Korea/Asia, Events, IPOs, Douglas Research Advisory

Douglas Kim

Korea/Asia, Events, IPOs

Quantitative Analysis

<ul>
<li>In this insight, we discuss 19 value oriented stocks in KOSPI200 that have been big outperformers YTD but whose share prices have risen less than 100% from 3/20/2020 to 1/25/2022.</li>
<li>Among the 19 stocks in this list, 32% are financials (6 companies) and 16% are F&amp;B (3 companies).</li>
<li>These 19 value-oriented stocks could continue to outperform the market in the next few weeks due to rising inflation, geopolitical uncertainties, and uncertainties regarding how the COVID-19 narrative will end.</li>
</ul>


In this insight, we discuss 19 value oriented stocks in KOSPI200 that have been big outperformers YTD but whose share prices have risen less than 100% from 3/20/2020 to 1/25/2022.
Among the 19 stocks in this list, 32% are financials (6 companies) and 16% are F&amp;B (3 companies).
These 19 value-oriented stocks could continue to outperform the market in the next few weeks due to rising inflation, geopolitical uncertainties, and uncertainties regarding how the COVID-19 narrative will end.


Outperforming Value Stocks With Contrarian Qualities in KOSPI200

Korea / Special Situations, Clepsydra Capital

Sanghyun Park

Korea / Special Situations

Korea / Special Sits, Pairs, Index Rebalancing

Clepsydra Capital

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

Event-Driven

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.<hr><h2>Financials</h2>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, Hana Financial/Shinhan Financial and Hyundai Marine/DB Insurance warrant attention.<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]">Hana Financial Group Inc</td>
<td style="width: 190px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}' data-sheets-formula="=Data!R[-3]C[5]">Shinhan Financial Group Co., Ltd.</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]">-1.08</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]">0.94</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]">1.02</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]">Hyundai Marine &amp; Fire Insurance Co., Ltd</td>
<td style="width: 190px;" data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}' data-sheets-formula="=Data!R[-4]C[7]">DB Insurance Co Ltd</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]">-1.07</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]">0.92</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]">0.76</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>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: Lotte Shopping/GS Retail and Lotte Shopping/Hyundai Department.<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]">Lotte Shopping Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"GS Retail Co Ltd"}' data-sheets-formula="=Data!R[-1]C[1]">GS Retail Co Ltd</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]">-1.53</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]">0.87</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]">0.88</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Lotte Shopping Co Ltd"}' data-sheets-formula="=Data!R[-3]C[5]">Lotte Shopping Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Hyundai Department Store Co Ltd"}' data-sheets-formula="=Data!R[-3]C[5]">Hyundai Department Store Co Ltd</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]">-1.13</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]">0.92</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]">0.87</td>
</tr>
<tr><td colspan="5" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table><h2>Industrials</h2>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.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.<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]">Korea Aerospace Industries Ltd</td>
<td style="width: 194.545px;" data-sheets-value='{"1":2,"2":"Hanwha Aerospace Co Ltd"}' data-sheets-formula="=Data!R[-1]C[1]">Hanwha Aerospace Co Ltd</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]">-1.26</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]">0.92</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]">0.62</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]">Korea Shipbuilding &amp;Offshore Engn Co Ltd</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]">Hyundai Mipo Dockyard Co., Ltd.</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]">-1.03</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]">0.97</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]">0.68</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

