Head of Research, IPOs & Placements, Aequitas Research

Sumeet Singh

Head of Research, IPOs & Placements

IPOs and Placements coverage across Asia Pacific

Aequitas Research

We cover all listing across the Asia Pacific along with follow-up placements. As of Dec 2020, our team had covered 539 IPOs with a hit rate of 73.5% and 685 placements with a hit rate of 66.8%.

We combine fundamental bottom-up views with our proprietary quantitative framework to provide a holistic coverage across new listings and placements. Our coverage includes pre-IPO notes before the deal is launched, follow-up analysis once the deal is live and post-listing trading analysis as well.

Event-Driven

<div><p>Kakao Bank (KB), South Korea’s largest digital bank, aims to raise up to US$2.2bn in its South Korean IPO. Kakao held a 31.62% stake prior to listing.</p></div><div><p>Even though Kakao started operations only in July 2017, by 2020 it had already garnered more than 1% market share of overall loans and deposits in South Korea. KB’s loans have grown by 138% over 2018 to 1Q21 to reach KRW21.6tn, while its deposits have grown by 197% over the same period to reach KRW24.2tn. </p></div><div><p>Over 2018-20, its total income (Net interest income plus net fee income) grew by 216%, while it went from booking a loss in 2018 to reporting a 26% profit margin by 2020. KB’s total income was up another 60% YoY in 1Q21, while PAT was up 153% YoY.</p></div><div><p>We have covered various aspects of the deal in our previous notes, <a href="https://www.smartkarma.com/insights/kakao-bank-ipo-riding-on-parent-s-network-but-boundaries-with-kakao-pay-remain-unclear" target="_blank" rel="noopener">Kakao Bank IPO - Riding on parents network but boundaries with Kakao Pay remain unclear</a> and <a href="https://www.smartkarma.com/insights/kakao-bank-ipo-pricey-as-it-should-be" target="_blank" rel="noopener">Kakao Bank IPO - Pricey as It Should Be</a>.</p></div><div><p>In this note, we will talk about out the what the lack of lock-up on some investors could mean for index inclusion.</p></div>

Kakao Bank (KB), South Korea’s largest digital bank, aims to raise up to US$2.2bn in its South Korean IPO. Kakao held a 31.62% stake prior to listing.Even though Kakao started operations only in July 2017, by 2020 it had already garnered more than 1% market share of overall loans and deposits in South Korea. KB’s loans have grown by 138% over 2018 to 1Q21 to reach KRW21.6tn, while its deposits have grown by 197% over the same period to reach KRW24.2tn. Over 2018-20, its total income (Net interest income plus net fee income) grew by 216%, while it went from booking a loss in 2018 to reporting a 26% profit margin by 2020. KB’s total income was up another 60% YoY in 1Q21, while PAT was up 153% YoY.We have covered various aspects of the deal in our previous notes, Kakao Bank IPO - Riding on parents network but boundaries with Kakao Pay remain unclear and Kakao Bank IPO - Pricey as It Should Be.In this note, we will talk about out the what the lack of lock-up on some investors could mean for index inclusion.

Kakao Bank IPO - Lack of Lock-Up and MSCI, FTSE Index Implications

Korea/Asia, Events, IPOs, Douglas Research Advisory

Douglas Kim

Korea/Asia, Events, IPOs

Cross Asset Strategy

<p>South Korea recorded the highest daily cases (1,378) of COVID-19 on 9 July in the past 18 months. Despite enormous efforts to control COVID-19 through massive mask wearing, social distancing measures, and use of smartphone based tracing system, they have not prevented <a href="https://www.theguardian.com/world/2021/jul/10/seoul-heads-for-lockdown-as-infections-spiral-in-south-korea" target="_blank" rel="noopener">COVID-19</a> cases reaching new highs in South Korea. The chief culprit has been the Delta variant. </p>

South Korea recorded the highest daily cases (1,378) of COVID-19 on 9 July in the past 18 months. Despite enormous efforts to control COVID-19 through massive mask wearing, social distancing measures, and use of smartphone based tracing system, they have not prevented COVID-19 cases reaching new highs in South Korea. The chief culprit has been the Delta variant. 

