Global Emerging Market Equities, Propitious Research

Wium Malan, CFA

Global Emerging Market Equities

Independent, Fundamental Quantitative Research

Propitious Research

Fully independent, fundamental and quantitative equity research, focused on companies in the Telecommunication, Media, Technology, Internet and Healthcare sectors in the Global Emerging Markets. Idea generation focused on Quality stocks, trading below their intrinsic Value, with sound Corporate Governance.

Quantitative Analysis

This insight provides the output, and an overview of our, machine learning-driven, proprietary GEM screening model, which takes a three-pronged approach to find the best medium-term long ideas within the top100 largest stocks in the MSCI GEM Index. The model is based on 1) a typical quantitative multifactor screening model, enhanced by two Artificial Intelligence Technology-based approaches using 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model. This model, which has been developed and tweaked over the past 24 months (and implemented in its current state for &gt;12 months), has a solid real-world performance track record, which I set out in the detail below. Notable new entrants to the top decile this month include South Korean auto names, <a href="https://www.smartkarma.com/entities/kia-motors-corp" target="_blank" data-smartkarma-citation="entities:156:1" rel="noopener">Kia Motors Corp (000270 KS)</a> and <a href="https://www.smartkarma.com/entities/hyundai-motor-co" target="_blank" data-smartkarma-citation="entities:155:1" rel="noopener">Hyundai Motor Co (005380 KS)</a> as well as Brazilian state-owned energy giant, <a href="https://www.smartkarma.com/entities/petroleo-brasileiro-sa" target="_blank" data-smartkarma-citation="entities:24459:1" rel="noopener">Petroleo Brasileiro Sa (PETR3 BZ)</a>.<table class="table table-striped">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<tbody>
<tr>
<td style="text-align: center; width: 48pt;">rank</td>
<td style="width: 169pt;">Name</td>
<td style="width: 119pt;">Sector</td>
<td style="width: 95pt;">Location</td>
<td style="text-align: center; width: 48pt;">prev.</td>
</tr>
<tr>
<td style="text-align: center;">1</td>
<td><a href="https://www.smartkarma.com/entities/cnooc-ltd" target="_blank" data-smartkarma-citation="entities:175:1" rel="noopener">CNOOC Ltd (883 HK)</a></td>
<td>Energy</td>
<td>China</td>
<td style="text-align: center;">1</td>
</tr>
<tr>
<td style="text-align: center;">2</td>
<td><a href="https://www.smartkarma.com/entities/mediatek-inc" target="_blank" data-smartkarma-citation="entities:772:1" rel="noopener">Mediatek Inc (2454 TT)</a></td>
<td>Information Technology</td>
<td>Taiwan</td>
<td style="text-align: center;">5</td>
</tr>
<tr>
<td style="text-align: center;">3</td>
<td><a href="https://www.smartkarma.com/entities/sk-hynix-inc" target="_blank" data-smartkarma-citation="entities:16955:1" rel="noopener">SK Hynix (000660 KS)</a></td>
<td>Information Technology</td>
<td>Korea (South)</td>
<td style="text-align: center;">23</td>
</tr>
<tr>
<td style="text-align: center;">4</td>
<td><a href="https://www.smartkarma.com/entities/kia-motors-corp" target="_blank" data-smartkarma-citation="entities:156:1" rel="noopener">Kia Motors Corp (000270 KS)</a></td>
<td>Consumer Discretionary</td>
<td>Korea (South)</td>
<td style="text-align: center;">60</td>
</tr>
<tr>
<td style="text-align: center;">5</td>
<td><a href="https://www.smartkarma.com/entities/bank-of-china-ltd-h" target="_blank" data-smartkarma-citation="entities:656:1" rel="noopener">Bank Of China Ltd (H) (3988 HK)</a></td>
<td>Financials</td>
<td>China</td>
<td style="text-align: center;">25</td>
</tr>
<tr>
<td style="text-align: center;">6</td>
<td><a href="https://www.smartkarma.com/entities/ptt-pcl" target="_blank" data-smartkarma-citation="entities:197:1" rel="noopener">PTT PCL (PTT TB)</a></td>
<td>Energy</td>
<td>Thailand</td>
<td style="text-align: center;">4</td>
</tr>
<tr>
<td style="text-align: center;">7</td>
<td><a href="https://www.smartkarma.com/entities/samsung-electronics-co-ltd" target="_blank" data-smartkarma-citation="entities:191:1" rel="noopener">Samsung Electronics (005930 KS)</a></td>
<td>Information Technology</td>
<td>Korea (South)</td>
<td style="text-align: center;">9</td>
</tr>
<tr>
<td style="text-align: center;">8</td>
<td><a href="https://www.smartkarma.com/entities/hyundai-motor-co" target="_blank" data-smartkarma-citation="entities:155:1" rel="noopener">Hyundai Motor Co (005380 KS)</a></td>
<td>Consumer Discretionary</td>
<td>Korea (South)</td>
<td style="text-align: center;">80</td>
</tr>
<tr>
<td style="text-align: center;">9</td>
<td><a href="https://www.smartkarma.com/entities/china-mobile-ltd" target="_blank" data-smartkarma-citation="entities:27:1" rel="noopener">China Mobile (941 HK)</a></td>
<td>Communication</td>
<td>China</td>
<td style="text-align: center;">29</td>
</tr>
<tr>
<td style="text-align: center;">10</td>
<td><a href="https://www.smartkarma.com/entities/petroleo-brasileiro-sa" target="_blank" data-smartkarma-citation="entities:24459:1" rel="noopener">Petroleo Brasileiro Sa (PETR3 BZ)</a></td>
<td>Energy</td>
<td>Brazil</td>
<td style="text-align: center;">59</td>
</tr>
<tr><td colspan="5" style="text-align: left;">Source: FactSet financial data and analytics, my estimates and calculations, Tensorflow, Keras, Google Colaboratory, pandas</td></tr>
</tbody>
</table>As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions.

