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>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



Earnings Surprise KOSPI Stocks Potentially Hedging Against Inflation for Basket & Pairs Trades

Korea/Asia, Events, IPOs, Douglas Research Advisory

Douglas Kim

Korea/Asia, Events, IPOs

<div class="content-highlight"><p>In this insight, we highlight the closing of the pricing gaps between the major Korean holdcos and opcos, especially with the upcoming end of the ban on the short selling of shares in Korea in May. In this insight, we highlight 26 pair trades that involve Korean holdcos and opcos.</p></div><p>Just a reminder that the ban on short selling of KOSPI 200 and KOSDAQ 150 included stocks will remain in place until May 2nd and investors will be able to resume their shorting on these 350 stocks starting on May 3rd. Once this ban on short selling of stocks ends, there could be more investors that may look to add to the holdco shares and short the opco shares (especially among the pairs where the holdco shares have underperformed the opco shares in the past year). However, some of these moves have already started to take place in the past few weeks, which could reduce the overall profits on the pair trades. </p><p>Traditionally, the pair trades involving Korean holdcos and opcos have been a trading strategy that many hedge funds have employed for many years. However, the ban on short selling of stocks in Korea have prevented many of these funds from employing these pair trade strategies. Once the ban ends, you will likely see these funds becoming more active in these trading methods once again. </p><div class="content-highlight">
<p>In the section below, we separated the pair trades into two different sections. The first table involves major opcos outperforming holdcos (regular and quasi) in the past year. Of the 26 pair trade strategies mentioned in this insight, this section I involves 19 of them (73% of total). The section II (which involve holdcos outperforming the opcos in the past year) involves 7 pair trade strategies (27% of total).</p>
<p>In other words, the opcos in general have vastly outperformed the holdcos in the past year, especially due to the ban on the short selling of stocks in Korea. Because the opcos tend to be the bigger company than the holdcos in terms of market cap, many fund managers (both active and passive) have tended to just invest in the opcos and pay less attention to the holdcos.  </p>
</div><p>Also, we described the holdcos as either regular or quasi. SK Telecom and Samsung C&amp;T would be examples of "quasi" holdcos whereas Orion Holdings and Hanjin Kal would be examples of regular holdcos. </p>

In this insight, we highlight the closing of the pricing gaps between the major Korean holdcos and opcos, especially with the upcoming end of the ban on the short selling of shares in Korea in May. In this insight, we highlight 26 pair trades that involve Korean holdcos and opcos.Just a reminder that the ban on short selling of KOSPI 200 and KOSDAQ 150 included stocks will remain in place until May 2nd and investors will be able to resume their shorting on these 350 stocks starting on May 3rd. Once this ban on short selling of stocks ends, there could be more investors that may look to add to the holdco shares and short the opco shares (especially among the pairs where the holdco shares have underperformed the opco shares in the past year). However, some of these moves have already started to take place in the past few weeks, which could reduce the overall profits on the pair trades. Traditionally, the pair trades involving Korean holdcos and opcos have been a trading strategy that many hedge funds have employed for many years. However, the ban on short selling of stocks in Korea have prevented many of these funds from employing these pair trade strategies. Once the ban ends, you will likely see these funds becoming more active in these trading methods once again. 
In the section below, we separated the pair trades into two different sections. The first table involves major opcos outperforming holdcos (regular and quasi) in the past year. Of the 26 pair trade strategies mentioned in this insight, this section I involves 19 of them (73% of total). The section II (which involve holdcos outperforming the opcos in the past year) involves 7 pair trade strategies (27% of total).
In other words, the opcos in general have vastly outperformed the holdcos in the past year, especially due to the ban on the short selling of stocks in Korea. Because the opcos tend to be the bigger company than the holdcos in terms of market cap, many fund managers (both active and passive) have tended to just invest in the opcos and pay less attention to the holdcos.  
Also, we described the holdcos as either regular or quasi. SK Telecom and Samsung C&amp;T would be examples of "quasi" holdcos whereas Orion Holdings and Hanjin Kal would be examples of regular holdcos. 

Closing the Gap: Korean Holdcos Vs. Opcos

<p>It was announced after the market close on 22 April that <a href="https://www.yna.co.kr/view/AKR20210422140100030?input=1195m" target="_blank" rel="noopener">Lotte Property &amp; Development</a> ("Lotte P&amp;D" or "Lotte Moolsan") will acquire 25% stakes of Lotte Shopping and Hotel Lotte's stakes in Lotte World Tower and Lotte World Mall for 1.4 trillion won ($1.3 billion) (including land and buildings). After this transaction, Lotte P&amp;D will have 100% stakes in Lotte World Tower and Lotte World Mall. </p><p>Prior to this transaction, Lotte World Tower and Lotte World Mall were owned by Lotte P&amp;D (75%), Lotte Shopping (15%), and Hotel Lotte (10%), respectively. Lotte P&amp;D will pay 831.2 billion won to Lotte Shopping and 555.4 billion won to Hotel Lotte according to the share ratios. The acquisition date is set for 7 May. The transaction amount was determined by calculating the average appraisal values by three companies. </p>

