Korea / Special Situations, Clepsydra Capital

Sanghyun Park

Korea / Special Situations

Korea / Special Sits, Pairs, Index Rebalancing

Clepsydra Capital

Investigate special situations, pairs, and index rebalancing trades in Korea.

Event-Driven

SK Bioscience ended another losing day with a -4.74% loss yesterday.The price is at -28.70% from the first trading close. Of course, it is still +85.38% from the offering price of ₩65,000.












Date
Open
High
Low
Close
Volume
TV/SO
Daily


3/18
130,000
169,000
130,000
169,000
876,189
1.15%
-


3/19
184,000
190,000
166,000
166,500
12,468,903
16.30%
-1.48%


3/22
161,000
162,500
143,000
144,000
4,675,116
6.11%
-13.51%


3/23
147,000
149,500
138,000
140,500
2,980,953
3.90%
-2.43%


3/24
138,000
142,000
135,500
136,500
1,707,179
2.23%
-2.85%


3/25
138,500
140,000
135,500
136,000
1,111,829
1.45%
-0.37%


3/26
136,500
138,000
131,500
132,000
1,157,841
1.51%
-2.94%


3/29
132,000
132,500
123,500
123,500
1,412,312
1.85%
-6.44%


3/30
126,500
129,500
124,500
126,500
1,269,769
1.66%
2.43%


3/31
124,500
125,500
120,000
120,500
1,016,714
1.33%
-4.74%

Source: KRX

KOSPI 200 fast entryIt has to stay within the top 50 ordinary shares on KOSPI by avg daily market cap for the first 15 trading days.We are now Day 11 today. For the past 10 trading sessions (March 18~31), SK Bioscience's avg daily MC is ₩10.8T, enough to occupy the 30th spot.The last spot (50th) is CJ Cheiljedang Corp, with a ₩6.3T MC.SK Bioscience needs to lose another 41.9% in the next 5 trading sessions to avoid KOSPI 200 fast entry.Well, I guess fast entry is still a foregone conclusion.








Top 50 KOSPI 200 by Avg daily MC (March 18~31)
Rank
Ticker
Avg daily MC (₩B)


Samsung Electronics Co Ltd
1
005930
488,029.7


SK Hynix Inc
2
000660
98,535.1


Naver Corp
3
035420
63,463.2


LG Chem Ltd
4
051910
56,876.3


Samsung Biologics Co Ltd
5
207940
48,035.8


Hyundai Motor Co
6
005380
47,573.2


Samsung SDI Co Ltd
7
006400
44,607.6


Kakao Corp
8
035720
43,716.6


Celltrion, Inc.
9
068270
38,115.3


Kia Motors Corporation
10
000270
33,916.8


HYUNDAI MOBIS CO., LTD.
11
012330
27,513.7


POSCO
12
005490
27,049.7


LG Electronics Inc.
13
066570
24,367.2


LG Household &amp; Healthcare Ltd
14
051900
24,013.0


Samsung C&amp;T Corp
15
028260
22,818.9


KB Financial Group Inc
16
105560
21,802.9


SK Telecom Co Ltd
17
017670
21,050.4


NCSoft Corp
18
036570
19,817.9


SK Innovation Co Ltd
19
096770
19,422.4


Shinhan Financial Group Co., Ltd.
20
055550
18,669.9


SK Holdings Co Ltd
21
034730
18,244.4


Samsung Life Insurance co., Ltd
22
032830
15,360.0


LG Corp
23
003550
15,261.0


Korea Electric Power Corporation
24
015760
14,951.3


Amorepacific Corp
25
090430
14,863.1


Samsung SDS Co Ltd
26
018260
14,527.7


Samsung Electro-Mechanics Co., Ltd.
27
009150
14,076.0


Hana Financial Group Inc
28
086790
12,455.5


KT&amp;G Corp
29
033780
11,113.8


SK Bioscience Co Ltd
30
302440
10,847.7


Netmarble Corp
31
251270
10,721.0


Lotte Chemical Corp
32
011170
10,483.1


HMM Co Ltd
33
011200
9,669.3


Korean Air Lines Co Ltd
34
003490
9,632.9


Hanon Systems
35
018880
9,587.0


S Oil Corp
36
010950
9,077.6


Posco Chemical Co Ltd
37
003670
9,041.5


Hanwha Solutions Corp
38
009830
8,891.6


Samsung Fire &amp; Marine Insurance Co Ltd
39
000810
8,873.3


Korea Shipbuilding &amp;Offshore Engn Co Ltd
40
009540
8,772.3


Big Hit Entertainment Co Ltd
41
352820
8,302.1


SK Biopharmaceuticals Co Ltd
42
326030
8,226.8


LG Display Co Ltd
43
034220
7,929.2


Korea Zinc Inc
44
010130
7,656.5


Kumho Petro Chemical Co Ltd
45
011780
7,332.0


Woori Financial Group Inc
46
316140
7,263.8


Hyundai Glovis Co Ltd
47
086280
7,170.0


KT Corp
48
030200
7,167.5


Industrial Bank of Korea
49
024110
6,700.4


CJ Cheiljedang Corp
50
097950
6,302.4

Source: KRX



SK Bioscience: K200 Fast Entry, Lock-Up Expiry, Loan/Short Balance, & Exit Points

