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

Korea's financial regulators, the Financial Services Commission, had advised the local banks and bank holding companies to keep a dividend payout ratio below 20%. The purpose was clear. It was to secure a sufficient capital buffer to absorb potential losses resulting from COVID-19.This recommendation for the 20% cap is effective until the end of this month.The market widely expects the FSC to not renew this 20% dividend cap after this month amid the fast-rebounding economy.

Korea's financial regulators, the Financial Services Commission, had advised the local banks and bank holding companies to keep a dividend payout ratio below 20%. The purpose was clear. It was to secure a sufficient capital buffer to absorb potential losses resulting from COVID-19.This recommendation for the 20% cap is effective until the end of this month.The market widely expects the FSC to not renew this 20% dividend cap after this month amid the fast-rebounding economy.

Korea Banks 20% Dividend Cap Scrapping: Projected Div Payout & Yield of 8 Bank Stocks

Korea's local banks are still going strong as an inflation hedge stock. In particular, the sector leaders continue to shine the most, which is understandable.Shinhan Financial Group has outperformed most of its local peers of late, causing a harsh diversion vs. several names.<table class="table table-striped">
<colgroup>
<col width="200">
<col width="70">
<col width="70">
<col width="200">
<col width="70">
<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 style="text-align: right;" data-sheets-value='{"1":2,"2":"Ticker"}'>Ticker</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Market cap"}'>Market cap</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":"Ticker"}'>Ticker</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"Market cap"}'>Market cap</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":"Woori Financial Group Inc"}'>Woori Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":316140}'>316140</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":7981.05789715}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩7,981.1B</td>
<td data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"055550"}'>055550</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":21826.3311565}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩21,826.3B</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.9223855252672568}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.92</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-1.7434864364413833}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>-1.74</td>
</tr>
<tr>
<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":2,"2":"086790"}'>086790</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":13676.0259241}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩13,676.0B</td>
<td data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"055550"}'>055550</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":21826.3311565}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩21,826.3B</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.9415706574402087}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.94</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-1.6583215542673198}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>-1.66</td>
</tr>
<tr>
<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":2,"2":"105560"}'>105560</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":23701.05144}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩23,701.1B</td>
<td data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"055550"}'>055550</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":21826.3311565}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩21,826.3B</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.9610188453950309}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.96</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-1.5589371642688024}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>-1.56</td>
</tr>
<tr>
<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":2,"2":"138930"}'>138930</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":2591.1852057}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩2,591.2B</td>
<td data-sheets-value='{"1":2,"2":"Shinhan Financial Group Co., Ltd."}'>Shinhan Financial Group Co., Ltd.</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"055550"}'>055550</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":21826.3311565}' data-sheets-numberformat='[null,2,"₩#,##0.0B"]'>₩21,826.3B</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.972704739094366}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>0.97</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-1.0491776745143695}' data-sheets-numberformat='[null,2,"#,##0.00000",1]'>-1.05</td>
</tr>
<tr><td colspan="8">Source: KRX 
</td></tr>
</tbody>
</table>The harshest diversion at this point goes to Woori Financial Group. The current sigma is -1.74 on a 20-day moving average. Its price ratio relative to Shinhan is now 9.55% below the 180-day average. In fact, it is at a 180-day low.<table class="table table-striped">
<colgroup>
<col width="70">
<col width="70">
<col width="70">
<col width="70">
</colgroup>
<tbody>
<tr>
<td data-sheets-value='{"1":2,"2":"Price ratio"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>Price ratio</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"%Δ to 180-day mean"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>%Δ to 180-day mean</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"%Δ to 180-day high"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>%Δ to 180-day high</td>
<td style="text-align: right;" data-sheets-value='{"1":2,"2":"%Δ to 180-day low"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>%Δ to 180-day low</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Woori Financial Group Inc"}' data-sheets-formula="=R[-16]C[0]">Woori Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.09551050492215632}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">-9.55%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.158687466379774}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">-15.87%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">0.00%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"Hana Financial Group Inc"}' data-sheets-formula="=R[-16]C[0]">Hana Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.007327297625351524}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">-0.73%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.10178905429741551}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">-10.18%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.14368007113294015}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">14.37%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"KB Financial Group Inc"}' data-sheets-formula="=R[-16]C[0]">KB Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.022657232352227285}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">-2.27%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.1009918086293668}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">-10.10%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.08257600627747405}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">8.26%</td>
</tr>
<tr>
<td data-sheets-value='{"1":2,"2":"BNK Financial Group Inc"}' data-sheets-formula="=R[-16]C[0]">BNK Financial Group Inc</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.037819100378221324}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">3.78%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":-0.048692073349346776}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">-4.87%</td>
<td style="text-align: right;" data-sheets-value='{"1":3,"3":0.12800025002083512}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C5-R[0]C[4])/R[0]C[4]">12.80%</td>
</tr>
<tr><td colspan="4">Source: KRX</td></tr>
</tbody>
</table><section class="figure"><figure class="editor-figure mceNonEditable"><img src="https://static-prod.smartkarma.com/images/media/000/210/024/original/Woori+Financial+Group+Inc+vs.+Shinhan+Financial+Group+Co.%2C+Ltd.png?1622371064" class="editor-image img-responsive" data-smartkarma-image-id="210024"></figure></section>