Highest Daily Cases of COVID-19 in Korea Driven by the Delta Variant - Impact on the Stock Market

<div class="content-highlight">
<p>In this insight, we update on the progress of the M&amp;A of<a href="https://biz.chosun.com/distribution/channel/2021/06/07/PIS7RH6QEZHHPP7AT5BQPVIJKE/" target="_blank" rel="noopener"> Ebay Korea. I</a>n the past several weeks, there have been four parties that have showed interest in acquiring Ebay Korea, including Shinsegae/Naver consortium, Lotte Group, MBK Partners, and SK Telecom. Only the Shinsege/Naver consortium and the Lotte Group have put in preliminary bids for Ebay Korea.</p>
<p>The overall price of Ebay Korea (3rd largest e-commerce player in Korea) has recently come down from 5 trillion won or more to about 3 to 4 trillion won. However, apparently Ebay Korea is still seeking a valuation close to 5 trillion won. </p>
</div>


In this insight, we update on the progress of the M&amp;A of Ebay Korea. In the past several weeks, there have been four parties that have showed interest in acquiring Ebay Korea, including Shinsegae/Naver consortium, Lotte Group, MBK Partners, and SK Telecom. Only the Shinsege/Naver consortium and the Lotte Group have put in preliminary bids for Ebay Korea.
The overall price of Ebay Korea (3rd largest e-commerce player in Korea) has recently come down from 5 trillion won or more to about 3 to 4 trillion won. However, apparently Ebay Korea is still seeking a valuation close to 5 trillion won. 


Who Will Acquire Ebay Korea -  Shinsegae/Naver Consortium or Lotte Group?

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.