This insight provides the output, and an overview of our, machine learning-driven, proprietary GEM screening model, which takes a three-pronged approach to find the best medium-term long ideas within the top100 largest stocks in the MSCI GEM Index. The model is based on 1) a typical quantitative multifactor screening model, enhanced by two Artificial Intelligence Technology-based approaches using 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model. This model, which has been developed and tweaked over the past 24 months (and implemented in its current state for &gt;12 months), has a solid real-world performance track record, which I set out in the detail below. Notable new entrants to the top decile this month include South Korean auto names, Kia Motors Corp (000270 KS) and Hyundai Motor Co (005380 KS) as well as Brazilian state-owned energy giant, Petroleo Brasileiro Sa (PETR3 BZ).









rank
Name
Sector
Location
prev.


1
CNOOC Ltd (883 HK)
Energy
China
1


2
Mediatek Inc (2454 TT)
Information Technology
Taiwan
5


3
SK Hynix (000660 KS)
Information Technology
Korea (South)
23


4
Kia Motors Corp (000270 KS)
Consumer Discretionary
Korea (South)
60


5
Bank Of China Ltd (H) (3988 HK)
Financials
China
25


6
PTT PCL (PTT TB)
Energy
Thailand
4


7
Samsung Electronics (005930 KS)
Information Technology
Korea (South)
9


8
Hyundai Motor Co (005380 KS)
Consumer Discretionary
Korea (South)
80


9
China Mobile (941 HK)
Communication
China
29


10
Petroleo Brasileiro Sa (PETR3 BZ)
Energy
Brazil
59

Source: FactSet financial data and analytics, my estimates and calculations, Tensorflow, Keras, Google Colaboratory, pandas

As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions.