<p>SK Bioscience ended Day 2 with a 1.48% loss.</p><p>Unlike Day 1, the TV soared to 16.3% of the SO with a price volatility of 13.04% of the opening.</p><p>The price ended only 2.08% above the daily low.</p><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Date"}' data-sheets-numberformat='{"1":5,"2":"m\"/\"d\"/\"yy"}'>Date</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Close"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Close</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Open"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Open</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"High"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>High</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Low"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Low</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"U Wick"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>U Wick</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Body"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Body</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"L Wick"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>L Wick</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"TV/SO"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>TV/SO</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Volume"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Volume</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"x Open"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>x Open</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Daily Δ"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Daily Δ</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":44274.64583333333}' data-sheets-numberformat='{"1":5,"2":"m\"/\"d\"/\"yy"}' data-sheets-formula="=Chart!R[506]C[0]">3/19/21</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":166500}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[3]">166,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":184000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[-1]">184,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":190000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[-1]">190,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":166000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[-1]">166,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.25}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=if((R[0]C[-4]-R[0]C[-3])&gt;0, (R[0]C[-4]-R[0]C[-2])/(R[0]C[-2]-R[0]C[-1]), (R[0]C[-3]-R[0]C[-2])/(R[0]C[-2]-R[0]C[-1]))">-25.00%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.7291666666666666}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=(R[0]C[-5]-R[0]C[-4])/(R[0]C[-3]-R[0]C[-2])">-72.92%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.020833333333333332}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=if((R[0]C[-6]-R[0]C[-5])&gt;0, (R[0]C[-5]-R[0]C[-3])/(R[0]C[-4]-R[0]C[-3]), (R[0]C[-6]-R[0]C[-3])/(R[0]C[-4]-R[0]C[-3]))">2.08%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.16299219607843138}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[1]/76500000">16.30%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":12468903}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=Chart!R[506]C[-4]">12,468,903</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.13043478260869565}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=(R[0]C[-7]-R[0]C[-6])/R[0]C[-8]">13.04%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.014792899408284023}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=(R[0]C[-10]-R[1]C[-10])/R[1]C[-10]">-1.48%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":44273.64583333333}' data-sheets-numberformat='{"1":5,"2":"m\"/\"d\"/\"yy"}' data-sheets-formula="=Chart!R[506]C[0]">3/18/21</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":169000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[3]">169,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":130000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[-1]">130,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":169000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[-1]">169,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":130000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=Chart!R[506]C[-1]">130,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=if((R[0]C[-4]-R[0]C[-3])&gt;0, (R[0]C[-4]-R[0]C[-2])/(R[0]C[-2]-R[0]C[-1]), (R[0]C[-3]-R[0]C[-2])/(R[0]C[-2]-R[0]C[-1]))">0.00%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=(R[0]C[-5]-R[0]C[-4])/(R[0]C[-3]-R[0]C[-2])">100.00%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=if((R[0]C[-6]-R[0]C[-5])&gt;0, (R[0]C[-5]-R[0]C[-3])/(R[0]C[-4]-R[0]C[-3]), (R[0]C[-6]-R[0]C[-3])/(R[0]C[-4]-R[0]C[-3]))">0.00%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.011453450980392157}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[1]/76500000">1.15%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":876189}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=Chart!R[506]C[-4]">876,189</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.3}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=(R[0]C[-7]-R[0]C[-6])/R[0]C[-8]">30.00%</td>
<td style="text-align: right;" data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}'></td>
</tr>
<tr><td colspan="12">Source: KRX</td></tr>
</tbody>
</table><hr><p><strong>Now, what's noteworthy is a suspicious activity seen at the Kyobo account.</strong></p><p>Day 1, the trade was virtually frozen as the price immediately hit the daily ceiling after the market opened.</p><p>Only 876,189 shares changed hands, representing 1.15% of the SO.</p><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Date"}'>Date</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Type"}'>Type</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Retail"}'>Retail</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Foreign"}'>Foreign</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Local inst."}'>Local inst.</td>
</tr>
<tr>
<td rowspan="3" data-sheets-value='{"1":3,"3":44274}' data-sheets-numberformat='[null,5,"yyyy-mm-dd",1]'>2021-03-19</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Sell"}'>Sell</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":11746546}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>11,746,546</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":980169}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>980,169</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":447703}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>447,703</td>
</tr>
<tr>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Buy"}'>Buy</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":12850670}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>12,850,670</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":217284}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>217,284</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":208502}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>208,502</td>
</tr>
<tr>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Net"}'>Net</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1104124}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>1,104,124</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-762885}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>-762,885</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-239201}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>-239,201</td>
</tr>
<tr>
<td rowspan="3" data-sheets-value='{"1":3,"3":44273}' data-sheets-numberformat='[null,5,"yyyy-mm-dd",1]'>2021-03-18</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Sell"}'>Sell</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":576646}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>576,646</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":178783}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>178,783</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":102294}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>102,294</td>
</tr>
<tr>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Buy"}'>Buy</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":752889}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>752,889</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":100850}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>100,850</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":20300}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>20,300</td>
</tr>
