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

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

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








Comp 1
Comp 2
180-day correlation coefficient
Business type


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


Hyundai Department Store Co Ltd
Lotte Shopping Co Ltd
0.97033
Retail


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


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


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


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


Hansol Chemical Co Ltd
Posco Chemical Co Ltd
0.95374
Chemical


Hanwha Aerospace Co Ltd
Korea Aerospace Industries Ltd
0.95243
Aerospace


KG DongbuSteel Co Ltd
POSCO
0.95174
Steel


KG DongbuSteel Co Ltd
Hyundai Steel Co
0.94972
Steel

Source: KRX

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








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


E-Mart Inc
Lotte Shopping Co Ltd
0.85128
-3.27


Silicon Works Co Ltd
LG Electronics Inc.
0.82411
2.79


BGF Retail Co Ltd
E-Mart Inc
0.84733
2.58


Woori Financial Group Inc
KB Financial Group Inc
0.84517
-2.47


Pan Ocean Co Ltd
HMM Co Ltd
0.93591
-2.33


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


SK Innovation Co Ltd
Samsung SDI Co Ltd
0.94587
2.19


Hanwha Aerospace Co Ltd
Korea Aerospace Industries Ltd
0.95243
2.09


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


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

Source: KRX



Korea Sector Long/Short Pairs Trade Ideas

Financials Credit Analyst

Hank Calenti, CFA

CreditContinuum

Credit

Executive Summary: The US Treasury bond market rally gave up the ghost at the longer-end of the curve while excess cash levels compressed rates at the short-end last week. The Treasury curve bull steepened for the first time in over three weeks.Market participants re-positioned into month-end and ahead of April data releases over the next two weeks. Expectations are for strong economic data. High readings could prove bearish for Treasury prices. However, since the markets are forward looking, the question is also why are rates not already higher?As markets focused on upside inflation risks, with commodity prices continuing to surge, both Sweden’s Riksbank and the Bank of Japan highlighted soft inflation pressures in their latest policy meetings. These statements may add to uncertainty over how sustainable the current rise in US inflation may prove to be.At the same time, the run-off of the US Treasury Department’s cash balances at the Fed continues to keep pressure on repo rates. This in turn is resulting in a melt-up with two year and under area yields being squeezed tighter as investors move up the curve for a return. For more on this melt-up approach, please see: <a href="https://www.smartkarma.com/compose/insight/50971/Bank%20Credit%20Strategy:%20Melt-Up%20Before%20Meltdown,%20Where%20to%20Position.%20Read%20more:%20https:/skr.ma/W1NcQ" target="_blank" rel="noopener">Bank Credit Strategy: Melt-Up Before Meltdown, Where to Position</a>. In bank credit, with short-end interest rates compressing week-on-week and short-end credit tightening, positive total returns were easy to generate. Although credit spreads tightened more in the long- than short-end, Treasury curve steepening meant that positive total returns were not realized in longer dated notes. Most Asia-headquartered bank credits tightened by two- to three- basis points week-on-week. Tier 2 and HY Senior Unsecured notes tended to outperform on a spread basis. Most European-headquartered bank credits tightened by three- to four- basis points week-on-week. Lower risk Senior Bail-in bonds delivered higher average spread tighteningWith peak earnings season now behind us, the upcoming week will essentially complete Q1 reports for most major financial institutions. US banks signaled that they anticipate significantly higher interest rates by raising record breaking amounts in the debt capital markets following their Q1 earnings announcements. Other banks may follow. May is historically the start of a bullish period for Treasuries. However, as we start the month, the absence of evidence should not be taken as evidence of its absence. Various holidays around the world may reduce visibility over the next week. Markets in China, Japan and the UK are closed on Monday, with those in Tokyo and China shut through Wednesday. Look to the end of the week for more directionality. We explore these market themes within.Market Outlook: Already looking past upcoming spikes in inflation and job creation, the Treasury market is more constrained in terms of rates than was previously expected. This should be risk asset supportive. The concern is that of impotent monetary policy and a constrained fiscal response in a shorter than expected business cycle.