Hyundai Marine & Fire Insurance Co., Ltd / Event-Driven

I monitor a total of 31 Korean financial company pairs for pair trading. All of those included as Compe 2 are eligible for short-selling as they are part of KOSPI 200 or KOSDAQ 150. Of these, 8 pairs have a +0.80 180-day correlation coefficient and are currently at -1.0σ on a 20-day moving average.<table class="table table-striped">
<colgroup>
<col width="100">
<col width="100">
<col width="100">
<col width="100">
</colgroup>
<tbody>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Comp 1"}'>Comp 1 (long)</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"Comp 2"}'>Comp 2 (short)</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":2,"2":"180-day correlation coefficient"}'>180-day correlation coefficient</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":2,"2":"Sigma on a 20-day moving average"}'>Sigma on a 20-day moving average</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Hyundai Marine &amp; Fire Insurance Co., Ltd"}'>Hyundai Marine &amp; Fire Insurance Co., Ltd</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}'>DB Insurance Co Ltd</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.9235179818343655}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.92352</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.776088884171848}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.77609</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Hyundai Marine &amp; Fire Insurance Co., Ltd"}'>Hyundai Marine &amp; Fire Insurance Co., Ltd</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"Samsung Fire &amp; Marine Insurance Co Ltd"}'>Samsung Fire &amp; Marine Insurance Co Ltd</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.8196008707394729}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.81960</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.4840607038916225}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.48406</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}'>Mirae Asset Securities Co Ltd</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"Samsung Securities Co Ltd"}'>Samsung Securities Co Ltd</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.7990679072379809}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.79907</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.3751173809487096}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.37512</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"Hana Financial Group Inc"}'>Hana Financial Group Inc</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.9374222791236334}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.93742</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.3726808804439994}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.37268</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"MERITZ SECURITIES CO LTD"}'>MERITZ SECURITIES CO LTD</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"Korea Investment Holdings Co., Ltd."}'>Korea Investment Holdings Co., Ltd.</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.812832800812022}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.81283</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.3098829415216042}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.30988</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Samsung Fire &amp; Marine Insurance Co Ltd"}'>Samsung Fire &amp; Marine Insurance Co Ltd</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}'>DB Insurance Co Ltd</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.8981233728989753}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.89812</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.2715664922292673}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.27157</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}'>Mirae Asset Securities Co Ltd</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"NH Investment &amp; Securities Co Ltd"}'>NH Investment &amp; Securities Co Ltd</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.7965427656434082}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.79654</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.1683633479718487}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.16836</td>
</tr>
<tr>
<td style="width: 248.182px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="width: 226.364px;" data-sheets-value='{"1":2,"2":"BNK Financial Group Inc"}'>BNK Financial Group Inc</td>
<td style="text-align: right; width: 87.2727px;" data-sheets-value='{"1":3,"3":0.9620185257943962}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>0.96202</td>
<td style="text-align: right; width: 99.0909px;" data-sheets-value='{"1":3,"3":-1.0500518144354605}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}'>-1.05005</td>
</tr>
<tr><td colspan="4" style="width: 248.182px;">Source: KRX 
</td></tr>
</tbody>
</table>They are all sufficiently liquid as their 30-day average daily trade value is mostly above ₩10B.<table class="table table-striped">
<colgroup>
<col width="100">
<col width="100">
<col width="100">
<col width="100">
</colgroup>
<tbody>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Comp 1 (long)"}'>Comp 1 (long)</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":2,"2":"30-day average daily trade value"}'>30-day average daily trade value</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"Comp 2 (short)"}'>Comp 2 (short)</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":2,"2":"30-day average daily trade value"}'>30-day average daily trade value</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Hyundai Marine &amp; Fire Insurance Co., Ltd"}'>Hyundai Marine &amp; Fire Insurance Co., Ltd</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":14.46202689473684}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩14.5B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}'>DB Insurance Co Ltd</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":9.622840144736841}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩9.6B</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Hyundai Marine &amp; Fire Insurance Co., Ltd"}'>Hyundai Marine &amp; Fire Insurance Co., Ltd</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":14.46202689473684}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩14.5B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"Samsung Fire &amp; Marine Insurance Co Ltd"}'>Samsung Fire &amp; Marine Insurance Co Ltd</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":26.038764}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩26.0B</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}'>Mirae Asset Securities Co