<tr>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Net"}'>Net</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":176243}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>176,243</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-77933}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>-77,933</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-81994}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>-81,994</td>
</tr>
<tr><td colspan="5" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table><p>What draws our attention is that 61% of the total buys on Day 1 came from the Kyobo account alone.</p><p>Yes, it is infrequent for a single brokerage, out of more than 60 brokerages, to take 60+% trades of stock in very high demand like this one on a single day.</p><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Kyobo"}'>Kyobo account</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Buy"}'>Sell</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Sell"}'>Buy</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Net"}'>Net</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Avg price"}'>Avg price</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":44274}' data-sheets-numberformat='[null,5,"yyyy-mm-dd",1]'>2021-03-19</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":666280}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'><strong>666,280</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":152471}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>152,471</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-513809}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>-513,809</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":166500}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>166,500</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":44273}' data-sheets-numberformat='[null,5,"yyyy-mm-dd",1]'>2021-03-18</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":900}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>900</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":529814}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'><strong>529,814</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":528914}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>528,914</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":169000}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>169,000</td>
</tr>
<tr><td colspan="5" rowspan="1" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table><p>Then, where did this Day 1 portion from Kyobo go on Day 2?</p><p>About 480,000 shares got sold <strong>within 10 minutes after the opening on Day 2</strong>, and the remaining got fully liquidated before 9:30 AM.</p><p>In the first 10 minutes on Day 2, the price change peaked at 12%p.</p><p><strong>This Kyobo trade set the tone for this volatility very early on Day 2 and possibly for the rest of the day.</strong></p><p>Their one-day swing trade return is estimated at 8~12%.</p><table class="table table-striped">
<colgroup>
<col width="100">
<col width="100">
<col width="100">
<col width="100">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Time"}'>Time</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Price (₩)"}'>Price (₩)</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"TV"}'>TV</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"% to prev"}'>% to prev session</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.37569444444444444}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:01:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":185000}' data-sheets-numberformat='[null,2,"#,##0"]'>185,000</td>
<td style="text-align: right;" data-sheets-numberformat='[null,2,"#,##0"]'>-</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.09467455621301775}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">9.47%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.3763888888888889}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:02:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":186500}' data-sheets-numberformat='[null,2,"#,##0"]'>186,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":212847}' data-sheets-numberformat='[null,2,"#,##0"]'>212,847</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.10355029585798817}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">10.36%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.3770833333333333}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:03:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":189500}' data-sheets-numberformat='[null,2,"#,##0"]'>189,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":291542}' data-sheets-numberformat='[null,2,"#,##0"]'>291,542</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.12130177514792899}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">12.13%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.37777777777777777}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:04:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":186500}' data-sheets-numberformat='[null,2,"#,##0"]'>186,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":405693}' data-sheets-numberformat='[null,2,"#,##0"]'>405,693</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.10355029585798817}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">10.36%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.3784722222222222}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:05:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":180500}' data-sheets-numberformat='[null,2,"#,##0"]'>180,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":324453}' data-sheets-numberformat='[null,2,"#,##0"]'>324,453</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.06804733727810651}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">6.80%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.37916666666666665}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:06:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":181500}' data-sheets-numberformat='[null,2,"#,##0"]'>181,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":213182}' data-sheets-numberformat='[null,2,"#,##0"]'>213,182</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.07396449704142012}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">7.40%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.3798611111111111}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:07:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":175500}' data-sheets-numberformat='[null,2,"#,##0"]'>175,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":266934}' data-sheets-numberformat='[null,2,"#,##0"]'>266,934</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.038461538461538464}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">3.85%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.38055555555555554}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:08:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":174000}' data-sheets-numberformat='[null,2,"#,##0"]'>174,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":193794}' data-sheets-numberformat='[null,2,"#,##0"]'>193,794</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.029585798816568046}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">2.96%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.38125}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:09:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":174000}' data-sheets-numberformat='[null,2,"#,##0"]'>174,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":146324}' data-sheets-numberformat='[null,2,"#,##0"]'>146,324</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.029585798816568046}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">2.96%</td>
</tr>
<tr>
<td data-sheets-value='{"1":3,"3":0.3819444444444444}' data-sheets-numberformat='[null,6,"h:mm:ss",1]'>9:10:00</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":172500}' data-sheets-numberformat='[null,2,"#,##0"]'>172,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":167856}' data-sheets-numberformat='[null,2,"#,##0"]'>167,856</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.020710059171597635}' data-sheets-formula="=(R[0]C[-2]-R1C5)/R1C5">2.07%</td>
</tr>
<tr><td colspan="4">Source: KRX</td></tr>
</tbody>
</table><section class="figure"><figure class="editor-figure mceNonEditable"><img src="https://static-prod.smartkarma.com/images/media/000/195/799/original/chart+%281%29.png?1616258828" class="editor-image img-responsive" data-smartkarma-image-id="195799"></figure></section>