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

Hyundai mobis continues to be in a harsh diversion relative to its siblings, Kia Corp and Hyundai Motor. It is at a -2.25σ on a 20-day moving average vs. Kia Corp. The 20-day sigma relative to Hyundai Motor is -1.55 now. <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: 147.682px;" data-sheets-value='{"1":2,"2":"Comp 1 (Long)"}'>Comp 1 (Long)</td>
<td style="width: 115.56px;" data-sheets-value='{"1":2,"2":"Comp 2 (Short)"}'>Comp 2 (Short)</td>
<td style="text-align: right; width: 117.552px;" 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: 127.604px;" data-sheets-value='{"1":2,"2":"180-day correlation coefficient"}'>180-day correlation coefficient</td>
<td style="text-align: right; width: 133.659px;" 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: 147.682px;" data-sheets-value='{"1":2,"2":"HYUNDAI MOBIS CO., LTD."}' data-sheets-formula="=Data!R[-1]C[1]">HYUNDAI MOBIS CO., LTD.</td>
<td style="width: 115.56px;" data-sheets-value='{"1":2,"2":"Kia Corp"}' data-sheets-formula="=Data!R[-1]C[1]">Kia Corp</td>
<td style="text-align: right; width: 117.552px;" data-sheets-value='{"1":3,"3":-2.2518639492767143}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]">-2.25</td>
<td style="text-align: right; width: 127.604px;" data-sheets-value='{"1":3,"3":0.7480010585761807}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=correl(index(googlefinance(R[0]C[2], "price",today()-200, today()), 0,2), index(googlefinance(R[0]C[3], "price",today()-200, today()), 0,2))'>0.75</td>
<td style="text-align: right; width: 133.659px;" data-sheets-value='{"1":3,"3":0.5563729124603229}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[2]C[-3]">0.56</td>
</tr>
<tr>
<td style="width: 147.682px;" data-sheets-value='{"1":2,"2":"HYUNDAI MOBIS CO., LTD."}' data-sheets-formula="=Data!R[-2]C[3]">HYUNDAI MOBIS CO., LTD.</td>
<td style="width: 115.56px;" data-sheets-value='{"1":2,"2":"Hyundai Motor Co"}' data-sheets-formula="=Data!R[-2]C[3]">Hyundai Motor Co</td>
<td style="text-align: right; width: 117.552px;" data-sheets-value='{"1":3,"3":-1.5544482164241549}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=R[8]C[-1]">-1.55</td>
<td style="text-align: right; width: 127.604px;" data-sheets-value='{"1":3,"3":0.929675478057548}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula='=correl(index(googlefinance(R[0]C[2], "price",today()-200, today()), 0,2), index(googlefinance(R[0]C[3], "price",today()-200, today()), 0,2))'>0.93</td>
<td style="text-align: right; width: 133.659px;" data-sheets-value='{"1":3,"3":0.7886057127133221}' data-sheets-numberformat='[null,2,"#,##0.00",1]' data-sheets-formula="=Beta!R[1]C[-1]">0.79</td>
</tr>
<tr><td colspan="5" style="width: 726.016px;">Source: KRX</td></tr>
</tbody>
</table>The current price ratio is 19.25% and 9.09% below the 180-day mean vs. Kia Corp and Hyundai Motor, respectively. Mobis is currently at a 180-day low to both Kia and Hyundai Motor.<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: 149.466px;" data-sheets-value='{"1":2,"2":"Comp 1 (Long)"}'>Comp 1 (Long)</td>
<td style="width: 120.391px;" data-sheets-value='{"1":2,"2":"Comp 2 (Short)"}'>Comp 2 (Short)</td>
<td style="text-align: right; width: 118.372px;" data-sheets-value='{"1":2,"2":"Price ratio %Δ to 180-day mean"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>Price ratio %Δ to 180-day mean</td>
<td style="text-align: right; width: 128.398px;" data-sheets-value='{"1":2,"2":"Price ratio %Δ to 180-day high"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>Price ratio %Δ to 180-day high</td>
<td style="text-align: right; width: 125.43px;" data-sheets-value='{"1":2,"2":"Price ratio %Δ to 180-day low"}' data-sheets-numberformat='[null,4,"[$₩]#,##0"]'>Price ratio %Δ to 180-day low</td>
</tr>
<tr>
<td style="width: 149.466px;" data-sheets-value='{"1":2,"2":"HYUNDAI MOBIS CO., LTD."}' data-sheets-formula="=R[-16]C[0]">HYUNDAI MOBIS CO., LTD.</td>
<td style="width: 120.391px;" data-sheets-value='{"1":2,"2":"Kia Corp"}' data-sheets-formula="=R[-16]C[0]">Kia Corp</td>
<td style="text-align: right; width: 118.372px;" data-sheets-value='{"1":3,"3":-0.19253623208265946}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C6-R[0]C[4])/R[0]C[4]">-19.25%</td>
<td style="text-align: right; width: 128.398px;" data-sheets-value='{"1":3,"3":-0.3974244135311329}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C6-R[0]C[4])/R[0]C[4]">-39.74%</td>
<td style="text-align: right; width: 125.43px;" data-sheets-value='{"1":3,"3":0}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C6-R[0]C[4])/R[0]C[4]">0.00%</td>
</tr>
<tr>
<td style="width: 149.466px;" data-sheets-value='{"1":2,"2":"HYUNDAI MOBIS CO., LTD."}' data-sheets-formula="=R[-16]C[0]">HYUNDAI MOBIS CO., LTD.</td>
<td style="width: 120.391px;" data-sheets-value='{"1":2,"2":"Hyundai Motor Co"}' data-sheets-formula="=R[-16]C[0]">Hyundai Motor Co</td>
<td style="text-align: right; width: 118.372px;" data-sheets-value='{"1":3,"3":-0.09085981735173156}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C6-R[0]C[4])/R[0]C[4]">-9.09%</td>
<td style="text-align: right; width: 128.398px;" data-sheets-value='{"1":3,"3":-0.18788573693448832}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C6-R[0]C[4])/R[0]C[4]">-18.79%</td>
<td style="text-align: right; width: 125.43px;" data-sheets-value='{"1":3,"3":0}' data-sheets-numberformat='[null,3,"0.00%",1]' data-sheets-formula="=(R[0]C6-R[0]C[4])/R[0]C[4]">0.00%</td>
</tr>
<tr><td colspan="5" style="width: 726.016px;">Source: KRX</td></tr>
</tbody>
</table><section class="figure"><figure class="editor-figure mceNonEditable"><img src="https://static-prod.smartkarma.com/images/media/000/209/837/original/HYUNDAI+MOBIS+CO.%2C+LTD.+vs.+Kia+Corp.png?1622273377" class="editor-image img-responsive" data-smartkarma-image-id="209837"></figure></section><section class="figure"><figure class="editor-figure mceNonEditable"><img src="https://static-prod.smartkarma.com/images/media/000/209/838/original/HYUNDAI+MOBIS+CO.%2C+LTD.+vs.+Hyundai+Motor+Co.png?1622273387" class="editor-image img-responsive" data-smartkarma-image-id="209838"></figure></section>