Ltd</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":25.03321042105263}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩25.0B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"Samsung Securities Co Ltd"}'>Samsung Securities Co Ltd</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":26.259585302631578}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩26.3B</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":69.40432211842105}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩69.4B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"Hana Financial Group Inc"}'>Hana Financial Group Inc</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":55.43731025263158}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩55.4B</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"MERITZ SECURITIES CO LTD"}'>MERITZ SECURITIES CO LTD</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":9.366756418421053}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩9.4B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"Korea Investment Holdings Co., Ltd."}'>Korea Investment Holdings Co., Ltd.</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":59.58392347368421}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩59.6B</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Samsung Fire &amp; Marine Insurance Co Ltd"}'>Samsung Fire &amp; Marine Insurance Co Ltd</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":26.038764}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩26.0B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}'>DB Insurance Co Ltd</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":9.622840144736841}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩9.6B</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}'>Mirae Asset Securities Co Ltd</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":25.03321042105263}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩25.0B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"NH Investment &amp; Securities Co Ltd"}'>NH Investment &amp; Securities Co Ltd</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":17.386767}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩17.4B</td>
</tr>
<tr>
<td style="width: 240.909px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="text-align: right; width: 93.6364px;" data-sheets-value='{"1":3,"3":69.40432211842105}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[0], "volumeavg")*average(index(googlefinance(R[-20]C[0], "price", today()-30, today()), 0, 2))/1000000000'>₩69.4B</td>
<td style="text-align: left; width: 228.182px;" data-sheets-value='{"1":2,"2":"BNK Financial Group Inc"}'>BNK Financial Group Inc</td>
<td style="text-align: right; width: 98.1818px;" data-sheets-value='{"1":3,"3":15.425117574736841}' data-sheets-numberformat='{"1":2,"2":"₩#,##0.0B"}' data-sheets-formula='=googlefinance(R[-20]C[3], "volumeavg")*average(index(googlefinance(R[-20]C[3], "price", today()-30, today()), 0, 2))/1000000000'>₩15.4B</td>
</tr>
<tr><td colspan="4" rowspan="1" style="width: 723.636px;" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table>These are 180-day beta values with Comp 1 (long) as a numerator and Comp 2 (short) as a denominator in the following beta calculation. Those closer to a 180-day beta value of 1 should have a higher degree of cointegration. Also, I can use this beta value as a hedge ratio for a short position.<div class="content-highlight">Covariance (Comp 1 180-day daily price Δ:Comp 2 180-day daily price Δ) / Variance (Comp 2 180-day daily price Δ)</div>Hyundai Marine &amp; Fire and DB Insurance have the highest cointegration coefficient at 0.79, followed by Mirae Asset and NH Investment at 0.71.<table class="table table-striped">
<colgroup>
<col width="100">
<col width="100">
<col width="100">
</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":"180-day beta (cointegration coefficient)"}'>180-day beta (cointegration coefficient)</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hyundai Marine &amp; Fire Insurance Co., Ltd"}'>Hyundai Marine &amp; Fire Insurance Co., Ltd</td>
<td data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}'>DB Insurance Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.7882489725798763}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-8]C[-1]">0.78825</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hyundai Marine &amp; Fire Insurance Co., Ltd"}'>Hyundai Marine &amp; Fire Insurance Co., Ltd</td>
<td data-sheets-value='{"1":2,"2":"Samsung Fire &amp; Marine Insurance Co Ltd"}'>Samsung Fire &amp; Marine Insurance Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.691545689879791}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-9]C[1]">0.69155</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}'>Mirae Asset Securities Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"Samsung Securities Co Ltd"}'>Samsung Securities Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.6962076152600883}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-10]C[3]">0.69621</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":"Hana Financial Group Inc"}'>Hana Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.5914597455570075}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-11]C[5]">0.59146</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"MERITZ SECURITIES CO LTD"}'>MERITZ SECURITIES CO LTD</td>
<td data-sheets-value='{"1":2,"2":"Korea Investment Holdings Co., Ltd."}'>Korea Investment Holdings Co., Ltd.</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.36087184468000433}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-12]C[7]">0.36087</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Samsung Fire &amp; Marine Insurance Co Ltd"}'>Samsung Fire &amp; Marine Insurance Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"DB Insurance Co Ltd"}'>DB Insurance Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.5457409326679108}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-13]C[9]">0.54574</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Mirae Asset Securities Co Ltd"}'>Mirae Asset Securities Co Ltd</td>
<td data-sheets-value='{"1":2,"2":"NH Investment &amp; Securities Co Ltd"}'>NH Investment &amp; Securities Co Ltd</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.7113678899298631}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-14]C[11]">0.71137</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":"BNK Financial Group Inc"}'>BNK Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.6430653047679042}' data-sheets-numberformat='{"1":2,"2":"#,##0.00000","3":1}' data-sheets-formula="=Beta!R[-15]C[13]">0.64307</td>
</tr>
<tr><td colspan="3">Source: KRX</td></tr>
</tbody>
</table>

