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

Cross Asset Strategy

In this analysis, we provide a detailed overview of investor positioning among 250 active Global Emerging Market funds with combined AUM of $500bn.  Highlights:<ul>
<li>Asia the dominant region but also net underweight.  Managers move to underweight EMEA and strengthen overweight in the Americas.</li>
<li>China the dominant weight but allocations move lower.  Mexico and Russia the key overweights, China, Saudi Arabia and Taiwan the largest underweights.</li>
<li>Allocations in China Consumer Discretionary continue to head lower from their peak in October 2020 of 13.9%.  The underweight of -2.33% is towards the lowest on record and is driven by lighter than benchmark holdings in <a href="https://www.smartkarma.com/entities/alibaba-group-holding-ltd" target="_blank" data-smartkarma-citation="entities:787:1" rel="noopener">Alibaba Group (BABA US)</a>, <a href="https://www.smartkarma.com/entities/meituan-dianping" target="_blank" data-smartkarma-citation="entities:26970:1" rel="noopener">Meituan (3690 HK)</a> and <a href="https://www.smartkarma.com/entities/nio-inc" target="_blank" data-smartkarma-citation="entities:27399:1" rel="noopener">NIO Inc (NIO US)</a>.</li>
<li>The rise in Russian Consumer Discretionary ownership driven by Fix Price Group, with 16.1% of funds buying in to the stock after it's IPO back in March.  A quarter of funds now have exposure to the sector, the largest in its history.</li>
<li>Turkish Financials exposure continues to dwindle, with only 16.1% of funds invested.  The most widely held stocks in the sector are <a href="https://www.smartkarma.com/entities/haci-omer-sabanci-holding-as" target="_blank" data-smartkarma-citation="entities:27708:1" rel="noopener">Haci Omer Sabanci Holding AS (SAHOL TI)</a> and <a href="https://www.smartkarma.com/entities/akbank-tas" target="_blank" data-smartkarma-citation="entities:25033:1" rel="noopener">Akbank TAS (AKBNK TI)</a>, but both are held by just 6% of the funds in our analysis.  <a href="https://www.smartkarma.com/entities/turkiye-garanti-bankasi-as" target="_blank" data-smartkarma-citation="entities:25026:1" rel="noopener">Turkiye Garanti Bankasi As (GARAN TI)</a> is now held by 4% of funds after 4% closed positions in the last 6-months. </li>
<li>
<a href="https://www.smartkarma.com/entities/contemporary-amperex-techn-a" target="_blank" data-smartkarma-citation="entities:24404:1" rel="noopener">CATL (A) (300750 CH).</a> Ownership continues to rise among EM managers, with today's investment levels the highest on record across all metrics.  Recent buyers in to the name include JP Morgan, TT International and American Century.</li>
<li><a href="https://www.smartkarma.com/entities/sea-ltd" target="_blank" data-smartkarma-citation="entities:8509:1" rel="noopener">Sea Ltd (SE US)</a>.  Ownership remains on an upward trajectory with no signs of that slowing down.  Over the last 6-months, a further 6.8% of funds added exposure, including Fidelity EM Opportunity, Aberdeen Emerging Markets and Ashmore EM Active Equity.  GQG Partners was the only closure after cashing in on a position held since early 2020.</li>
<li>
<a href="https://www.smartkarma.com/entities/anhui-conch-cement-co-ltd" target="_blank" data-smartkarma-citation="entities:159:1" rel="noopener">Anhui Conch Cement (914 HK)</a>. Outflows have been severe, with 26.9% of funds invested 6-months ago, now down to just 16.9%.  Among those closing positions were Ashmore Emerging Markets Active, Pictet EM Sustainable and Dunham Emerging Markets.</li>
<li>
<a href="https://www.smartkarma.com/entities/credicorp-ltd" target="_blank" data-smartkarma-citation="entities:25963:1" rel="noopener">Credicorp Ltd (BAP US).  </a>Once among the larger holdings in EM active portfolios, <a href="https://www.smartkarma.com/entities/credicorp-ltd" target="_blank" data-smartkarma-citation="entities:25963:1" rel="noopener">Credicorp Ltd (BAP US)</a> has moved out of favour with EM fund managers.  In a slide that started in late 2019, the percentage of funds with investment in <a href="https://www.smartkarma.com/entities/credicorp-ltd" target="_blank" data-smartkarma-citation="entities:25963:1" rel="noopener">Credicorp Ltd (BAP US)</a> fell a further 6.4% over the last 6-months.  Closures were led by Liontrust, Matthews and HSBC.</li>
</ul>