<p>I track a total of 141 sector pairs in Korea. Starting this insight, I grouped them into three groups: 1. Financials, 2. Retail, and 3. Industrials.</p><hr><h2>Financials</h2><p>4 pairs from the Financials group have a 180-day correlation coefficient above 0.75 (or close) and a 180-day daily price return beta above 0.70 while sitting below -1.0σ on a 20-day moving average. Of these, <strong>Hana Financial/Shinhan Financial</strong> and <strong>Hyundai Marine/DB Insurance</strong> warrant attention.</p><table class="table table-striped">
<colgroup>
<col width="200">
<col width="200">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td style="width: 241.818px;" data-sheets-value='{"1":2,"2":"Comp 1 (Long)"}'>Comp 1 (Long)</td>
<td style="width: 190px;" data-sheets-value='{"1":2,"2":"Comp 2 (Short)"}'>Comp 2 (Short)</td>
<td style="text-align: right; width: 64.5455px;" data-sheets-value='{"1":2,"2":"Sigma on a 20-day moving average"}'>Sigma on a 20-day moving average</td>
<td style="text-align: right; width: 60.9091px;" data-sheets-value='{"1":2,"2":"180-day correlation coefficient"}'>180-day correlation coefficient</td>
<td style="text-align: right; width: 82.7273px;" data-sheets-value='{"1":2,"2":"Cointegration coefficient (180-day price Δ beta)"}'>Cointegration coefficient (180-day price Δ beta)</td>
</tr>
<tr>
<td style="width: 241.818px;" data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}' data-sheets-formula="=Data!R[-1]C[1]">Mirae Asset Securities Co Ltd</td>
<td style="width: 190px;" data-sheets-value='{"1":2,"2":"Samsung Securities Co Ltd"}' data-sheets-formula="=Data!R[-1]C[1]">Samsung Securities Co Ltd</td>
<td style="text-align: right; width: 64.5455px;" data-sheets-value='{"1":3,"3":-1.222273476903716}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]">-1.22</td>
<td style="text-align: right; width: 60.9091px;" data-sheets-value='{"1":3,"3":0.7539862250329782}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[0]C[3]">0.75</td>
<td style="text-align: right; width: 82.7273px;" data-sheets-value='{"1":3,"3":0.6991098567863366}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[2]C[-3]">0.70</td>
</tr>
<tr>
<td style="width: 241.818px;" data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}' data-sheets-formula="=Data!R[-2]C[3]">Mirae Asset Securities Co Ltd</td>
<td style="width: 190px;" data-sheets-value='{"1":2,"2":"NH Investment &amp; Securities Co Ltd"}' data-sheets-formula="=Data!R[-2]C[3]">NH Investment &amp; Securities Co Ltd</td>
<td style="text-align: right; width: 64.5455px;" data-sheets-value='{"1":3,"3":-1.1519173688233821}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]">-1.15</td>
<td style="text-align: right; width: 60.9091px;" data-sheets-value='{"1":3,"3":0.7418291376039101}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[2]C[3]">0.74</td>
<td style="text-align: right; width: 82.7273px;" data-sheets-value='{"1":3,"3":0.7031067314990199}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[1]C[3]">0.70</td>
</tr>
<tr>
<td style="width: 241.818px;" data-sheets-value='{"1":2,"2":"Hana Financial Group Inc"}' data-sheets-formula="=Data!R[-3]C[5]"><strong>Hana Financial Group Inc</strong></td>
<td style="width: 190px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}' data-sheets-formula="=Data!R[-3]C[5]"><strong>Shinhan Financial Group Co., Ltd.</strong></td>
<td style="text-align: right; width: 64.5455px;" data-sheets-value='{"1":3,"3":-1.0788984328569544}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]"><strong>-1.08</strong></td>
<td style="text-align: right; width: 60.9091px;" data-sheets-value='{"1":3,"3":0.9401716746799647}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[2]C[3]"><strong>0.94</strong></td>
<td style="text-align: right; width: 82.7273px;" data-sheets-value='{"1":3,"3":1.021970873112603}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[0]C[5]"><strong>1.02</strong></td>
</tr>
<tr>
<td style="width: 241.818px;" data-sheets-value='{"1":2,"2":"Hyundai Marine &amp; Fire Insurance Co., Ltd"}' data-sheets-formula="=Data!R[-4]C[7]"><strong>Hyundai Marine &amp; Fire Insurance Co., Ltd</strong></td>
<td style="width: 190px;" data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}' data-sheets-formula="=Data!R[-4]C[7]"><strong>DB Insurance Co Ltd</strong></td>
<td style="text-align: right; width: 64.5455px;" data-sheets-value='{"1":3,"3":-1.0725124068124459}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]"><strong>-1.07</strong></td>
<td style="text-align: right; width: 60.9091px;" data-sheets-value='{"1":3,"3":0.9161479896868121}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[2]C[3]"><strong>0.92</strong></td>
<td style="text-align: right; width: 82.7273px;" data-sheets-value='{"1":3,"3":0.756650231829711}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[-1]C[7]"><strong>0.76</strong></td>
</tr>
<tr><td colspan="5" style="width: 723.636px;" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table><h2>Retail</h2><p>2 pairs from the Retail group have a 180-day correlation coefficient above 0.75 and a 180-day daily price return beta above 0.70 while sitting below -1.0σ on a 20-day moving average: <strong>Lotte Shopping/GS Retail</strong> and <strong>Lotte Shopping/Hyundai Department</strong>.</p><table class="table table-striped">
<colgroup>
<col width="200">
<col width="200">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Comp 1 (Long)"}'>Comp 1 (Long)</td>
<td data-sheets-value='{"1":2,"2":"Comp 2 (Short)"}'>Comp 2 (Short)</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>
<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":"Cointegration coefficient (180-day price Δ beta)"}'>Cointegration coefficient (180-day price Δ beta)</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Lotte Shopping Co Ltd"}' data-sheets-formula="=Data!R[-1]C[1]"><strong>Lotte Shopping Co Ltd</strong></td>