SK Bioscience ended Day 2 with a 1.48% loss.Unlike Day 1, the TV soared to 16.3% of the SO with a price volatility of 13.04% of the opening.The price ended only 2.08% above the daily low.
















Date
Close
Open
High
Low
U Wick
Body
L Wick
TV/SO
Volume
x Open
Daily Δ


3/19/21
166,500
184,000
190,000
166,000
-25.00%
-72.92%
2.08%
16.30%
12,468,903
13.04%
-1.48%


3/18/21
169,000
130,000
169,000
130,000
0.00%
100.00%
0.00%
1.15%
876,189
30.00%


Source: KRX

Now, what's noteworthy is a suspicious activity seen at the Kyobo account.Day 1, the trade was virtually frozen as the price immediately hit the daily ceiling after the market opened.Only 876,189 shares changed hands, representing 1.15% of the SO.









Date
Type
Retail
Foreign
Local inst.


2021-03-19
Sell
11,746,546
980,169
447,703


Buy
12,850,670
217,284
208,502


Net
1,104,124
-762,885
-239,201


2021-03-18
Sell
576,646
178,783
102,294


Buy
752,889
100,850
20,300


Net
176,243
-77,933
-81,994

Source: KRX

What draws our attention is that 61% of the total buys on Day 1 came from the Kyobo account alone.Yes, it is infrequent for a single brokerage, out of more than 60 brokerages, to take 60+% trades of stock in very high demand like this one on a single day.









Kyobo account
Sell
Buy
Net
Avg price


2021-03-19
666,280
152,471
-513,809
166,500


2021-03-18
900
529,814
528,914
169,000

Source: KRX

Then, where did this Day 1 portion from Kyobo go on Day 2?About 480,000 shares got sold within 10 minutes after the opening on Day 2, and the remaining got fully liquidated before 9:30 AM.In the first 10 minutes on Day 2, the price change peaked at 12%p.This Kyobo trade set the tone for this volatility very early on Day 2 and possibly for the rest of the day.Their one-day swing trade return is estimated at 8~12%.








Time
Price (₩)
TV
% to prev session


9:01:00
185,000
-
9.47%


9:02:00
186,500
212,847
10.36%


9:03:00
189,500
291,542
12.13%


9:04:00
186,500
405,693
10.36%


9:05:00
180,500
324,453
6.80%


9:06:00
181,500
213,182
7.40%


9:07:00
175,500
266,934
3.85%


9:08:00
174,000
193,794
2.96%


9:09:00
174,000
146,324
2.96%


9:10:00
172,500
167,856
2.07%

Source: KRX



What Happened to SK Bioscience on Day 2 (Feat. Kyobo Securities)?