Executive Summary: The US Treasury bond market rally gave up the ghost at the longer-end of the curve while excess cash levels compressed rates at the short-end last week. The Treasury curve bull steepened for the first time in over three weeks.Market participants re-positioned into month-end and ahead of April data releases over the next two weeks. Expectations are for strong economic data. High readings could prove bearish for Treasury prices. However, since the markets are forward looking, the question is also why are rates not already higher?As markets focused on upside inflation risks, with commodity prices continuing to surge, both Sweden’s Riksbank and the Bank of Japan highlighted soft inflation pressures in their latest policy meetings. These statements may add to uncertainty over how sustainable the current rise in US inflation may prove to be.At the same time, the run-off of the US Treasury Department’s cash balances at the Fed continues to keep pressure on repo rates. This in turn is resulting in a melt-up with two year and under area yields being squeezed tighter as investors move up the curve for a return. For more on this melt-up approach, please see: Bank Credit Strategy: Melt-Up Before Meltdown, Where to Position. In bank credit, with short-end interest rates compressing week-on-week and short-end credit tightening, positive total returns were easy to generate. Although credit spreads tightened more in the long- than short-end, Treasury curve steepening meant that positive total returns were not realized in longer dated notes. Most Asia-headquartered bank credits tightened by two- to three- basis points week-on-week. Tier 2 and HY Senior Unsecured notes tended to outperform on a spread basis. Most European-headquartered bank credits tightened by three- to four- basis points week-on-week. Lower risk Senior Bail-in bonds delivered higher average spread tighteningWith peak earnings season now behind us, the upcoming week will essentially complete Q1 reports for most major financial institutions. US banks signaled that they anticipate significantly higher interest rates by raising record breaking amounts in the debt capital markets following their Q1 earnings announcements. Other banks may follow. May is historically the start of a bullish period for Treasuries. However, as we start the month, the absence of evidence should not be taken as evidence of its absence. Various holidays around the world may reduce visibility over the next week. Markets in China, Japan and the UK are closed on Monday, with those in Tokyo and China shut through Wednesday. Look to the end of the week for more directionality. We explore these market themes within.Market Outlook: Already looking past upcoming spikes in inflation and job creation, the Treasury market is more constrained in terms of rates than was previously expected. This should be risk asset supportive. The concern is that of impotent monetary policy and a constrained fiscal response in a shorter than expected business cycle.  

Bank Credit Weekly: Bull Steepening, but Is It Sustainable?

