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.

Index Rebalance

<ul>
<li>We generally detect an increase in the buying volume of local institutions for stocks moving from Large Cap to Mid Cap ahead of the KOSPI size index rebalancing.</li>
<li>At the last close, Mid→Large: Pan Ocean, Hyundai Mipo, BGF Retail, &amp; OCI / Large→Mid: Hanmi Science, KCC, DB Hitek, &amp; KEPCO E&amp;C
</li>
<li>The level of price impact/correlation in the last rebalancing was exceptionally high. This is reason enough for us to consider setting up a preemptive position for this rebalancing event.</li>
</ul>


We generally detect an increase in the buying volume of local institutions for stocks moving from Large Cap to Mid Cap ahead of the KOSPI size index rebalancing.
At the last close, Mid→Large: Pan Ocean, Hyundai Mipo, BGF Retail, &amp; OCI / Large→Mid: Hanmi Science, KCC, DB Hitek, &amp; KEPCO E&amp;C

The level of price impact/correlation in the last rebalancing was exceptionally high. This is reason enough for us to consider setting up a preemptive position for this rebalancing event.


KOSPI Size Index Series: September Rebalancing Preview

Event-Driven

<ul>
<li>Three local indexes in Korea face rebalancing trading on March 10, supposedly at the close.</li>
<li>They are 1. KOSPI 200 Fast Entry for LG Energy Solution, 2. KOSPI Size Index Series biannual rebalancing, and 3. KRX BBIG K-New Deal biannual rebalancing.</li>
<li>There are 18 stocks in total, which have an estimated passive flow of ±0.5x ADTV &amp; an average daily trade value of +₩5.0B.</li>
</ul>


Three local indexes in Korea face rebalancing trading on March 10, supposedly at the close.
They are 1. KOSPI 200 Fast Entry for LG Energy Solution, 2. KOSPI Size Index Series biannual rebalancing, and 3. KRX BBIG K-New Deal biannual rebalancing.
There are 18 stocks in total, which have an estimated passive flow of ±0.5x ADTV &amp; an average daily trade value of +₩5.0B.


Combined Rebalancing Flow Estimations of Three Indexes in Korea on March 10

<ul>
<li>KOSPI Size Index Series rebalances twice a year in March and September. The base date is February 28. It is a three-month review, so, from December 1 to February 28.</li>
<li>In the KOSPI size index rebalancing, an increase in the buying volume of local institutions for stocks moving from LargeCap to MidCap is generally detected. </li>
<li>14 new MidCap entrants are expected to have a sizable passive impact.</li>
</ul>


KOSPI Size Index Series rebalances twice a year in March and September. The base date is February 28. It is a three-month review, so, from December 1 to February 28.
In the KOSPI size index rebalancing, an increase in the buying volume of local institutions for stocks moving from LargeCap to MidCap is generally detected. 
14 new MidCap entrants are expected to have a sizable passive impact.


KOSPI Size Index Series Rebalancing Screening Results

Hyundai Oilbank has officially started the IPO process. It has reportedly sent out an RFP to local and overseas brokerages. The company is expected to pursue a listing on the KOSPI in early 2022.<div class="content-highlight">
After stopping preparations for an IPO in 2019, it again seeks to enter the stock market after more than two years. This is the third challenge since 2012, when the first listing attempt was made.
<ul>
<li>In 2012, the company gave up halfway due to the sharp drop in international oil prices. </li>
<li>In 2017, Hyundai Oilbank was designated as a company subject to accounting supervision. So the IPO was again abandoned midway.</li>
</ul>
</div>In January 2019, Hyundai Oilbank raised an equity investment worth ₩1.37T from Saudi Aramco for a 17% stake, and as a result, officially announced the suspension of the IPO. So, the estimated IPO valuation at this point is about ₩8.1T. The Aramco investment of ₩1.37T for a 17% stake translates into an ₩8.1T valuation.Hyundai Oilbank recorded an operating profit of ₩412.8B in the first quarter of this year, successfully turning to black compared to the previous quarter.

Hyundai Oilbank has officially started the IPO process. It has reportedly sent out an RFP to local and overseas brokerages. The company is expected to pursue a listing on the KOSPI in early 2022.
After stopping preparations for an IPO in 2019, it again seeks to enter the stock market after more than two years. This is the third challenge since 2012, when the first listing attempt was made.