Korea's local banks are still going strong as an inflation hedge stock. In particular, the sector leaders continue to shine the most, which is understandable.Shinhan Financial Group has outperformed most of its local peers of late, causing a harsh diversion vs. several names.












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


Woori Financial Group Inc
316140
₩7,981.1B
Shinhan Financial Group Co., Ltd.
055550
₩21,826.3B
0.92
-1.74


Hana Financial Group Inc
086790
₩13,676.0B
Shinhan Financial Group Co., Ltd.
055550
₩21,826.3B
0.94
-1.66


KB Financial Group Inc
105560
₩23,701.1B
Shinhan Financial Group Co., Ltd.
055550
₩21,826.3B
0.96
-1.56


BNK Financial Group Inc
138930
₩2,591.2B
Shinhan Financial Group Co., Ltd.
055550
₩21,826.3B
0.97
-1.05

Source: KRX 


The harshest diversion at this point goes to Woori Financial Group. The current sigma is -1.74 on a 20-day moving average. Its price ratio relative to Shinhan is now 9.55% below the 180-day average. In fact, it is at a 180-day low.








Price ratio
%Δ to 180-day mean
%Δ to 180-day high
%Δ to 180-day low


Woori Financial Group Inc
-9.55%
-15.87%
0.00%


Hana Financial Group Inc
-0.73%
-10.18%
14.37%


KB Financial Group Inc
-2.27%
-10.10%
8.26%


BNK Financial Group Inc
3.78%
-4.87%
12.80%

Source: KRX



Korea Financials: Woori Vs. Shinhan Price Ratio at a 180-Day Low

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

Financials Credit Analyst

Hank Calenti, CFA

CreditContinuum

Credit

Executive Summary: Friday’s jobs report shocker capped off a week which included US Treasury Secretary Janet Yellen needing to be Mirandized. This was followed by the Fed stating the obvious in its financial stability report. Unmentioned in this report is that the role of this Fed is no longer to take away the punchbowl, but instead it is the bartender. The week ended with a jobs report that was the biggest miss in nominal terms, ever.The report was at odds with recent PMI surveys in which respondents stated that it was difficult to hire. This may be the result of frictions in hiring as unemployment benefits as well as child-care needs and postponed school re-openings delay employee re-entry. The truth serum for the US economy may occur around September when extended unemployment payments expire.The market impact was an immediate 10 basis point (bp) drop in the 10-year Treasury while the 2-year note even fell 1bp. Prices on the 10-year gave up these gains as the day progressed, but the smart money, that is the bond market, may be correct with the recent dampening of interest rates suggesting a longer time period to a less accommodative Fed.  The US Treasury bond curve ended the week flatter with short-end yields falling by much less than the longer part of the curve.With rates continuing to push credit spreads, Asia- and European-headquartered banks generated negative total returns in all but the lowest duration notes. There was a clear preference for Additional Tier 1 (AT1) bond issuance over the past week while over US$11 billion equivalent was raised by financial institutions globally in USD and Euros. In what may have been the busiest AT1 bond issuance weeks of the year, five institutions brought over US$6.5 billion in six securities.Nonetheless, this is a great credit environment and spreads which could go even tighter into year-end. A single piece of data, such as April’s non-farm payroll figure, is unlikely to change investor positioning. It may be a little too early to fade the market as fundamentals catch-up.Next week we will get a record-matching $126 billion slate of Treasury auctions. We may also see a robust serving of corporate and financials debt issuance. US consumer-price data is set to be published on Tuesday, 12 May. A sharp annual acceleration is forecast, although the market is likely to dismiss the figure as resulting from a comparison to depressed levels of a year earlier.With peak Q1 earnings season now behind us, Japanese banks will deliver their year-end results over the next week while several other stragglers pepper the remainder of the month. 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 with material debt issuance over the next few weeks after reporting earnings.Market Outlook: The question is whether the over-heating narrative will make a comeback and bring rate and tapering expectations closer. Financial stability risks appear binary from here. Either the Fed is over-accommodative, and it sits on its hands too long while asset bubbles over-inflate so that it gets nasty when it moves, or the Fed gets impatient, and it lowers accommodation unexpectedly which creates a risk off reaction. In both cases, curves flatten in a risk-off environment. We suspect the former is more likely, and for now, the Fed remains the bartender.