<td data-sheets-value='{"1":2,"2":"GS Retail Co Ltd"}' data-sheets-formula="=Data!R[-1]C[1]"><strong>GS Retail Co Ltd</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-1.5275880593828013}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]"><strong>-1.53</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.8726370244086025}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[0]C[3]"><strong>0.87</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.8778679151859091}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[2]C[-3]"><strong>0.88</strong></td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Lotte Shopping Co Ltd"}' data-sheets-formula="=Data!R[-3]C[5]"><strong>Lotte Shopping Co Ltd</strong></td>
<td data-sheets-value='{"1":2,"2":"Hyundai Department Store Co Ltd"}' data-sheets-formula="=Data!R[-3]C[5]"><strong>Hyundai Department Store Co Ltd</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-1.126049210184334}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]"><strong>-1.13</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.9181905655104922}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[1]C[3]"><strong>0.92</strong></td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.8654420144103113}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[0]C[3]"><strong>0.87</strong></td>
</tr>
<tr><td colspan="5" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table><h2>Industrials</h2><p>4 pairs from the Retail group have a 180-day correlation coefficient above 0.75 and a 180-day daily price return beta above 0.70 (or close) while sitting below -1.0σ on a 20-day moving average.</p><p>Of these, the Celltrion siblings are the most correlated/cointegrated ones with a substantial diversion. I mentioned their trading dynamics in the previous insight. I will cover <strong>KAI/Hanwha Aerospace </strong>and <strong>Korea Shipbuilding/Hyundai Mipo</strong> in this post.</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 style="width: 230px;" data-sheets-value='{"1":2,"2":"Comp 1 (Long)"}'>Comp 1 (Long)</td>
<td style="width: 194.545px;" data-sheets-value='{"1":2,"2":"Comp 2 (Short)"}'>Comp 2 (Short)</td>
<td style="text-align: right; width: 80px;" data-sheets-value='{"1":2,"2":"Sigma on a 20-day moving average"}'>Sigma on a 20-day moving average</td>
<td style="text-align: right; width: 58.1818px;" data-sheets-value='{"1":2,"2":"180-day correlation coefficient"}'>180-day correlation coefficient</td>
<td style="text-align: right; width: 77.2727px;" data-sheets-value='{"1":2,"2":"Cointegration coefficient (180-day price Δ beta)"}'>Cointegration coefficient (180-day price Δ beta)</td>
</tr>
<tr>
<td style="width: 230px;" data-sheets-value='{"1":2,"2":"Korea Aerospace Industries Ltd"}' data-sheets-formula="=Data!R[-1]C[1]"><strong>Korea Aerospace Industries Ltd</strong></td>
<td style="width: 194.545px;" data-sheets-value='{"1":2,"2":"Hanwha Aerospace Co Ltd"}' data-sheets-formula="=Data!R[-1]C[1]"><strong>Hanwha Aerospace Co Ltd</strong></td>
<td style="text-align: right; width: 80px;" data-sheets-value='{"1":3,"3":-1.2612782453636326}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]"><strong>-1.26</strong></td>
<td style="text-align: right; width: 58.1818px;" data-sheets-value='{"1":3,"3":0.9151301901660448}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[2]C[3]"><strong>0.92</strong></td>
<td style="text-align: right; width: 77.2727px;" data-sheets-value='{"1":3,"3":0.6164208180229479}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[2]C[1]"><strong>0.62</strong></td>
</tr>
<tr>
<td style="width: 230px;" data-sheets-value='{"1":2,"2":"Celltrion, Inc."}' data-sheets-formula="=Data!R[-2]C[3]">Celltrion, Inc.</td>
<td style="width: 194.545px;" data-sheets-value='{"1":2,"2":"Celltrion HealthCare Co Ltd"}' data-sheets-formula="=Data!R[-2]C[3]">Celltrion HealthCare Co Ltd</td>
<td style="text-align: right; width: 80px;" data-sheets-value='{"1":3,"3":-1.178153322167809}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]">-1.18</td>
<td style="text-align: right; width: 58.1818px;" data-sheets-value='{"1":3,"3":0.8365102631201596}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[2]C[3]">0.84</td>
<td style="text-align: right; width: 77.2727px;" data-sheets-value='{"1":3,"3":0.7643376618440517}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[1]C[3]">0.76</td>
</tr>
<tr>
<td style="width: 230px;" data-sheets-value='{"1":2,"2":"Celltrion Pharm Inc"}' data-sheets-formula="=Data!R[-3]C[5]">Celltrion Pharm Inc</td>
<td style="width: 194.545px;" data-sheets-value='{"1":2,"2":"Celltrion HealthCare Co Ltd"}' data-sheets-formula="=Data!R[-3]C[5]">Celltrion HealthCare Co Ltd</td>
<td style="text-align: right; width: 80px;" data-sheets-value='{"1":3,"3":-1.066075796850893}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]">-1.07</td>
<td style="text-align: right; width: 58.1818px;" data-sheets-value='{"1":3,"3":0.8799929165201812}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[3]C[3]">0.88</td>
<td style="text-align: right; width: 77.2727px;" data-sheets-value='{"1":3,"3":0.7823707069161424}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[0]C[7]">0.78</td>
</tr>
<tr>
<td style="width: 230px;" data-sheets-value='{"1":2,"2":"Korea Shipbuilding &amp;Offshore Engn Co Ltd"}' data-sheets-formula="=Data!R[-5]C[9]"><strong>Korea Shipbuilding &amp;Offshore Engn Co Ltd</strong></td>
<td style="width: 194.545px;" data-sheets-value='{"1":2,"2":"Hyundai Mipo Dockyard Co., Ltd."}' data-sheets-formula="=Data!R[-5]C[9]"><strong>Hyundai Mipo Dockyard Co., Ltd.</strong></td>
<td style="text-align: right; width: 80px;" data-sheets-value='{"1":3,"3":-1.0295619383635715}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]"><strong>-1.03</strong></td>
<td style="text-align: right; width: 58.1818px;" data-sheets-value='{"1":3,"3":0.9657685058341984}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Sort!R[3]C[3]"><strong>0.97</strong></td>
<td style="text-align: right; width: 77.2727px;" data-sheets-value='{"1":3,"3":0.6811111503062213}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[-2]C[11]"><strong>0.68</strong></td>
</tr>
<tr><td colspan="5" style="width: 723.636px;" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table>

