail
NEXCHIP 02249半導體Jul 10	8.2% Error 2.7%	8.0%	1.264 Error 57.9%	3	5.781 Error 15.6%	5	View win-rate detail
BEFAR GROUP 06745新材料Jul 10	7.3% Error 45.4%	5.0%	1.552 Error 22.4%	2	8.104 Error 15.8%	7	View win-rate detail
RIGOL 00537工業製造Jul 9	11.2% Error 39.5%	8.0%	2.199 Error 232.8%	0.661	13.894 Error 98.5%	7	View win-rate detail

Hong Kong IPO win rates, Pool A/B and allotment results

This is the IPO win-rate topic hub: start with one-lot probability and expected lots, then compare A-tail, B-head, clawback rules, and top-hammer applications across funding tiers.

Forecasts are scenario estimates for the subscription stage. Final win rates, allocated lots, clawback, and public-offer allocation are determined by the issuer's official allotment announcement.

One-lot win rate

Understand one-lot probability and expected allocated lots.

Pool A and Pool B

Compare A-tail and B-head thresholds and allocation rules.

Clawback mechanism

See how oversubscription may change public-offer supply and win rates.

Top-hammer application

Understand its relationship with Pool B, allocation, and funding use.

IPO win-rate questions

What does the one-lot win rate mean?

It is the share of one-lot applicants who receive at least one lot. It is not the allocation ratio for every tier and does not directly predict the lots received by a large application.

How does a forecast differ from the official allotment result?

A forecast estimates scenarios from public-offer supply, applicant counts, and historical patterns. The issuer's official announcement confirms clawback, Pool A/B allocation, and lots received at each tier.

Why compare A-tail, B-head, and top-hammer applications?

They represent different funding tiers and positions in the allocation table. Entering Pool B does not guarantee better capital efficiency, so expected lots, funding use, and scenario risk should be compared together.