<p>SK Bio, as well expected, began the day with a 100% gain to the offering price and ended at a daily ceiling (+30%), making the total daily gain 160% from the offering price</p><table class="table table-striped">
<colgroup>
<col width="200">
<col width="200">
</colgroup>
<tbody>
<tr><td colspan="2" rowspan="1" data-sheets-value='{"1":2,"2":"Market debut (March 18)"}'>Market debut (March 18)</td></tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Close"}'>Close</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":169000}' data-sheets-numberformat='{"1":4,"2":"[$₩]#,##0","3":1}'>₩169,000</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":" - % to opening"}'>- % to the opening price</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.3}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=(R[-1]C[0]-R[0]C[1])/R[0]C[1]">30.00%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":" - % to IPO price"}'>- % to the IPO price</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1.6}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=(R[-2]C[0]-R[0]C[1])/R[0]C[1]">160.00%</td>
</tr>
<tr><td colspan="2" rowspan="1" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table><p>Again unsurprisingly, it was all about local retail trading.</p><p>Local retail ended with a 176,243 net buy position and accounted for 77% of all trades.</p><table class="table table-striped">
<colgroup>
<col width="200">
<col width="100">
<col width="100">
<col width="100">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Trade by investor type"}'>Trade by investor type</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Retail"}'>Retail</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Inst."}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Inst.</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Foreign"}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>Foreign</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Sell"}'>Sell</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":576646}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>576,646</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":102294}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>102,294</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":178783}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>178,783</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Buy"}'>Buy</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":752889}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>752,889</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":20300}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>20,300</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":100850}' data-sheets-numberformat='{"1":2,"2":"#,##0"}'>100,850</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Net"}'>Net</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":176243}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=R[-1]C[0]-R[-2]C[0]">176,243</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-81994}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=R[-1]C[0]-R[-2]C[0]">-81,994</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-77933}' data-sheets-numberformat='{"1":2,"2":"#,##0"}' data-sheets-formula="=R[-1]C[0]-R[-2]C[0]">-77,933</td>
</tr>
<tr><td colspan="4" rowspan="1" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table><div>
<p>Now, KOSPI 200 fast entry?</p>
<p>It now seems a done deal.</p>
<p>The current MC sits at ₩12.9T, the 29th place right behind Amorepacific.</p>
<p>It has to stay within the top 50 ordinary shares on KOSPI for the first 15 trading days.</p>
<p>Given the lockup release schedule, retail-driven trading will likely continue at least for the first 15 trading days, <strong>suggesting a less likelihood of price correction during the fast entry review period</strong>.</p>
<div><div><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td style="width: 215px;" data-sheets-value='{"1":2,"2":"Float schedule"}'>Float schedule</td>
<td style="text-align: right; width: 220px;"><p>Float shares</p></td>
<td style="text-align: right; width: 193px;">% of SO</td>
</tr>
<tr>
<td style="width: 215px;" data-sheets-value='{"1":2,"2":"Immediate"}'>Immediate</td>
<td style="text-align: right; width: 220px;" data-sheets-value='{"1":3,"3":8897510}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-7]C[0]+R[-6]C[0]+R[-5]C[0]">8,897,510</td>
<td style="text-align: right; width: 193px;" data-sheets-value='{"1":3,"3":0.1163073202614379}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">11.63%</td>
</tr>
<tr>
<td style="width: 215px;" data-sheets-value='{"1":2,"2":"15 days"}'>15 days</td>
<td style="text-align: right; width: 220px;" data-sheets-value='{"1":3,"3":9261890}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-1]C[0]+R[-9]C[0]">9,261,890</td>
<td style="text-align: right; width: 193px;" data-sheets-value='{"1":3,"3":0.12107045751633987}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">12.11%</td>
</tr>
<tr>
<td style="width: 215px;" data-sheets-value='{"1":2,"2":"1 month"}'>1 month</td>
<td style="text-align: right; width: 220px;" data-sheets-value='{"1":3,"3":12592890}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-1]C[0]+R[-12]C[0]">12,592,890</td>
<td style="text-align: right; width: 193px;" data-sheets-value='{"1":3,"3":0.1646129411764706}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">16.46%</td>
</tr>
<tr>
<td style="width: 215px;" data-sheets-value='{"1":2,"2":"3 months"}'>3 months</td>
<td style="text-align: right; width: 220px;" data-sheets-value='{"1":3,"3":15923890}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-1]C[0]+R[-13]C[0]">15,923,890</td>
<td style="text-align: right; width: 193px;" data-sheets-value='{"1":3,"3":0.2081554248366013}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">20.82%</td>
</tr>
<tr>
<td style="width: 215px;" data-sheets-value='{"1":2,"2":"6 months"}'>6 months</td>
<td style="text-align: right; width: 220px;" data-sheets-value='{"1":3,"3":72221990}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-1]C[0]+R[-15]C[0]+R[-18]C[0]">72,221,990</td>
<td style="text-align: right; width: 193px;" data-sheets-value='{"1":3,"3":0.9440783006535948}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">94.41%</td>
</tr>
<tr>
<td style="width: 215px;" data-sheets-value='{"1":2,"2":"1 year"}'>1 year</td>
<td style="text-align: right; width: 220px;" data-sheets-value='{"1":3,"3":76712390}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-1]C[0]+R[-18]C[0]">76,712,390</td>
<td style="text-align: right; width: 193px;" data-sheets-value='{"1":3,"3":1.002776339869281}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">100.28%</td>
</tr>
<tr><td colspan="3" style="width: 628px;" data-sheets-value='{"1":2,"2":"Source: DART"}'>Source: DART</td></tr>
</tbody>
</table></div></div>
</div>

SK Bio, as well expected, began the day with a 100% gain to the offering price and ended at a daily ceiling (+30%), making the total daily gain 160% from the offering price





Market debut (March 18)

Close
₩169,000


- % to the opening price
30.00%


- % to the IPO price
160.00%

Source: KRX

Again unsurprisingly, it was all about local retail trading.Local retail ended with a 176,243 net buy position and accounted for 77% of all trades.








Trade by investor type
Retail
Inst.
Foreign


Sell
576,646
102,294
178,783


Buy
752,889
20,300
100,850


Net
176,243
-81,994
-77,933

Source: KRX


Now, KOSPI 200 fast entry?
It now seems a done deal.
The current MC sits at ₩12.9T, the 29th place right behind Amorepacific.
It has to stay within the top 50 ordinary shares on KOSPI for the first 15 trading days.
Given the lockup release schedule, retail-driven trading will likely continue at least for the first 15 trading days, suggesting a less likelihood of price correction during the fast entry review period.