I track a total of 141 sector pairs in Korea. Starting this insight, I grouped them into three groups: 1. Financials, 2. Retail, and 3. Industrials.Financials4 pairs from the Financials group have a 180-day correlation coefficient above 0.75 (or close) and a 180-day daily price return beta above 0.70 while sitting below -1.0σ on a 20-day moving average. Of these, Hana Financial/Shinhan Financial and Hyundai Marine/DB Insurance warrant attention.









Comp 1 (Long)
Comp 2 (Short)
Sigma on a 20-day moving average
180-day correlation coefficient
Cointegration coefficient (180-day price Δ beta)


Mirae Asset Securities Co Ltd
Samsung Securities Co Ltd
-1.22
0.75
0.70


Mirae Asset Securities Co Ltd
NH Investment &amp; Securities Co Ltd
-1.15
0.74
0.70


Hana Financial Group Inc
Shinhan Financial Group Co., Ltd.
-1.08
0.94
1.02


Hyundai Marine &amp; Fire Insurance Co., Ltd
DB Insurance Co Ltd
-1.07
0.92
0.76

Source: KRX

Retail2 pairs from the Retail group have a 180-day correlation coefficient above 0.75 and a 180-day daily price return beta above 0.70 while sitting below -1.0σ on a 20-day moving average: Lotte Shopping/GS Retail and Lotte Shopping/Hyundai Department.