Korea has 6 closely correlated and co-integrated sector pairs. All of them have a +0.8 180-day correlation coefficient. Also, they all have a sufficiently linear 180-day price scattered chart showing the nature of their high cointegration.<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 colspan="5" rowspan="1" style="width: 723.636px;" data-sheets-value='{"1":2,"2":"Closely correlated &amp; co-integrated sector pairs in Korea"}'>Closely correlated &amp; co-integrated sector pairs in Korea</td></tr>
<tr>
<td style="width: 181.818px;" data-sheets-value='{"1":2,"2":"Name"}'>Name</td>
<td style="width: 96.3636px;" data-sheets-value='{"1":2,"2":"Ticker"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>Ticker</td>
<td style="width: 165.455px;" data-sheets-value='{"1":2,"2":"Name"}'>Name</td>
<td style="width: 60.9091px;" data-sheets-value='{"1":2,"2":"Ticker"}'>Ticker</td>
<td style="text-align: right; width: 135.455px;" data-sheets-value='{"1":2,"2":"180-day correlation"}'>180-day correlation</td>
</tr>
<tr>
<td style="width: 181.818px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}' data-sheets-formula="=Data!R[-2]C[1]">Shinhan Financial Group Co., Ltd.</td>
<td style="width: 96.3636px;" data-sheets-value='{"1":2,"2":"055550"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-1]C[0]">055550</td>
<td style="width: 165.455px;" data-sheets-value='{"1":2,"2":"KB Financial Group Inc"}' data-sheets-formula="=Data!R[-2]C[0]">KB Financial Group Inc</td>
<td style="width: 60.9091px;" data-sheets-value='{"1":2,"2":"105560"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-1]C[-1]">105560</td>
<td style="text-align: right; width: 135.455px;" data-sheets-value='{"1":3,"3":0.9610517352067552}' data-sheets-numberformat='[null,2,"#,##0.00000",1]' data-sheets-formula="=correl('1'!R870C10:R994C10, '1'!R870C11:R994C11)">0.96105</td>
</tr>
<tr>
<td style="width: 181.818px;" data-sheets-value='{"1":2,"2":"LG Uplus Corp"}' data-sheets-formula="=Data!R[-3]C[3]">LG Uplus Corp</td>
<td style="width: 96.3636px;" data-sheets-value='{"1":2,"2":"032640"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-2]C[2]">032640</td>
<td style="width: 165.455px;" data-sheets-value='{"1":2,"2":"SK Telecom Co Ltd"}' data-sheets-formula="=Data!R[-3]C[2]">SK Telecom Co Ltd</td>
<td style="width: 60.9091px;" data-sheets-value='{"1":2,"2":"017670"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-2]C[1]">017670</td>
<td style="text-align: right; width: 135.455px;" data-sheets-value='{"1":3,"3":0.8992786663054949}' data-sheets-numberformat='[null,2,"#,##0.00000",1]' data-sheets-formula="=correl('2'!R870C10:R994C10, '2'!R870C11:R994C11)">0.89928</td>
</tr>
<tr>
<td style="width: 181.818px;" data-sheets-value='{"1":2,"2":"Kia Corp"}' data-sheets-formula="=Data!R[-4]C[5]">Kia Corp</td>
<td style="width: 96.3636px;" data-sheets-value='{"1":2,"2":"000270"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-3]C[4]">000270</td>
<td style="width: 165.455px;" data-sheets-value='{"1":2,"2":"Hyundai Motor Co"}' data-sheets-formula="=Data!R[-4]C[4]">Hyundai Motor Co</td>
<td style="width: 60.9091px;" data-sheets-value='{"1":2,"2":"005380"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-3]C[3]">005380</td>
<td style="text-align: right; width: 135.455px;" data-sheets-value='{"1":3,"3":0.8668911065680154}' data-sheets-numberformat='[null,2,"#,##0.00000",1]' data-sheets-formula="=correl('3'!R870C10:R994C10, '3'!R870C11:R994C11)">0.86689</td>
</tr>
<tr>
<td style="width: 181.818px;" data-sheets-value='{"1":2,"2":"Celltrion HealthCare Co Ltd"}' data-sheets-formula="=Data!R[-5]C[7]">Celltrion HealthCare Co Ltd</td>
<td style="width: 96.3636px;" data-sheets-value='{"1":2,"2":"091990"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-4]C[6]">091990</td>
<td style="width: 165.455px;" data-sheets-value='{"1":2,"2":"Celltrion, Inc."}' data-sheets-formula="=Data!R[-5]C[6]">Celltrion, Inc.</td>
<td style="width: 60.9091px;" data-sheets-value='{"1":2,"2":"068270"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-4]C[5]">068270</td>
<td style="text-align: right; width: 135.455px;" data-sheets-value='{"1":3,"3":0.850526307159412}' data-sheets-numberformat='[null,2,"#,##0.00000",1]' data-sheets-formula="=correl('4'!R870C10:R994C10, '4'!R870C11:R994C11)">0.85053</td>
</tr>
<tr>
<td style="width: 181.818px;" data-sheets-value='{"1":2,"2":"BGF Retail Co Ltd"}' data-sheets-formula="=Data!R[-6]C[9]">BGF Retail Co Ltd</td>
<td style="width: 96.3636px;" data-sheets-value='{"1":2,"2":"282330"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-5]C[8]">282330</td>
<td style="width: 165.455px;" data-sheets-value='{"1":2,"2":"GS Retail Co Ltd"}' data-sheets-formula="=Data!R[-6]C[8]">GS Retail Co Ltd</td>