Float schedule
Float shares
% of SO


Immediate
8,897,510
11.63%


15 days
9,261,890
12.11%


1 month
12,592,890
16.46%


3 months
15,923,890
20.82%


6 months
72,221,990
94.41%


1 year
76,712,390
100.28%

Source: DART




SK Bioscience to KOSPI 200 Is a Done Deal & A Likely Deletion

Korea/Asia, Events, IPOs, Douglas Research Advisory

Douglas Kim

Korea/Asia, Events, IPOs

Equity Capital Markets

<p><a href="https://www.smartkarma.com/entities/sk-bioscience-co-ltd" target="_blank" data-smartkarma-citation="entities:31484:1" rel="noopener">SK Bioscience (302440 KS)</a> IPO is scheduled to be completed this week on March 18th. This IPO was on a near cruise control, scoring big points among many institutional and retail investors in the pre-IPO period. Nonetheless, in the past several days, a new development has taken place with numerous countries, including Germany, France, Italy, and Spain announcing that they will temporarily suspend <a href="https://edition.cnn.com/2021/03/15/europe/italy-lockdown-europe-coronavirus-monday-scli-intl/index.html" target="_blank" rel="noopener">AstraZeneca COVID-19 vaccinations</a> until further notice.</p><div class="content-highlight"><p><em>If we had to take an educated guess, we think that many investors that may have wanted to sell their shares of SK Bioscience at about 150,000 won to 200,000 won per share may now lower their high end expectations so that they may now be trying to sell the stock at about 100,000 won to 150,000 won per share. The high end range of 100,000 won to 150,000 won still represents a healthy upside from the IPO price but still less than the even higher levels that many investors were expecting from this IPO. Our base case price target of SK Bioscience remains 149,000 won per share (129% higher than the IPO price) or 11.5 trillion won in implied market cap.  </em></p></div>

SK Bioscience (302440 KS) IPO is scheduled to be completed this week on March 18th. This IPO was on a near cruise control, scoring big points among many institutional and retail investors in the pre-IPO period. Nonetheless, in the past several days, a new development has taken place with numerous countries, including Germany, France, Italy, and Spain announcing that they will temporarily suspend AstraZeneca COVID-19 vaccinations until further notice.If we had to take an educated guess, we think that many investors that may have wanted to sell their shares of SK Bioscience at about 150,000 won to 200,000 won per share may now lower their high end expectations so that they may now be trying to sell the stock at about 100,000 won to 150,000 won per share. The high end range of 100,000 won to 150,000 won still represents a healthy upside from the IPO price but still less than the even higher levels that many investors were expecting from this IPO. Our base case price target of SK Bioscience remains 149,000 won per share (129% higher than the IPO price) or 11.5 trillion won in implied market cap.  

Germany, France, Italy, & Spain Suspend AstraZeneca COVID-19 Vaccinations: Impact on SK Bioscience