In 2012, the company gave up halfway due to the sharp drop in international oil prices. 
In 2017, Hyundai Oilbank was designated as a company subject to accounting supervision. So the IPO was again abandoned midway.

In January 2019, Hyundai Oilbank raised an equity investment worth ₩1.37T from Saudi Aramco for a 17% stake, and as a result, officially announced the suspension of the IPO. So, the estimated IPO valuation at this point is about ₩8.1T. The Aramco investment of ₩1.37T for a 17% stake translates into an ₩8.1T valuation.Hyundai Oilbank recorded an operating profit of ₩412.8B in the first quarter of this year, successfully turning to black compared to the previous quarter.

KOSE (Long) Vs. HHI Holdings (Short) Pair Trade on Hyundai Oilbank IPO

I grouped Korea's closely correlated sector pairs within the same GICS sub-category. I applied the additional filtering: 1. a market cap of ₩1T or more and 2. stocks eligible to short-selling (KOSPI 200 and KOSDAQ 150). This grouping returns 141 sector pairs worthy of close monitoring for a long/short trade.<div class="content-highlight">You can find all the 141 sector pairs with the 180-day correlation coefficients and the sigma on a 20-day moving average in the attached file.</div><hr>Below are the top 10 most correlated sector pairs above a 180-day correlation coefficient of 0.95 or higher. They are mostly cyclical stocks, which are in line with our conventional thinking that cyclical stocks have a stronger tendency to move together than growth stocks.<table class="table table-striped">
<colgroup>
<col width="200">
<col width="200">
<col width="100">
<col width="100">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Comp 1"}'>Comp 1</td>
<td data-sheets-value='{"1":2,"2":"Comp 2"}'>Comp 2</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"180-day correlation coefficient"}'>180-day correlation coefficient</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Business type"}'>Business type</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hyundai Mipo Dockyard Co., Ltd."}'>Hyundai Mipo Dockyard Co., Ltd.</td>
<td data-sheets-value='{"1":2,"2":"Korea Shipbuilding &amp;Offshore Engn Co Ltd"}'>Korea Shipbuilding &amp;Offshore Engn Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.97405}'>0.97405</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Shipbuilding"}'>Shipbuilding</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hyundai Department Store Co Ltd"}'>Hyundai Department Store Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Lotte Shopping Co Ltd"}'>Lotte Shopping Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.97033}'>0.97033</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Retail"}'>Retail</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"GS Engineering &amp; Construction Corp"}'>GS Engineering &amp; Construction Corp</td>
<td data-sheets-value='{"1":2,"2":"Hyundai Engineering &amp; Construction Co."}'>Hyundai Engineering &amp; Construction Co.</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.96379}'>0.96379</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Construction"}'>Construction</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"HYUNDAI MOBIS CO., LTD."}'>HYUNDAI MOBIS CO., LTD.</td>
<td data-sheets-value='{"1":2,"2":"Hyundai Motor Co"}'>Hyundai Motor Co</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.96145}'>0.96145</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Automotive"}'>Automotive</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td data-sheets-value='{"1":2,"2":"KB Financial Group Inc"}'>KB Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.95989}'>0.95989</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Commercial bank"}'>Commercial bank</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"LG Innotek Co., Ltd."}'>LG Innotek Co., Ltd.</td>
<td data-sheets-value='{"1":2,"2":"LG Electronics Inc."}'>LG Electronics Inc.</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.95986}'>0.95986</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"IT"}'>IT</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hansol Chemical Co Ltd"}'>Hansol Chemical Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Posco Chemical Co Ltd"}'>Posco Chemical Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.95374}'>0.95374</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Chemical"}'>Chemical</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hanwha Aerospace Co Ltd"}'>Hanwha Aerospace Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Korea Aerospace Industries Ltd"}'>Korea Aerospace Industries Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.95243}'>0.95243</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Aerospace"}'>Aerospace</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"KG DongbuSteel Co Ltd"}'>KG DongbuSteel Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"POSCO"}'>POSCO</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.95174}'>0.95174</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Steel"}'>Steel</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"KG DongbuSteel Co Ltd"}'>KG DongbuSteel Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Hyundai Steel Co"}'>Hyundai Steel Co</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.94972}'>0.94972</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Steel"}'>Steel</td>
</tr>
<tr><td colspan="4" rowspan="1" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table>Below are the top 10 stocks suffering the harshest diversion on a 20-day moving average among those having a 180-day correlation coefficient of 0.80 or higher. E-Mart and Lotte Shopping top the list with a -3.27σ and a correlation coefficient of 0.85. Silicon Works/LG Electronics and BGF Retail/E-Mart follow as they are also in a heavy diversion at +2.79σ and +2.58σ, respectively. Woori Financial/KB Financial and Pan Ocean/HMM aren't far away either.<table class="table table-striped">