<td style="width: 60.9091px;" data-sheets-value='{"1":2,"2":"007070"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-5]C[7]">007070</td>
<td style="text-align: right; width: 135.455px;" data-sheets-value='{"1":3,"3":0.7760017852780396}' data-sheets-numberformat='[null,2,"#,##0.00000",1]' data-sheets-formula="=correl('5'!R870C10:R994C10, '5'!R870C11:R994C11)">0.77600</td>
</tr>
<tr>
<td style="width: 181.818px;" data-sheets-value='{"1":2,"2":"Hyundai Department Store Co Ltd"}' data-sheets-formula="=Data!R[-7]C[11]">Hyundai Department Store Co Ltd</td>
<td style="width: 96.3636px;" data-sheets-value='{"1":2,"2":"069960"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-6]C[10]">069960</td>
<td style="width: 165.455px;" data-sheets-value='{"1":2,"2":"Shinsegae Inc"}' data-sheets-formula="=Data!R[-7]C[10]">Shinsegae Inc</td>
<td style="width: 60.9091px;" data-sheets-value='{"1":2,"2":"004170"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]' data-sheets-formula="=Data!R[-6]C[9]">004170</td>
<td style="text-align: right; width: 135.455px;" data-sheets-value='{"1":3,"3":0.9327816777063404}' data-sheets-numberformat='[null,2,"#,##0.00000",1]' data-sheets-formula="=correl('6'!R870C10:R994C10, '6'!R870C11:R994C11)">0.93278</td>
</tr>
<tr><td colspan="5" rowspan="1" style="width: 723.636px;" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table>In the light of the short-selling resumption next week, these sector pairs will likely draw attention.Then, which pair is at an extreme sigma (±2.0σ) on a 20-day moving average? At the last close, three pairs come on the radar.<ol>
<li>Shinhan Financial and KB Financial: +2.16σ</li>
<li>Kia Corp and Hyundai Motor: -2.04σ</li>
<li>Hyundai Department Store and Shinsegae: -1.81σ</li>
</ol><table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td style="width: 320.909px;" data-sheets-value='{"1":2,"2":"Sigma (σ) on a 20-day moving average"}'>Sigma (σ) on a 20-day moving average</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":2,"2":"Today"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>Today</td>
<td style="text-align: right; width: 45.4545px;" data-sheets-value='{"1":2,"2":"T-1"}'>T-1</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":2,"2":"T-2"}'>T-2</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":2,"2":"T-3"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>T-3</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":2,"2":"T-4"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>T-4</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":2,"2":"T-5"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>T-5</td>
</tr>
<tr>
<td style="width: 320.909px;" data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd./KB Financial Group Inc"}' data-sheets-formula='=Data!R[-11]C[1]&amp;"/"&amp;Data!R[-11]C[2]'>Shinhan Financial Group Co., Ltd./KB Financial Group Inc</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":2.160993227615466}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$2")'>2.16</td>
<td style="text-align: right; width: 45.4545px;" data-sheets-value='{"1":3,"3":2.542772683735228}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$3")'>2.54</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":1.786381555409661}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$4")'>1.79</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":1.1128800501751042}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$5")'>1.11</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":0.181530165798035}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$6")'>0.18</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.22641999615163677}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$7")'>-0.23</td>
</tr>
<tr>
<td style="width: 320.909px;" data-sheets-value='{"1":2,"2":"LG Uplus Corp/SK Telecom Co Ltd"}' data-sheets-formula='=Data!R[-12]C[3]&amp;"/"&amp;Data!R[-12]C[4]'>LG Uplus Corp/SK Telecom Co Ltd</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.24660010671563912}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$2")'>-0.25</td>
<td style="text-align: right; width: 45.4545px;" data-sheets-value='{"1":3,"3":-0.10403426571856596}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$3")'>-0.10</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.6117954095017157}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$4")'>-1.61</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.6376484979254156}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$5")'>-1.64</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.4249128627679264}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$6")'>-1.42</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-2.087684413507805}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$7")'>-2.09</td>