<p>Below is the final allocation.</p><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Type"}'>Type</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Initial"}'>Initial</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Subscription"}'>Subscription</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Allocation"}'>Allocation</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"%"}'>%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"ESOP"}'>ESOP</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":4590000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>4,590,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":4490400}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>4,490,400</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":4490400}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>4,490,400</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.1957}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}'>19.57%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Institutional"}'>Institutional</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":12622500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>12,622,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":12622500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>12,622,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":12622500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>12,622,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.55}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}'>55.00%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Retail"}'>Retail</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":5737500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>5,737,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1957531110}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>1,957,531,110</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":5837100}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>5,837,100</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.2543}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}'>25.43%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Total"}'>Total</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":22950000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=sum(R[-3]C[0]:R[-1]C[0])">22,950,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1974644010}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>1,974,644,010</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":22950000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=sum(R[-3]C[0]:R[-1]C[0])">22,950,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=sum(R[-3]C[0]:R[-1]C[0])">100.00%</td>
</tr>
<tr><td colspan="5" rowspan="1" data-sheets-value='{"1":2,"2":"Source: DART"}'>Source: DART</td></tr>
</tbody>
</table><p>Below is the lockup pledge as a % of subscriptions.</p><p>Nearly 60% subscribed without lockup pledge.</p><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td style="width: 214.545px;" data-sheets-value='{"1":2,"2":"Lockup"}'>Lockup</td>
<td style="text-align: right; width: 185.455px;" data-sheets-value='{"1":2,"2":"Qty."}'>Qty.</td>
<td style="text-align: right; width: 163.636px;" data-sheets-value='{"1":2,"2":"% of subsc"}'>% of subscription</td>
</tr>
<tr>
<td style="width: 214.545px;" data-sheets-value='{"1":2,"2":"6 months"}'>6 months</td>
<td style="text-align: right; width: 185.455px;" data-sheets-value='{"1":3,"3":2194034000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>2,194,034,000</td>
<td style="text-align: right; width: 163.636px;" data-sheets-value='{"1":3,"3":0.13627910091946424}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R53C2">13.63%</td>
</tr>
<tr>
<td style="width: 214.545px;" data-sheets-value='{"1":2,"2":"3 months"}'>3 months</td>
<td style="text-align: right; width: 185.455px;" data-sheets-value='{"1":3,"3":3595764000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>3,595,764,000</td>
<td style="text-align: right; width: 163.636px;" data-sheets-value='{"1":3,"3":0.22334543814661778}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R53C2">22.33%</td>
</tr>
<tr>
<td style="width: 214.545px;" data-sheets-value='{"1":2,"2":"1 month"}'>1 month</td>
<td style="text-align: right; width: 185.455px;" data-sheets-value='{"1":3,"3":3246456500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>3,246,456,500</td>
<td style="text-align: right; width: 163.636px;" data-sheets-value='{"1":3,"3":0.20164873151197776}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R53C2">20.16%</td>
</tr>
<tr>
<td style="width: 214.545px;" data-sheets-value='{"1":2,"2":"15 days"}'>15 days</td>
<td style="text-align: right; width: 185.455px;" data-sheets-value='{"1":3,"3":610574000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>610,574,000</td>
<td style="text-align: right; width: 163.636px;" data-sheets-value='{"1":3,"3":0.03792487981717738}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R53C2">3.79%</td>
</tr>
<tr>
<td style="width: 214.545px;" data-sheets-value='{"1":2,"2":"None"}'>None</td>
<td style="text-align: right; width: 185.455px;" data-sheets-value='{"1":3,"3":9646828500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>9,646,828,500</td>
<td style="text-align: right; width: 163.636px;" data-sheets-value='{"1":3,"3":0.5991981503952372}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R53C2">59.92%</td>
</tr>
<tr>
<td style="width: 214.545px;" data-sheets-value='{"1":2,"2":"Total"}'>Total</td>
<td style="text-align: right; width: 185.455px;" data-sheets-value='{"1":3,"3":16099563214}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>16,099,563,214</td>
<td style="text-align: right; width: 163.636px;" data-sheets-value='{"1":3,"3":1}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R53C2">100.00%</td>
</tr>
<tr><td colspan="3" rowspan="1" style="width: 605.455px;" data-sheets-value='{"1":2,"2":"Source: DART"}'>Source: DART</td></tr>
</tbody>
</table><p>A little surprisingly, 85% of the allocation went to lockup pledgers.</p><p>What can be deducted from it? It indicates that this IPO may have attracted more long-term investors than what we usually see from Korea's local IPOs.</p><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Lockup"}'>Lockup</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Qty."}'>Qty.</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"% of alloc"}'>% of allocation</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"% of SO"}'>% of SO</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"6 months"}'>6 months</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":3948100}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>3,948,100</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.3127827292533175}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R14C2">31.28%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.05160915032679739}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-2]/R28C2">5.16%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"3 months"}'>3 months</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":3331000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>3,331,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.2638938403644286}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R14C2">26.39%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.04354248366013072}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-2]/R28C2">4.35%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"1 month"}'>1 month</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":3118610}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>3,118,610</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.24706753812636165}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R14C2">24.71%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.040766143790849675}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-2]/R28C2">4.08%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"15 days"}'>15 days</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":364380}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>364,380</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.02886749851455734}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R14C2">2.89%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.00476313725490196}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-2]/R28C2">0.48%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"None"}'>None</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1860410}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>1,860,410</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.14738839374133492}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R14C2">14.74%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.02431908496732026}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-2]/R28C2">2.43%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Total"}'>Total</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":12622500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>12,622,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R14C2">100.00%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.165}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-2]/R28C2">16.50%</td>
</tr>
<tr><td colspan="4" rowspan="1" data-sheets-value='{"1":2,"2":"Source: DART"}'>Source: DART</td></tr>
</tbody>
</table><p>Following is the post-IPO shareholding.</p><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Shareholder"}'>Post-IPO shareholding</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Qty."}'>Qty.</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"%"}'>%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Lock-up"}'>Lock-up</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"SK Chemical"}'>SK Chemical</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":52350000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>52,350,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.6843137254901961}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">68.43%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"6 months"}'>6 months</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"ESOP"}'>ESOP</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":4490400}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-17]C[2]">4,490,400</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.058698039215686275}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">5.87%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"1 year"}'>1 year</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Institutional"}'>Institutional</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":12622500}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=sum(R[1]C[0]:R[5]C[0])">12,622,500</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.165}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">16.50%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"-"}'>-</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":" - 6 months"}'>- 6 months</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":3948100}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-12]C[0]">3,948,100</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.05160915032679739}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">5.16%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"6 months"}'>6 months</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":" - 3 months"}'>- 3 months</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":3331000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-12]C[0]">3,331,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.04354248366013072}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">4.35%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"3 months"}'>3 months</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":" - 1 month"}'>- 1 month</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":3118610}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-12]C[0]">3,118,610</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.040766143790849675}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">4.08%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"1 month"}'>1 month</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":" - 15 days"}'>- 15 days</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":364380}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-12]C[0]">364,380</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.00476313725490196}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">0.48%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"15 days"}'>15 days</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":" - None"}'>- None</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1860410}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-12]C[0]">1,860,410</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.02431908496732026}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">2.43%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"None"}'>None</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Retail"}'>Retail</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":5837100}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[-22]C[2]">5,837,100</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.07630196078431373}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">7.63%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"None"}'>None</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Other"}'>Other</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1200000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}' data-sheets-formula="=R[1]C[0]-R[-9]C[0]-R[-8]C[0]-R[-7]C[0]-R[-1]C[0]">1,200,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.01568627450980392}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=R[0]C[-1]/R28C2">1.57%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"None"}'>None</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Total"}'>Total</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":76500000}' data-sheets-numberformat='{"1":2,"2":"#,##0","3":1}'>76,500,000</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1}' data-sheets-numberformat='{"1":3,"2":"0.00%","3":1}' data-sheets-formula="=sum(R[-10]C[0]:R[-8]C[0])+R[-2]C[0]+R[-1]C[0]">100.00%</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"-"}'>-</td>
</tr>
<tr><td colspan="4" rowspan="1" data-sheets-value='{"1":2,"2":"Source: DART"}'>Source: DART</td></tr>
</tbody>
</table>