Hyundai mobis continues to be in a harsh diversion relative to its siblings, Kia Corp and Hyundai Motor. It is at a -2.25σ on a 20-day moving average vs. Kia Corp. The 20-day sigma relative to Hyundai Motor is -1.55 now. 









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


HYUNDAI MOBIS CO., LTD.
Kia Corp
-2.25
0.75
0.56


HYUNDAI MOBIS CO., LTD.
Hyundai Motor Co
-1.55
0.93
0.79

Source: KRX

The current price ratio is 19.25% and 9.09% below the 180-day mean vs. Kia Corp and Hyundai Motor, respectively. Mobis is currently at a 180-day low to both Kia and Hyundai Motor.









Comp 1 (Long)
Comp 2 (Short)
Price ratio %Δ to 180-day mean
Price ratio %Δ to 180-day high
Price ratio %Δ to 180-day low


HYUNDAI MOBIS CO., LTD.
Kia Corp
-19.25%
-39.74%
0.00%


HYUNDAI MOBIS CO., LTD.
Hyundai Motor Co
-9.09%
-18.79%
0.00%

Source: KRX



Korea Sector Pair: Hyundai Mobis Vs. Kia and Hyundai Motor

Asset Allocation & Fund Positioning, Copley Fund Research

Steven Holden

Asset Allocation & Fund Positioning

Copley Fund Research

Copley Fund Research provide research and data on asset allocation among Global and Emerging Market equity funds.  The analysis details absolute and relative positioning across countries, sectors and industries together with highly granular data on single stock and single fund allocations.  The data is updated monthly with time series going back to January 2011.

Global Research - Analysis of 165 prominent Global equity funds, combined AUM $335bn
GEM Research - Analysis of 127 prominent global EM equity funds, combined AUM $250bn

Asset Allocation - Global and GEM Equity

Thematic (Sector/Industry)