Comp 1 (Long)
Comp 2 (Short)
Sigma on a 20-day moving average
180-day correlation coefficient
Cointegration coefficient (180-day price Δ beta)


Lotte Shopping Co Ltd
GS Retail Co Ltd
-1.53
0.87
0.88


Lotte Shopping Co Ltd
Hyundai Department Store Co Ltd
-1.13
0.92
0.87

Source: KRX

Industrials4 pairs from the Retail group have a 180-day correlation coefficient above 0.75 and a 180-day daily price return beta above 0.70 (or close) while sitting below -1.0σ on a 20-day moving average.Of these, the Celltrion siblings are the most correlated/cointegrated ones with a substantial diversion. I mentioned their trading dynamics in the previous insight. I will cover KAI/Hanwha Aerospace and Korea Shipbuilding/Hyundai Mipo in this post.









Comp 1 (Long)
Comp 2 (Short)
Sigma on a 20-day moving average
180-day correlation coefficient
Cointegration coefficient (180-day price Δ beta)


Korea Aerospace Industries Ltd
Hanwha Aerospace Co Ltd
-1.26
0.92
0.62


Celltrion, Inc.
Celltrion HealthCare Co Ltd
-1.18
0.84
0.76


Celltrion Pharm Inc
Celltrion HealthCare Co Ltd
-1.07
0.88
0.78


Korea Shipbuilding &amp;Offshore Engn Co Ltd
Hyundai Mipo Dockyard Co., Ltd.
-1.03
0.97
0.68

Source: KRX



Korea Sector Pair Trade: Financials, Retail, & Industrials

Strong earnings stocks can protect investors against inflation risk.So, I examined the first-quarter operating profits of all the KOSPI stocks and by how far they beat the local street consensus (compiled by FnGuide).For this, I used the following filters:
1Q21 operating profit beat the local market consensus by 10% or higher.
1Q21 operating profit exceeded ₩50B.
1Q21 operating profit increased year over year.
A total of 59 KOSPI stocks satisfy these filters, as shown below, which potentially serve as an inflation-hedged stock for a basket trade.









Name
Ticker
1Q21 operating profit
1Q21 consensus operating profit
Earnings surprise as a % of consensus