Below is the final allocation.









Type
Initial
Subscription
Allocation
%


ESOP
4,590,000
4,490,400
4,490,400
19.57%


Institutional
12,622,500
12,622,500
12,622,500
55.00%


Retail
5,737,500
1,957,531,110
5,837,100
25.43%


Total
22,950,000
1,974,644,010
22,950,000
100.00%

Source: DART

Below is the lockup pledge as a % of subscriptions.Nearly 60% subscribed without lockup pledge.







Lockup
Qty.
% of subscription


6 months
2,194,034,000
13.63%


3 months
3,595,764,000
22.33%


1 month
3,246,456,500
20.16%


15 days
610,574,000
3.79%


None
9,646,828,500
59.92%


Total
16,099,563,214
100.00%

Source: DART

A little surprisingly, 85% of the allocation went to lockup pledgers.What can be deducted from it? It indicates that this IPO may have attracted more long-term investors than what we usually see from Korea's local IPOs.








Lockup
Qty.
% of allocation
% of SO


6 months
3,948,100
31.28%
5.16%


3 months
3,331,000
26.39%
4.35%


1 month
3,118,610
24.71%
4.08%


15 days
364,380
2.89%
0.48%


None
1,860,410
14.74%
2.43%


Total
12,622,500
100.00%
16.50%

Source: DART

Following is the post-IPO shareholding.








Post-IPO shareholding
Qty.
%
Lock-up


SK Chemical
52,350,000
68.43%
6 months


ESOP
4,490,400
5.87%
1 year


Institutional
12,622,500
16.50%
-


- 6 months
3,948,100
5.16%
6 months


- 3 months
3,331,000
4.35%
3 months


- 1 month
3,118,610
4.08%
1 month


- 15 days
364,380
0.48%
15 days


- None
1,860,410
2.43%
None


Retail
5,837,100
7.63%
None


Other
1,200,000
1.57%
None


Total
76,500,000
100.00%
-

Source: DART



SK Bioscience IPO: Allocation & Lockup Schedule

South Korea

SK Bioscience Co.,Ltd. operates biopharmaceutical businesses. The Company produces vaccines, sky cellflu prefilled syringes, sky cellflu quadrivalent prefilled syringes, varicella drugs, and other related products. SK Bioscience markets its products throughout South Korea.

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SK Bioscience (302440 KS)

SK Bioscience: K200 Fast Entry, Lock-Up Expiry, Loan/Short Balance, & Exit Points

This post checks the current situation of SK Bioscience on K200 fast entry, lock-up expiry, loan/short balance, and potential exit points.

Sanghyun Park

What Happened to SK Bioscience on Day 2 (Feat. Kyobo Securities)?

This post looks at the suspicious activities seen at the Kyobo account on SK Biosciences' Day 1 and Day 2 and what it can mean for the price in the...

Sanghyun Park

SK Bioscience to KOSPI 200 Is a Done Deal & A Likely Deletion

This post has the latest update on KOSPI 200 June rebalancing and examines SK Bioscience's KOSPI 200 fast entry situation along with a likely...

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Germany, France, Italy, & Spain Suspend AstraZeneca COVID-19 Vaccinations: Impact on SK Bioscience

While we remain bullish on the SK Bioscience IPO, the fact that 15 countries have decided to temporarily suspend AstraZeneca COVID-19 vaccines will...

Douglas Kim

SK Bioscience IPO: Allocation & Lockup Schedule

This post details SK Bioscience IPO's allocation results and lockup schedule (also the index float for KOSPI 200 fast-entry).

Sanghyun Park

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