Executive Summary: Friday’s jobs report shocker capped off a week which included US Treasury Secretary Janet Yellen needing to be Mirandized. This was followed by the Fed stating the obvious in its financial stability report. Unmentioned in this report is that the role of this Fed is no longer to take away the punchbowl, but instead it is the bartender. The week ended with a jobs report that was the biggest miss in nominal terms, ever.The report was at odds with recent PMI surveys in which respondents stated that it was difficult to hire. This may be the result of frictions in hiring as unemployment benefits as well as child-care needs and postponed school re-openings delay employee re-entry. The truth serum for the US economy may occur around September when extended unemployment payments expire.The market impact was an immediate 10 basis point (bp) drop in the 10-year Treasury while the 2-year note even fell 1bp. Prices on the 10-year gave up these gains as the day progressed, but the smart money, that is the bond market, may be correct with the recent dampening of interest rates suggesting a longer time period to a less accommodative Fed.  The US Treasury bond curve ended the week flatter with short-end yields falling by much less than the longer part of the curve.With rates continuing to push credit spreads, Asia- and European-headquartered banks generated negative total returns in all but the lowest duration notes. There was a clear preference for Additional Tier 1 (AT1) bond issuance over the past week while over US$11 billion equivalent was raised by financial institutions globally in USD and Euros. In what may have been the busiest AT1 bond issuance weeks of the year, five institutions brought over US$6.5 billion in six securities.Nonetheless, this is a great credit environment and spreads which could go even tighter into year-end. A single piece of data, such as April’s non-farm payroll figure, is unlikely to change investor positioning. It may be a little too early to fade the market as fundamentals catch-up.Next week we will get a record-matching $126 billion slate of Treasury auctions. We may also see a robust serving of corporate and financials debt issuance. US consumer-price data is set to be published on Tuesday, 12 May. A sharp annual acceleration is forecast, although the market is likely to dismiss the figure as resulting from a comparison to depressed levels of a year earlier.With peak Q1 earnings season now behind us, Japanese banks will deliver their year-end results over the next week while several other stragglers pepper the remainder of the month. 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 with material debt issuance over the next few weeks after reporting earnings.Market Outlook: The question is whether the over-heating narrative will make a comeback and bring rate and tapering expectations closer. Financial stability risks appear binary from here. Either the Fed is over-accommodative, and it sits on its hands too long while asset bubbles over-inflate so that it gets nasty when it moves, or the Fed gets impatient, and it lowers accommodation unexpectedly which creates a risk off reaction. In both cases, curves flatten in a risk-off environment. We suspect the former is more likely, and for now, the Fed remains the bartender.

Bank Credit Weekly: Jobs Report Shocker Flattens Curves

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

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








Comp 1
Comp 2
180-day correlation coefficient
Business type


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


Hyundai Department Store Co Ltd
Lotte Shopping Co Ltd
0.97033
Retail


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


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


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


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


Hansol Chemical Co Ltd
Posco Chemical Co Ltd
0.95374
Chemical


Hanwha Aerospace Co Ltd
Korea Aerospace Industries Ltd
0.95243
Aerospace


KG DongbuSteel Co Ltd
POSCO
0.95174
Steel


KG DongbuSteel Co Ltd
Hyundai Steel Co
0.94972
Steel

Source: KRX

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








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


E-Mart Inc
Lotte Shopping Co Ltd
0.85128
-3.27


Silicon Works Co Ltd
LG Electronics Inc.
0.82411
2.79


BGF Retail Co Ltd
E-Mart Inc
0.84733
2.58


Woori Financial Group Inc
KB Financial Group Inc
0.84517
-2.47


Pan Ocean Co Ltd
HMM Co Ltd
0.93591
-2.33


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


SK Innovation Co Ltd
Samsung SDI Co Ltd
0.94587
2.19


Hanwha Aerospace Co Ltd
Korea Aerospace Industries Ltd
0.95243
2.09


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


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

Source: KRX



Korea Sector Long/Short Pairs Trade Ideas

South Korea

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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