Korea / Special Sits, Pairs, Index Rebalancing, Clepsydra Capital

Sanghyun Park

Korea / Special Sits, Pairs, Index Rebalancing

Clepsydra Capital

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

Event-Driven

<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>
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<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">
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<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">
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<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

SK Biobioscience IPO price hit the ceiling at ₩65,000, raising ₩1.5T with a ₩5T market cap.







Pricing (₩B)
Floor
Ceiling (final)


Price (₩)
₩49,000
₩65,000


Base deal size
1,124.6
1,491.8


- Institutional allotment
618.5
820.5


Implied market cap
3,748.5
4,972.5

Source: DART

Literally, all of the pricing sits at or above the high end.Despite several concerns, many investors must not be complaining about the price level.It is worth noting that foreign funds priced it a little less favorably than local investors.









By investor type
Institution
%
Demand
%


Local PO funds
513
35.04%
5,673,643,000
35.24%


Local BR/IA
32
2.19%
358,760,000
2.23%


Local pension/bank/insurance
281
19.19%
3,056,667,000
18.99%


Local others (hedge funds)
346
23.63%
3,927,753,000
24.40%


Foreign (foreign funds)
130
8.88%
1,150,736,214
7.15%


Foreign others (local hot money)
162
11.07%
1,932,004,000
12.00%


Total
1,464
100.00%
16,099,563,214
100.00%


Subscription rate
1,275.47

Source: DART











By price
Institution
%
Demand
%


Above high end
1,135
77.53%
12,982,935,500
80.64%


75-100% of high end
271
18.51%
2,592,017,714
16.10%


50-75% of high end
0
0.00%
0
0.00%


25-50% of high end
0
0.00%
0
0.00%


0-25% of high end
0
0.00%
0
0.00%


Median
0
0.00%
0
0.00%


75-100% of median
0
0.00%
0
0.00%


50-75% of median
0
0.00%
0
0.00%


25-50% of median
0
0.00%
0
0.00%


0-25% of median
0
0.00%
0
0.00%


Low end or below
0
0.00%
0
0.00%


Unspecified
58
3.96%
524,610,000
3.26%


Total
1,464
100.00%
16,099,563,214
100.00%

Source: DART











Local PO funds
Institution
%
Demand
%


Above high end
412
80.31%
4,614,489,000
81.33%


75-100% of high end
55
10.72%
610,783,000
10.77%


50-75% of high end
0
0.00%
0
0.00%


25-50% of high end
0
0.00%
0
0.00%


0-25% of high end
0
0.00%
0
0.00%


Median
0
0.00%
0
0.00%


75-100% of median
0
0.00%
0
0.00%


50-75% of median
0
0.00%
0
0.00%


25-50% of median
0
0.00%
0
0.00%


0-25% of median
0
0.00%
0
0.00%


Low end or below
0
0.00%
0
0.00%


Unspecified
46
8.97%
448,371,000
7.90%


Total
513
100.00%
5,673,643,000
100.00%

Source: DART











Local BR/IA
Institution
%
Demand
%


Above high end
23
71.88%
268,023,000
74.71%


75-100% of high end
9
28.13%
90,737,000
25.29%


50-75% of high end
0
0.00%
0
0.00%


25-50% of high end
0
0.00%
0
0.00%


0-25% of high end
0
0.00%
0
0.00%


Median
0
0.00%
0
0.00%


75-100% of median
0
0.00%
0
0.00%


50-75% of median
0
0.00%
0
0.00%


25-50% of median
0
0.00%
0
0.00%


0-25% of median
0
0.00%
0
0.00%


Low end or below
0
0.00%
0
0.00%


Unspecified
0
0.00%
0
0.00%


Total
32
100.00%
358,760,000
100.00%

Source: DART











Local pension/bank/insurance
Institution
%
Demand
%


Above high end
233
82.92%
2,553,390,000
83.54%


75-100% of high end
43
15.30%
452,784,000
14.81%


50-75% of high end
0
0.00%
0
0.00%


25-50% of high end
0
0.00%
0
0.00%


0-25% of high end
0
0.00%
0
0.00%


Median
0
0.00%
0
0.00%


75-100% of median
0
0.00%
0
0.00%


50-75% of median
0
0.00%
0
0.00%


25-50% of median
0
0.00%
0
0.00%


0-25% of median
0
0.00%
0
0.00%


Low end or below
0
0.00%
0
0.00%


Unspecified
5
1.78%
50,493,000
1.65%


Total
281
100.00%
3,056,667,000
100.00%

Source: DART











Local others (hedge funds)
Institution
%
Demand
%


Above high end
294
84.97%
3,412,833,000
86.89%


75-100% of high end
45
13.01%
489,174,000
12.45%


50-75% of high end
0
0.00%
0
0.00%


25-50% of high end
0
0.00%
0
0.00%


0-25% of high end
0
0.00%
0
0.00%


Median
0
0.00%
0
0.00%


75-100% of median
0
0.00%
0
0.00%


50-75% of median
0
0.00%
0
0.00%


25-50% of median
0
0.00%
0
0.00%


0-25% of median
0
0.00%
0
0.00%


Low end or below
0
0.00%
0
0.00%


Unspecified
7
2.02%
25,746,000
0.66%


Total
346
100.00%
3,927,753,000
100.00%

Source: DART











Foreign (foreign funds)
Institution
%
Demand
%


Above high end
22
16.92%
240,862,500
20.93%


75-100% of high end
108
83.08%
909,873,714
79.07%


50-75% of high end
0
0.00%
0
0.00%


25-50% of high end
0
0.00%
0
0.00%


0-25% of high end
0
0.00%
0
0.00%


Median
0
0.00%
0
0.00%


75-100% of median
0
0.00%
0
0.00%


50-75% of median
0
0.00%
0
0.00%


25-50% of median
0
0.00%
0
0.00%


0-25% of median
0
0.00%
0
0.00%


Low end or below
0
0.00%
0
0.00%


Unspecified
0
0.00%
0
0.00%


Total
130
100.00%
1,150,736,214
100.00%

Source: DART











Foreign others (local hot money)
Institution
%
Demand
%


Above high end
151
93.21%
1,893,338,000
98.00%


75-100% of high end
11
6.79%
38,666,000
2.00%


50-75% of high end
0
0.00%
0
0.00%


25-50% of high end
0
0.00%
0
0.00%


0-25% of high end
0
0.00%
0
0.00%


Median
0
0.00%
0
0.00%


75-100% of median
0
0.00%
0
0.00%


50-75% of median
0
0.00%
0
0.00%


25-50% of median
0
0.00%
0
0.00%


0-25% of median
0
0.00%
0
0.00%


Low end or below
0
0.00%
0
0.00%


Unspecified
0
0.00%
0
0.00%


Total
162
100.00%
1,932,004,000
100.00%

Source: DART



SK Bioscience IPO: Bookbuilding Results & KOSPI 200 Fast Entry

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)

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...

Sanghyun Park

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

SK Bioscience IPO: Bookbuilding Results & KOSPI 200 Fast Entry

This post has SK Bioscience's IPO book-building results and examines the situation/likelihood of the KOSPI 200 fast entry.

Sanghyun Park

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