I monitor a total of 31 Korean financial company pairs for pair trading. All of those included as Compe 2 are eligible for short-selling as they are part of KOSPI 200 or KOSDAQ 150. Of these, 8 pairs have a +0.80 180-day correlation coefficient and are currently at -1.0σ on a 20-day moving average.








Comp 1 (long)
Comp 2 (short)
180-day correlation coefficient
Sigma on a 20-day moving average


Hyundai Marine &amp; Fire Insurance Co., Ltd
DB Insurance Co Ltd
0.92352
-1.77609


Hyundai Marine &amp; Fire Insurance Co., Ltd
Samsung Fire &amp; Marine Insurance Co Ltd
0.81960
-1.48406


Mirae Asset Securities Co Ltd
Samsung Securities Co Ltd
0.79907
-1.37512


Shinhan Financial Group Co., Ltd.
Hana Financial Group Inc
0.93742
-1.37268


MERITZ SECURITIES CO LTD
Korea Investment Holdings Co., Ltd.
0.81283
-1.30988


Samsung Fire &amp; Marine Insurance Co Ltd
DB Insurance Co Ltd
0.89812
-1.27157


Mirae Asset Securities Co Ltd
NH Investment &amp; Securities Co Ltd
0.79654
-1.16836


Shinhan Financial Group Co., Ltd.
BNK Financial Group Inc
0.96202
-1.05005

Source: KRX 


They are all sufficiently liquid as their 30-day average daily trade value is mostly above ₩10B.








Comp 1 (long)
30-day average daily trade value
Comp 2 (short)
30-day average daily trade value


Hyundai Marine &amp; Fire Insurance Co., Ltd
₩14.5B
DB Insurance Co Ltd
₩9.6B


Hyundai Marine &amp; Fire Insurance Co., Ltd
₩14.5B
Samsung Fire &amp; Marine Insurance Co Ltd
₩26.0B


Mirae Asset Securities Co Ltd
₩25.0B
Samsung Securities Co Ltd
₩26.3B


Shinhan Financial Group Co., Ltd.
₩69.4B
Hana Financial Group Inc
₩55.4B


MERITZ SECURITIES CO LTD
₩9.4B
Korea Investment Holdings Co., Ltd.
₩59.6B


Samsung Fire &amp; Marine Insurance Co Ltd
₩26.0B
DB Insurance Co Ltd
₩9.6B


Mirae Asset Securities Co Ltd
₩25.0B
NH Investment &amp; Securities Co Ltd
₩17.4B


Shinhan Financial Group Co., Ltd.
₩69.4B
BNK Financial Group Inc
₩15.4B

Source: KRX

These are 180-day beta values with Comp 1 (long) as a numerator and Comp 2 (short) as a denominator in the following beta calculation. Those closer to a 180-day beta value of 1 should have a higher degree of cointegration. Also, I can use this beta value as a hedge ratio for a short position.Covariance (Comp 1 180-day daily price Δ:Comp 2 180-day daily price Δ) / Variance (Comp 2 180-day daily price Δ)Hyundai Marine &amp; Fire and DB Insurance have the highest cointegration coefficient at 0.79, followed by Mirae Asset and NH Investment at 0.71.







Comp 1 (long)
Comp 2 (short)
180-day beta (cointegration coefficient)


Hyundai Marine &amp; Fire Insurance Co., Ltd
DB Insurance Co Ltd
0.78825


Hyundai Marine &amp; Fire Insurance Co., Ltd
Samsung Fire &amp; Marine Insurance Co Ltd
0.69155


Mirae Asset Securities Co Ltd
Samsung Securities Co Ltd
0.69621


Shinhan Financial Group Co., Ltd.
Hana Financial Group Inc
0.59146


MERITZ SECURITIES CO LTD
Korea Investment Holdings Co., Ltd.
0.36087


Samsung Fire &amp; Marine Insurance Co Ltd
DB Insurance Co Ltd
0.54574


Mirae Asset Securities Co Ltd
NH Investment &amp; Securities Co Ltd
0.71137


Shinhan Financial Group Co., Ltd.
BNK Financial Group Inc
0.64307

Source: KRX



Korean Financials Pair Trading: 4 Pairs Deserve Attention at This Point

Silver Point

Thomas J. Monaco

Silver Point is a boutique research firm that focuses on the broader financial services sector, with a particular focus on troubled institutions globally. 