</tr>
<tr>
<td style="width: 320.909px;" data-sheets-value='{"1":2,"2":"Kia Corp/Hyundai Motor Co"}' data-sheets-formula='=Data!R[-13]C[5]&amp;"/"&amp;Data!R[-13]C[6]'>Kia Corp/Hyundai Motor Co</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-2.0407109696616685}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$2")'>-2.04</td>
<td style="text-align: right; width: 45.4545px;" data-sheets-value='{"1":3,"3":-2.0025888653486463}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$3")'>-2.00</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.1695043509161958}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$4")'>-1.17</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.004698906236027549}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$5")'>0.00</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":0.10273148905333784}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$6")'>0.10</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.0802292544899608}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$7")'>-1.08</td>
</tr>
<tr>
<td style="width: 320.909px;" data-sheets-value='{"1":2,"2":"Celltrion HealthCare Co Ltd/Celltrion, Inc."}' data-sheets-formula='=Data!R[-14]C[7]&amp;"/"&amp;Data!R[-14]C[8]'>Celltrion HealthCare Co Ltd/Celltrion, Inc.</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.31044173489418}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$2")'>-0.31</td>
<td style="text-align: right; width: 45.4545px;" data-sheets-value='{"1":3,"3":-0.7388105592067177}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$3")'>-0.74</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.0110561898756243}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$4")'>-1.01</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.0565672498648304}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$5")'>-0.06</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.1863196550598434}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$6")'>-0.19</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":0.5127886597937376}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$7")'>0.51</td>
</tr>
<tr>
<td style="width: 320.909px;" data-sheets-value='{"1":2,"2":"BGF Retail Co Ltd/GS Retail Co Ltd"}' data-sheets-formula='=Data!R[-15]C[9]&amp;"/"&amp;Data!R[-15]C[10]'>BGF Retail Co Ltd/GS Retail Co Ltd</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":0.2360588141562515}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$2")'>0.24</td>
<td style="text-align: right; width: 45.4545px;" data-sheets-value='{"1":3,"3":-2.102593735245234}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$3")'>-2.10</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-2.5568836748773385}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$4")'>-2.56</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-2.3214237049099196}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$5")'>-2.32</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.024092449046153583}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$6")'>-0.02</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":0.4712110272418423}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$7")'>0.47</td>
</tr>
<tr>
<td style="width: 320.909px;" data-sheets-value='{"1":2,"2":"Hyundai Department Store Co Ltd/Shinsegae Inc"}' data-sheets-formula='=Data!R[-16]C[11]&amp;"/"&amp;Data!R[-16]C[12]'>Hyundai Department Store Co Ltd/Shinsegae Inc</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.807722354711979}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$2")'>-1.81</td>
<td style="text-align: right; width: 45.4545px;" data-sheets-value='{"1":3,"3":-2.309293992473888}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$3")'>-2.31</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-2.530789825470454}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$4")'>-2.53</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.733312375711856}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$5")'>-1.73</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-1.3190244950375916}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$6")'>-1.32</td>
<td style="text-align: right; width: 46.3636px;" data-sheets-value='{"1":3,"3":-0.03766239765956894}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=indirect(R[0]C8&amp;"!$I$7")'>-0.04</td>
</tr>
<tr><td colspan="7" rowspan="1" style="width: 723.636px;" data-sheets-value='{"1":2,"2":"Source: KRX"}'>Source: KRX</td></tr>
</tbody>
</table>Out of these three pairs currently at ±2.0σ, the Kia Corp/Hyundai Motor pair deserves special attention as mean reversion appears already in progress for the other two pairs.