EM Autos are the 10th largest industry group weight among active EM managers, with average weights of 1.55%.  The more interesting metric however, is the extent to which Autos have become one of the largest industry level underweights across global EM.  The increased underweight has almost exclusively been driven by two stocks, <a href="https://www.smartkarma.com/entities/nio-inc" target="_blank" data-smartkarma-citation="entities:27399:1" rel="noopener">NIO Inc (NIO US)</a> and <a href="https://www.smartkarma.com/entities/byd-co-ltd-h" target="_blank" data-smartkarma-citation="entities:20962:1" rel="noopener">BYD (1211 HK</a>, with both seeing benchmark weights rise on the back of strong price increases.Such outright volatility has created a challenge for active managers who are benchmarked to the MSCI Emerging Markets Index.   Namely, how do you position yourself in a prominent index component that rises 900% in the space of 12 months?  The temptation might be to add <a href="https://www.smartkarma.com/entities/nio-inc" target="_blank" data-smartkarma-citation="entities:27399:1" rel="noopener">NIO Inc (NIO US)</a> to a portfolio to dampen some of that benchmark risk, and indeed we have seen that happening among managers with low active shares.However, so far, high active managers have remained on the sidelines, sticking instead with more established autos stocks of <a href="https://www.smartkarma.com/entities/kia-motors-corp" target="_blank" data-smartkarma-citation="entities:156:1" rel="noopener">Kia Motors Corp (000270 KS)</a> , <a href="https://www.smartkarma.com/entities/geely-automobile-holdings-ltd" target="_blank" data-smartkarma-citation="entities:127:1" rel="noopener">Geely Auto (175 HK)</a> and <a href="https://www.smartkarma.com/entities/hero-motocorp-ltd" target="_blank" data-smartkarma-citation="entities:2391:1" rel="noopener">Hero Motocorp (HMCL IN)</a>.  Therefore, there is very little peer group risk in avoiding either <a href="https://www.smartkarma.com/entities/nio-inc" target="_blank" data-smartkarma-citation="entities:27399:1" rel="noopener">NIO Inc (NIO US)</a> or <a href="https://www.smartkarma.com/entities/byd-co-ltd-h" target="_blank" data-smartkarma-citation="entities:20962:1" rel="noopener">BYD (1211 HK)</a> .  In fact, if a high active manager decides to invest in either stock, it will represent an out-of-consensus long compared to the majority of your active peers.

EM Autos are the 10th largest industry group weight among active EM managers, with average weights of 1.55%.  The more interesting metric however, is the extent to which Autos have become one of the largest industry level underweights across global EM.  The increased underweight has almost exclusively been driven by two stocks, NIO Inc (NIO US) and BYD (1211 HK, with both seeing benchmark weights rise on the back of strong price increases.Such outright volatility has created a challenge for active managers who are benchmarked to the MSCI Emerging Markets Index.   Namely, how do you position yourself in a prominent index component that rises 900% in the space of 12 months?  The temptation might be to add NIO Inc (NIO US) to a portfolio to dampen some of that benchmark risk, and indeed we have seen that happening among managers with low active shares.However, so far, high active managers have remained on the sidelines, sticking instead with more established autos stocks of Kia Motors Corp (000270 KS) , Geely Auto (175 HK) and Hero Motocorp (HMCL IN).  Therefore, there is very little peer group risk in avoiding either NIO Inc (NIO US) or BYD (1211 HK) .  In fact, if a high active manager decides to invest in either stock, it will represent an out-of-consensus long compared to the majority of your active peers.

EM Autos Underweight:  Ignore Benchmark Risk and Focus on Peers

Pan-Asia Index Rebalances/ Events/ Delta One, Periscope Analytics

Brian Freitas

Pan-Asia Index Rebalances/ Events/ Delta One

Short selling has finally resumed in Korea and it has been a busy couple of days on that front with foreign investors piling into stocks that have run up more than their peers over the last year and are potentially overvalued.Long/short pairs could be implemented in Korea during the short sell ban if you had long inventory or if there were listed single stock futures on the short leg.With short selling permitted on constituents of the <a href="https://www.smartkarma.com/entities/korea-stock-exchange-kospi-200-index" target="_blank" data-smartkarma-citation="entities:26021:1" rel="noopener">Korea Stock Exchange Kospi 200 Index (KOSPI2 INDEX)</a>, we take a look at long/short pairs where both stocks are members of the index. These pairs belong to the same GICS sub-industry, are correlated and exhibit mean-reversion. We also use short sale data from the last couple of trading sessions to see which stocks are being targeted by short sellers.

Short selling has finally resumed in Korea and it has been a busy couple of days on that front with foreign investors piling into stocks that have run up more than their peers over the last year and are potentially overvalued.Long/short pairs could be implemented in Korea during the short sell ban if you had long inventory or if there were listed single stock futures on the short leg.With short selling permitted on constituents of the Korea Stock Exchange Kospi 200 Index (KOSPI2 INDEX), we take a look at long/short pairs where both stocks are members of the index. These pairs belong to the same GICS sub-industry, are correlated and exhibit mean-reversion. We also use short sale data from the last couple of trading sessions to see which stocks are being targeted by short sellers.

Korea Pair Trades: High Risk/Reward Names with Short Sell Screen

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

South Korea

Kia Corporation manufactures, sells, and exports passenger cars, mini-buses, trucks and commercial vehicles. The Company also manufactures auto-parts and tools using hybrid electric and fuel cell technology. Kia markets its products worldwide.

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Kia Corp (000270 KS)

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

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