It was announced after the market close on 22 April that Lotte Property &amp; Development ("Lotte P&amp;D" or "Lotte Moolsan") will acquire 25% stakes of Lotte Shopping and Hotel Lotte's stakes in Lotte World Tower and Lotte World Mall for 1.4 trillion won ($1.3 billion) (including land and buildings). After this transaction, Lotte P&amp;D will have 100% stakes in Lotte World Tower and Lotte World Mall. Prior to this transaction, Lotte World Tower and Lotte World Mall were owned by Lotte P&amp;D (75%), Lotte Shopping (15%), and Hotel Lotte (10%), respectively. Lotte P&amp;D will pay 831.2 billion won to Lotte Shopping and 555.4 billion won to Hotel Lotte according to the share ratios. The acquisition date is set for 7 May. The transaction amount was determined by calculating the average appraisal values by three companies. 

Lotte P&D to Buy 25% Stakes in Lotte World Tower & Lotte World Mall for 1.4 Trillion Won - Why?

Equity Bottom-Up

<div class="content-highlight"><p>On 8 April, <a href="https://www.smartkarma.com/entities/e-mart-inc" target="_blank" data-smartkarma-citation="entities:6993:1" rel="noopener">E Mart Inc (139480 KS)</a> double downed on the pricing war against major competitors such as <a href="https://www.smartkarma.com/entities/coupang" target="_blank" data-smartkarma-citation="entities:28425:1" rel="noopener">Coupang (CPNG US)</a>. Emart started a discount strategy of providing the lowest prices versus the competitors. If the consumers find products sold at other competing e-commerce websites such as Coupang or Lotte Mall that are priced lower than the ones sold at Emart, then the difference in the amount can be accumulated as "e-money" by the Emart customers. </p></div><p>On 9 April, Kurly also announced that it will launch a new customer acquisition strategy with interesting marketing and discount promotions lasting until the end of next month. This extreme price competition by Emart, Kurly, and others will likely drive the overall industry margins even further in the next several months.</p><div class="content-highlight"><p>Overall, we maintain our long-term (one year) positive view on Emart. We think that the company has been making some excellent reorganization moves and SSG.com IPO in the next one to two years is likely to surprise many investors (positively). Nonetheless, it is becoming more apparent that the price competition in the e-commerce/retail industry in Korea by major players such as Emart, Coupang, and Kurly will be getting worse in the next several months and this could negatively impact these players as well others such as <a href="https://www.smartkarma.com/entities/lotte-shopping-co" target="_blank" data-smartkarma-citation="entities:2338:1" rel="noopener">Lotte Shopping Co (023530 KS), and</a> other home shopping companies in the near term. </p></div>

On 8 April, E Mart Inc (139480 KS) double downed on the pricing war against major competitors such as Coupang (CPNG US). Emart started a discount strategy of providing the lowest prices versus the competitors. If the consumers find products sold at other competing e-commerce websites such as Coupang or Lotte Mall that are priced lower than the ones sold at Emart, then the difference in the amount can be accumulated as "e-money" by the Emart customers. On 9 April, Kurly also announced that it will launch a new customer acquisition strategy with interesting marketing and discount promotions lasting until the end of next month. This extreme price competition by Emart, Kurly, and others will likely drive the overall industry margins even further in the next several months.Overall, we maintain our long-term (one year) positive view on Emart. We think that the company has been making some excellent reorganization moves and SSG.com IPO in the next one to two years is likely to surprise many investors (positively). Nonetheless, it is becoming more apparent that the price competition in the e-commerce/retail industry in Korea by major players such as Emart, Coupang, and Kurly will be getting worse in the next several months and this could negatively impact these players as well others such as Lotte Shopping Co (023530 KS), and other home shopping companies in the near term. 

Emart & Kurly - Double Down On the Pricing War Against Coupang

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)

Korea Sector Pair Trade: Financials, Retail, & Industrials

This post examines Korean sector pairs' 20-day sigma and potential entry points, as they are grouped into Financials, Retail, and Industrials.

Sanghyun Park

Earnings Surprise KOSPI Stocks Potentially Hedging Against Inflation for Basket & Pairs Trades

This post finds the first-quarter earnings surprise stocks in KOSPI that potentially hedge against inflation for basket and pairs trades.

Sanghyun Park

Closing the Gap: Korean Holdcos Vs. Opcos

In this insight, we highlight the closing of the pricing gaps between the major Korean holdcos and opcos, especially with the upcoming end of the...

Douglas Kim

Lotte P&D to Buy 25% Stakes in Lotte World Tower & Lotte World Mall for 1.4 Trillion Won - Why?

Lotte Property & Development will acquire 25% stakes of Lotte Shopping and Hotel Lotte's stakes in Lotte World Tower and Lotte World Mall for 1.4...

Douglas Kim

Emart & Kurly - Double Down On the Pricing War Against Coupang

On 8 April, E Mart Inc double downed on the pricing war against major competitors such as Coupang (CPNG US). Emart started a discount strategy of...

Douglas Kim

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