Korea has 6 closely correlated and co-integrated sector pairs. All of them have a +0.8 180-day correlation coefficient. Also, they all have a sufficiently linear 180-day price scattered chart showing the nature of their high cointegration.








Closely correlated &amp; co-integrated sector pairs in Korea

Name
Ticker
Name
Ticker
180-day correlation


Shinhan Financial Group Co., Ltd.
055550
KB Financial Group Inc
105560
0.96105


LG Uplus Corp
032640
SK Telecom Co Ltd
017670
0.89928


Kia Corp
000270
Hyundai Motor Co
005380
0.86689


Celltrion HealthCare Co Ltd
091990
Celltrion, Inc.
068270
0.85053


BGF Retail Co Ltd
282330
GS Retail Co Ltd
007070
0.77600


Hyundai Department Store Co Ltd
069960
Shinsegae Inc
004170
0.93278

Source: KRX

In the light of the short-selling resumption next week, these sector pairs will likely draw attention.Then, which pair is at an extreme sigma (±2.0σ) on a 20-day moving average? At the last close, three pairs come on the radar.
Shinhan Financial and KB Financial: +2.16σ
Kia Corp and Hyundai Motor: -2.04σ
Hyundai Department Store and Shinsegae: -1.81σ












Sigma (σ) on a 20-day moving average
Today
T-1
T-2
T-3
T-4
T-5


Shinhan Financial Group Co., Ltd./KB Financial Group Inc
2.16
2.54
1.79
1.11
0.18
-0.23


LG Uplus Corp/SK Telecom Co Ltd
-0.25
-0.10
-1.61
-1.64
-1.42
-2.09


Kia Corp/Hyundai Motor Co
-2.04
-2.00
-1.17
0.00
0.10
-1.08


Celltrion HealthCare Co Ltd/Celltrion, Inc.
-0.31
-0.74
-1.01
-0.06
-0.19
0.51


BGF Retail Co Ltd/GS Retail Co Ltd
0.24
-2.10
-2.56
-2.32
-0.02
0.47


Hyundai Department Store Co Ltd/Shinsegae Inc
-1.81
-2.31
-2.53
-1.73
-1.32
-0.04

Source: KRX

Out of these three pairs currently at ±2.0σ, the Kia Corp/Hyundai Motor pair deserves special attention as mean reversion appears already in progress for the other two pairs.

Korea Pair Trade Idea: 6 Closely Correlated & Cointegrated Sector Pairs

Executive Summary:  A flattening US interest rate yield curve changed the dynamics for credit investors for most of the month of April, in complete contrast to how interest rates behaved in March. The narrative changed to one that was Treasury bond supportive, and this supported shorter-duration securities. Meanwhile yield curve flattening pushed credit spreads in the opposite direction longer out the curve.The sweet spot for total return investors moved dramatically. Investors that were positioned in higher duration bonds witnessed underperformance on a total return basis. Meanwhile, investors in lower duration bonds outperformed on a total return basis. However, as the month ended, we began to witness a return of the reflation trade with US interest rates ending the month on an upswing. May could see a repeat of March’s carry-trade performance.From a pure credit spread perspective, ignoring movements in the risk-free rate, Asia’s bank bonds had a mixed performance with one-third of the +100 bonds that we track widening during the month. Vaccine versus virus concerns led to lower economic growth projections by the market than seen in the prior month. Expectations for an unsynchronized global recovery also factored. Taken together, this would delay the much needed catch-up in credit fundamentals to match the monetary accommodation driven spread compression over the last year. As such, bond spread tightening was less ebullient while widening lessened the amount of fundamental performance needed to justify lofty spread levels.

Executive Summary:  A flattening US interest rate yield curve changed the dynamics for credit investors for most of the month of April, in complete contrast to how interest rates behaved in March. The narrative changed to one that was Treasury bond supportive, and this supported shorter-duration securities. Meanwhile yield curve flattening pushed credit spreads in the opposite direction longer out the curve.The sweet spot for total return investors moved dramatically. Investors that were positioned in higher duration bonds witnessed underperformance on a total return basis. Meanwhile, investors in lower duration bonds outperformed on a total return basis. However, as the month ended, we began to witness a return of the reflation trade with US interest rates ending the month on an upswing. May could see a repeat of March’s carry-trade performance.From a pure credit spread perspective, ignoring movements in the risk-free rate, Asia’s bank bonds had a mixed performance with one-third of the +100 bonds that we track widening during the month. Vaccine versus virus concerns led to lower economic growth projections by the market than seen in the prior month. Expectations for an unsynchronized global recovery also factored. Taken together, this would delay the much needed catch-up in credit fundamentals to match the monetary accommodation driven spread compression over the last year. As such, bond spread tightening was less ebullient while widening lessened the amount of fundamental performance needed to justify lofty spread levels.