KB Financial Group Inc
105560
₩1,747.2B
₩1,450.5B
20.46%


Shinhan Financial Group Co., Ltd.
055550
₩1,682.4B
₩1,477.7B
13.85%


POSCO
005490
₩1,552.4B
₩1,286.8B
20.64%


LG Electronics Inc.
066570
₩1,516.6B
₩1,202.6B
26.11%


LG Chem Ltd
051910
₩1,408.1B
₩1,009.6B
39.47%


Samsung Life Insurance co., Ltd
032830
₩1,334.4B
₩858.8B
55.38%


Hana Financial Group Inc
086790
₩1,075.8B
₩967.3B
11.21%


LG Corp
003550
₩1,000.9B
₩704.0B
42.17%


Woori Financial Group Inc
316140
₩933.3B
₩803.4B
16.17%


Hanwha Corp
000880
₩848.5B
₩365.7B
131.99%


Industrial Bank of Korea
024110
₩780.3B
₩636.7B
22.56%


GS Holdings Corp
078930
₩706.4B
₩483.3B
46.17%


S Oil Corp
010950
₩629.2B
₩340.9B
84.60%


Lotte Chemical Corp
011170
₩623.8B
₩494.7B
26.10%


Kumho Petro Chemical Co Ltd
011780
₩612.5B
₩445.3B
37.56%


Hyundai Heavy Industries Holdings Co Ltd
267250
₩534.3B
₩300.7B
77.69%


SK Innovation Co Ltd
096770
₩502.5B
₩378.2B
32.87%


KT Corp
030200
₩444.2B
₩386.9B
14.80%


Samsung Securities Co Ltd
016360
₩399.4B
₩338.8B
17.87%


SK Telecom Co Ltd
017670
₩388.8B
₩345.8B
12.44%


CJ Cheiljedang Corp
097950
₩385.1B
₩327.5B
17.58%


NH Investment &amp; Securities Co Ltd
005940
₩374.4B
₩300.0B
24.82%


Hyundai Steel Co
004020
₩303.9B
₩174.8B
73.88%


Samsung C&amp;T Corp
028260
₩302.6B
₩231.6B
30.67%


Doosan Infracore Co Ltd
042670
₩295.4B
₩242.0B
22.04%


MERITZ SECURITIES CO LTD
008560
₩284.7B
₩221.0B
28.80%


LG Uplus Corp
032640
₩275.6B
₩237.5B
16.05%


Hanwha Solutions Corp
009830
₩254.6B
₩229.1B
11.12%


Hyosung TNC Corp
298020
₩246.8B
₩185.5B
33.05%


Daewoo Engineering &amp; Construction Co Ltd
047040
₩229.4B
₩150.1B
52.79%


Samsung SDS Co Ltd
018260
₩217.1B
₩194.7B
11.50%


Hyundai Engineering &amp; Construction Co.
000720
₩200.9B
₩171.3B
17.30%


DL E&amp;C Ord Shs
375500
₩199.8B
₩154.5B
29.29%


Amorepacific Group
002790
₩197.7B
₩146.3B
35.10%


Hankook Tire &amp; Technology Co Ltd
161390
₩186.0B
₩166.4B
11.82%


Samsung Card Co Ltd
029780
₩185.3B
₩149.9B
23.60%


DGB Financial Group Co Ltd
139130
₩178.4B
₩159.9B
11.58%


JB Financial Group Co Ltd
175330
₩177.8B
₩151.6B
17.35%


Amorepacific Corp
090430
₩176.2B
₩146.6B
20.20%


Doosan Bobcat Inc
241560
₩171.3B
₩120.3B
42.42%


Coway Co Ltd
021240
₩170.7B
₩150.0B
13.82%


Shinsegae Inc
004170
₩123.6B
₩89.2B
38.53%


E-Mart Inc
139480
₩123.2B
₩111.7B
10.30%


LG International Corp
001120
₩113.3B
₩69.7B
62.60%


Samsung Engineering Co Ltd
028050
₩107.3B
₩92.8B
15.62%


Hyosung Corp
004800
₩100.6B
₩70.0B
43.76%


Hyosung Advanced Materials Corp
298050
₩83.4B
₩62.6B
33.17%


Hyundai Construction Equipment Co Ltd
267270
₩79.7B
₩52.2B
52.83%


KCC Corp
002380
₩77.4B
₩53.0B
45.94%


SK Biopharmaceuticals Co Ltd
326030
₩75.9B
₩56.3B
34.87%


Mando Corp
204320
₩71.9B
₩63.4B
13.29%


F&amp;F Co., Ltd
007700
₩70.0B
₩33.7B
107.99%


Kolon Industries Inc
120110
₩69.1B
₩55.1B
25.40%


Dongwon Industries Co., Ltd
006040
₩68.1B
₩59.3B
14.91%


Hanwha Aerospace Co Ltd
012450
₩65.9B
₩34.4B
91.40%


Hyundai Department Store Co Ltd
069960
₩65.0B
₩57.9B
12.28%


POONGSAN CORPORATION
103140
₩62.4B
₩44.8B
39.35%


Hyosung Chemical Corp
298000
₩61.1B
₩48.9B
24.92%


KEPCO Plant Service &amp; Engineering Co Ltd
051600
₩56.4B
₩30.2B
86.94%

Source: KRX &amp; FnGuide



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South Korea

Lotte Shopping Co., Ltd. operates department stores and discount stores in South Korea.  The Company retails clothing, household goods, foods and other items through several branches.  Lotte Shopping also operates supermarkets and movie theaters.

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Lotte Shopping Co (023530 KS)

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