<colgroup>
<col width="200">
<col width="200">
<col width="100">
<col width="100">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Comp 1"}'>Comp 1</td>
<td data-sheets-value='{"1":2,"2":"Comp 2"}'>Comp 2</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"180-day correlation coefficient"}'>180-day correlation coefficient</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Sigma on a 20-day moving average"}'>Sigma on a 20-day moving average</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"E-Mart Inc"}'>E-Mart Inc</td>
<td data-sheets-value='{"1":2,"2":"Lotte Shopping Co Ltd"}'>Lotte Shopping Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.8512795053699035}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.85128</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-3.2691212591928522}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>-3.27</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Silicon Works Co Ltd"}'>Silicon Works Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"LG Electronics Inc."}'>LG Electronics Inc.</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.8241051481549174}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.82411</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":2.794228170493331}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>2.79</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"BGF Retail Co Ltd"}'>BGF Retail Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"E-Mart Inc"}'>E-Mart Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.847331528848073}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.84733</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":2.5786176868102397}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>2.58</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Woori Financial Group Inc"}'>Woori Financial Group Inc</td>
<td data-sheets-value='{"1":2,"2":"KB Financial Group Inc"}'>KB Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.8451741524292201}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.84517</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-2.465145723060455}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>-2.47</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Pan Ocean Co Ltd"}'>Pan Ocean Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"HMM Co Ltd"}'>HMM Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.935905540581882}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.93591</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-2.33130934144712}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>-2.33</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hyundai Mipo Dockyard Co., Ltd."}'>Hyundai Mipo Dockyard Co., Ltd.</td>
<td data-sheets-value='{"1":2,"2":"Korea Shipbuilding &amp;Offshore Engn Co Ltd"}'>Korea Shipbuilding &amp;Offshore Engn Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.9740462682980674}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.97405</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":2.2401490556241117}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>2.24</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"SK Innovation Co Ltd"}'>SK Innovation Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Samsung SDI Co Ltd"}'>Samsung SDI Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.9458691690353619}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.94587</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":2.1867120471913637}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>2.19</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hanwha Aerospace Co Ltd"}'>Hanwha Aerospace Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Korea Aerospace Industries Ltd"}'>Korea Aerospace Industries Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.9524282780324967}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.95243</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":2.086128932784612}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>2.09</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hyundai Mipo Dockyard Co., Ltd."}'>Hyundai Mipo Dockyard Co., Ltd.</td>
<td data-sheets-value='{"1":2,"2":"Daewoo Engineering &amp; Construction Co Ltd"}'>Daewoo Engineering &amp; Construction Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.8206840084895756}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.82068</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":1.9737577992711182}' data-sheets-numberformat='{"1":2,"2":"#,##0.00","3":1}'>1.97</td>
</tr>
<tr>
<td>Woori Financial Group Inc</td>
<td>Shinhan Financial Group Co., Ltd.</td>
<td style="text-align: right;">0.87902</td>
<td style="text-align: right;">-1.93</td>
</tr>
<tr><td colspan="4" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table>

I grouped Korea's closely correlated sector pairs within the same GICS sub-category. I applied the additional filtering: 1. a market cap of ₩1T or more and 2. stocks eligible to short-selling (KOSPI 200 and KOSDAQ 150). This grouping returns 141 sector pairs worthy of close monitoring for a long/short trade.You can find all the 141 sector pairs with the 180-day correlation coefficients and the sigma on a 20-day moving average in the attached file.Below are the top 10 most correlated sector pairs above a 180-day correlation coefficient of 0.95 or higher. They are mostly cyclical stocks, which are in line with our conventional thinking that cyclical stocks have a stronger tendency to move together than growth stocks.