Asia Ex Japan

Thematic (Sector/Industry)

*Reversing Course Again: On April 3, 2020, the European Insurance and Occupational Pensions Authority (EIOPA) - the European Union’s financial regulatory body which replaced the Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS) – in the context of the corona virus, released a statement urging insurers to “temporarily suspend all discretionary dividend distributions and share buybacks aimed at remunerating shareholders” in order to preserve capital. In an odd reversal of course, EIOPA has updated its commentary surrounding European insurance capital management; and  *Asia-Pacific Insurers Still At Risk: Asia-Pacific insurance counterparts previously acted in capital solidarity with their Western peer during COVID-19. Many Asia-Pacific insurers purchased USD-denominated assets in their desperate search for yield in this low/negative rate environment where the impact of credit default and ratings agency downgrades on their investment portfolios is likely to continue to be outsized and challenge capital their ratios. We are hopeful that the Asia-Pacific insurers - particularly in Japan, Australia, Taiwan, and South Korea – don’t behave as desperately as their European counterparts have.

*Reversing Course Again: On April 3, 2020, the European Insurance and Occupational Pensions Authority (EIOPA) - the European Union’s financial regulatory body which replaced the Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS) – in the context of the corona virus, released a statement urging insurers to “temporarily suspend all discretionary dividend distributions and share buybacks aimed at remunerating shareholders” in order to preserve capital. In an odd reversal of course, EIOPA has updated its commentary surrounding European insurance capital management; and  *Asia-Pacific Insurers Still At Risk: Asia-Pacific insurance counterparts previously acted in capital solidarity with their Western peer during COVID-19. Many Asia-Pacific insurers purchased USD-denominated assets in their desperate search for yield in this low/negative rate environment where the impact of credit default and ratings agency downgrades on their investment portfolios is likely to continue to be outsized and challenge capital their ratios. We are hopeful that the Asia-Pacific insurers - particularly in Japan, Australia, Taiwan, and South Korea – don’t behave as desperately as their European counterparts have. 

Technical Analyst, Vermilion Research

Joe Jasper

Technical Analyst

Technical and Quantitative Research

Vermilion Research

Vermilion’s objective is to deliver timely, actionable, and unique technical research to professional investors through the application of our various technical indicators.

Our research strategists seek to highlight securities which we believe are at major inflection points, based on our various technical and quantitative indicators, and offer asymmetric risk/return profiles. Our unique research methodology, centered on relative strength, enables our strategists to deliver superior investment recommendations.

Our process begins by organizing the universe of actively traded stocks into discrete sectors, then into industry groups. We then apply our technical and quantitative tools to identify developing price trends. Once attractive trends are identified within a selected sector or group, we screen for individual stocks which we believe offer the best risk/reward profile. We provide a historical perspective that often reveals, through our long-term charts, how price patterns often repeat themselves over time.

Technical Analysis

The risk of a pullback remains very real, however at this point the weight of the evidence suggests a meaningful pullback is not imminent. We recap important technical levels across market as well as Actionable Themes within the Consumer Staples, Health Care, and Technology sectors.

The risk of a pullback remains very real, however at this point the weight of the evidence suggests a meaningful pullback is not imminent. We recap important technical levels across market as well as Actionable Themes within the Consumer Staples, Health Care, and Technology sectors.

South Korea

Hyundai Marine & Fire Insurance Co., Ltd. (HDMF) is a property and casualty insurer.  The Company's products include auto, fire, marine, general liability, leisure, travel, pension, and medical insurances.

Hyundai Marine & Fire Insurance Co., Ltd (001450 KS)

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Hyundai Marine & Fire Insurance Co., Ltd (001450 KS)

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