Bank Credit Compass: Total Return Sweet Spot Moves, but May Rebound

Korea/Asia, Events, IPOs, Douglas Research Advisory

Douglas Kim

Korea/Asia, Events, IPOs

Equity Capital Markets

Korea Investment Holdings Co, Ltd. / Equity Capital Markets

<div class="content-highlight">According to K-Bank, it gained 1.72 million new customers in 1Q 2021. This was more than the 1.57 million customers that K-Bank gained from 2018 to 2020. It is estimated about 70% of K-Bank's new customers in 2021 are young people in their 20s and 30s that are flocking to K-Bank to invest in cryptocurrencies such as Bitcoin and Ethereum at the cryptocurrency exchange Upbit, which requires the users to have an account at K-Bank to deposit or withdraw money for cryptocurrency trading.</div><a href="https://www.hankyung.com/finance/article/2021041583121" target="_blank" rel="noopener">Korea Value Asset Mgmt</a> has a 27.1% stake in Kakao Bank. Korea Value Asset Mgmt is 100% owned by Korea Investment &amp; Securities, which is also 100% owned by <a href="https://www.smartkarma.com/entities/korea-investment-holdings-co-ltd" target="_blank" data-smartkarma-citation="entities:14839:1" rel="noopener">Korea Investment Holdings Co, Ltd. (071050 KS)</a>. Thus, Korea Investment &amp; Securities directly and indirectly owns 31.8% stake in Kakao Bank. Korea Investment &amp; Securities currently has a market cap of 5.8 trillion won. If Kakao Bank is valued at 20 trillion won, a 31.8% stake in the company would be worth 6.4 trillion won, which would be worth 110% of Korea Investment &amp; Securities' market cap. Therefore, although the exact number of how many new licenses for Internet banks remains uncertain, we believe that there could be any many as 2 to 3+ new Internet banks in the next 2-3 years. It is probably no coincidence that Kakao Bank is trying to complete its IPO this year. Kakao Bank will be aiming to accelerate this IPO prior to the Korean government giving out new licenses for Internet banks this year.

According to K-Bank, it gained 1.72 million new customers in 1Q 2021. This was more than the 1.57 million customers that K-Bank gained from 2018 to 2020. It is estimated about 70% of K-Bank's new customers in 2021 are young people in their 20s and 30s that are flocking to K-Bank to invest in cryptocurrencies such as Bitcoin and Ethereum at the cryptocurrency exchange Upbit, which requires the users to have an account at K-Bank to deposit or withdraw money for cryptocurrency trading.Korea Value Asset Mgmt has a 27.1% stake in Kakao Bank. Korea Value Asset Mgmt is 100% owned by Korea Investment &amp; Securities, which is also 100% owned by Korea Investment Holdings Co, Ltd. (071050 KS). Thus, Korea Investment &amp; Securities directly and indirectly owns 31.8% stake in Kakao Bank. Korea Investment &amp; Securities currently has a market cap of 5.8 trillion won. If Kakao Bank is valued at 20 trillion won, a 31.8% stake in the company would be worth 6.4 trillion won, which would be worth 110% of Korea Investment &amp; Securities' market cap. Therefore, although the exact number of how many new licenses for Internet banks remains uncertain, we believe that there could be any many as 2 to 3+ new Internet banks in the next 2-3 years. It is probably no coincidence that Kakao Bank is trying to complete its IPO this year. Kakao Bank will be aiming to accelerate this IPO prior to the Korean government giving out new licenses for Internet banks this year. 

K-Bank Vs. Kakao Bank: A Big Change in New Customers Driven by Cryptos

South Korea

Shinhan Financial Group Co., Ltd., a holding company, provides a full range of consumer and commercial banking-related financial services.  The company's main businesses include banking, securities brokerage, trust banking, and assets management to individuals, businesses, and other financial institutions.

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Shinhan Financial (055550 KS)

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