Comp 1
Comp 2
180-day correlation coefficient
Business type


Hyundai Mipo Dockyard Co., Ltd.
Korea Shipbuilding &amp;Offshore Engn Co Ltd
0.97405
Shipbuilding


Hyundai Department Store Co Ltd
Lotte Shopping Co Ltd
0.97033
Retail


GS Engineering &amp; Construction Corp
Hyundai Engineering &amp; Construction Co.
0.96379
Construction


HYUNDAI MOBIS CO., LTD.
Hyundai Motor Co
0.96145
Automotive


Shinhan Financial Group Co., Ltd.
KB Financial Group Inc
0.95989
Commercial bank


LG Innotek Co., Ltd.
LG Electronics Inc.
0.95986
IT


Hansol Chemical Co Ltd
Posco Chemical Co Ltd
0.95374
Chemical


Hanwha Aerospace Co Ltd
Korea Aerospace Industries Ltd
0.95243
Aerospace


KG DongbuSteel Co Ltd
POSCO
0.95174
Steel


KG DongbuSteel Co Ltd
Hyundai Steel Co
0.94972
Steel

Source: KRX

Below are the top 10 stocks suffering the harshest diversion on a 20-day moving average among those having a 180-day correlation coefficient of 0.80 or higher. E-Mart and Lotte Shopping top the list with a -3.27σ and a correlation coefficient of 0.85. Silicon Works/LG Electronics and BGF Retail/E-Mart follow as they are also in a heavy diversion at +2.79σ and +2.58σ, respectively. Woori Financial/KB Financial and Pan Ocean/HMM aren't far away either.








Comp 1
Comp 2
180-day correlation coefficient
Sigma on a 20-day moving average


E-Mart Inc
Lotte Shopping Co Ltd
0.85128
-3.27


Silicon Works Co Ltd
LG Electronics Inc.
0.82411
2.79


BGF Retail Co Ltd
E-Mart Inc
0.84733
2.58


Woori Financial Group Inc
KB Financial Group Inc
0.84517
-2.47


Pan Ocean Co Ltd
HMM Co Ltd
0.93591
-2.33


Hyundai Mipo Dockyard Co., Ltd.
Korea Shipbuilding &amp;Offshore Engn Co Ltd
0.97405
2.24


SK Innovation Co Ltd
Samsung SDI Co Ltd
0.94587
2.19


Hanwha Aerospace Co Ltd
Korea Aerospace Industries Ltd
0.95243
2.09


Hyundai Mipo Dockyard Co., Ltd.
Daewoo Engineering &amp; Construction Co Ltd
0.82068
1.97


Woori Financial Group Inc
Shinhan Financial Group Co., Ltd.
0.87902
-1.93

Source: KRX



Korea Sector Long/Short Pairs Trade Ideas

South Korea

HD HYUNDAI MIPO. operates shipbuilding businesses. The Company produces gas carriers, car carriers, bulk carriers, special ships, and other products. HD HYUNDAI MIPO conducts businesses in South Korea.

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HD Hyundai Mipo (010620 KS)

KOSPI Size Index Series: September Rebalancing Preview

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Combined Rebalancing Flow Estimations of Three Indexes in Korea on March 10

This post estimates the combined rebalancing flows of Korea's three local indexes, which should be seen on March 10, supposedly at the close.

Sanghyun Park

KOSPI Size Index Series Rebalancing Screening Results

This post has the screening results of the KOSPI Size Index Series rebalancing: 14 new MidCap entrants are expected to have a sizable passive impact.

Sanghyun Park

KOSE (Long) Vs. HHI Holdings (Short) Pair Trade on Hyundai Oilbank IPO

Hyundai Oilbank has started the IPO process, likely acting as a discount factor on HHIH's share price. So, from a swing trade perspective, Long...

Sanghyun Park

Korea Sector Long/Short Pairs Trade Ideas

This post investigates the 141 sector pairs worthy of close monitoring for a long/short trade. They are selected based on the GICS sub-categories,...

Sanghyun Park

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