In this analysis, we provide a detailed overview of investor positioning among 250 active Global Emerging Market funds with combined AUM of $500bn.  Highlights:
Asia the dominant region but also net underweight.  Managers move to underweight EMEA and strengthen overweight in the Americas.
China the dominant weight but allocations move lower.  Mexico and Russia the key overweights, China, Saudi Arabia and Taiwan the largest underweights.
Allocations in China Consumer Discretionary continue to head lower from their peak in October 2020 of 13.9%.  The underweight of -2.33% is towards the lowest on record and is driven by lighter than benchmark holdings in Alibaba Group (BABA US), Meituan (3690 HK) and NIO Inc (NIO US).
The rise in Russian Consumer Discretionary ownership driven by Fix Price Group, with 16.1% of funds buying in to the stock after it's IPO back in March.  A quarter of funds now have exposure to the sector, the largest in its history.
Turkish Financials exposure continues to dwindle, with only 16.1% of funds invested.  The most widely held stocks in the sector are Haci Omer Sabanci Holding AS (SAHOL TI) and Akbank TAS (AKBNK TI), but both are held by just 6% of the funds in our analysis.  Turkiye Garanti Bankasi As (GARAN TI) is now held by 4% of funds after 4% closed positions in the last 6-months. 

CATL (A) (300750 CH). Ownership continues to rise among EM managers, with today's investment levels the highest on record across all metrics.  Recent buyers in to the name include JP Morgan, TT International and American Century.
Sea Ltd (SE US).  Ownership remains on an upward trajectory with no signs of that slowing down.  Over the last 6-months, a further 6.8% of funds added exposure, including Fidelity EM Opportunity, Aberdeen Emerging Markets and Ashmore EM Active Equity.  GQG Partners was the only closure after cashing in on a position held since early 2020.

Anhui Conch Cement (914 HK). Outflows have been severe, with 26.9% of funds invested 6-months ago, now down to just 16.9%.  Among those closing positions were Ashmore Emerging Markets Active, Pictet EM Sustainable and Dunham Emerging Markets.

Credicorp Ltd (BAP US).  Once among the larger holdings in EM active portfolios, Credicorp Ltd (BAP US) has moved out of favour with EM fund managers.  In a slide that started in late 2019, the percentage of funds with investment in Credicorp Ltd (BAP US) fell a further 6.4% over the last 6-months.  Closures were led by Liontrust, Matthews and HSBC.


EM Fund Positioning:  All You Need to Know

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 bottom-fishing in Chinese education through <a href="https://www.smartkarma.com/entities/new-oriental-education-tech-grp-adr" target="_blank" data-smartkarma-citation="entities:531:1" rel="noopener">New Oriental Education (EDU US)</a>, Russian internet play <a href="https://www.smartkarma.com/entities/yandex-nv-a" target="_blank" data-smartkarma-citation="entities:414:1" rel="noopener">Yandex (YNDX US)</a>, Brazilian beverages giant <a href="https://www.smartkarma.com/entities/ambev-sa" target="_blank" data-smartkarma-citation="entities:24454:1" rel="noopener">Ambev SA (ABEV3 BZ)</a>, and a rebound call on <a href="https://www.smartkarma.com/entities/tencent-holdings-ltd" target="_blank" data-smartkarma-citation="entities:26:1" rel="noopener">Tencent (700 HK)</a>.<table class="table table-striped">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<tbody>
<tr>
<td style="text-align: center; width: 74.9062px;">rank</td>
<td style="width: 262.578px;">Name</td>
<td style="width: 185.766px;">Sector</td>
<td style="width: 147.797px;">Location</td>
<td style="text-align: center; width: 74.9531px;">prev.</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">1</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/tata-consultancy-svcs-ltd" target="_blank" data-smartkarma-citation="entities:18708:1" rel="noopener">Tata Consultancy Svcs (TCS IN)</a> </td>
<td style="width: 185.766px;">Information Technology</td>
<td style="width: 147.797px;">India</td>
<td style="text-align: center; width: 74.9531px;">19</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">2</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/shenzhou-intl-group-holdings-ltd" target="_blank" data-smartkarma-citation="entities:10415:1" rel="noopener">Shenzhou Intl Group Holdings (2313 HK)</a> </td>
<td style="width: 185.766px;">Consumer Discretionary</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">12</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">3</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/new-oriental-education-technology-group" target="_blank" data-smartkarma-citation="entities:32210:1" rel="noopener">New Oriental Education &amp; Technology Group (9901 HK)</a> </td>
<td style="width: 185.766px;">Consumer Discretionary</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">92</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">4</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/yandex-nv-a" target="_blank" data-smartkarma-citation="entities:414:1" rel="noopener">Yandex (YNDX US)</a> </td>
<td style="width: 185.766px;">Communication</td>
<td style="width: 147.797px;">Russian Federation</td>
<td style="text-align: center; width: 74.9531px;">88</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">5</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/china-resources-land-ltd" target="_blank" data-smartkarma-citation="entities:418:1" rel="noopener">China Resources Land (1109 HK)</a> </td>
<td style="width: 185.766px;">Real Estate</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">33</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">6</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/ambev-sa" target="_blank" data-smartkarma-citation="entities:24454:1" rel="noopener">Ambev SA (ABEV3 BZ)</a> </td>
<td style="width: 185.766px;">Consumer Staples</td>
<td style="width: 147.797px;">Brazil</td>
<td style="text-align: center; width: 74.9531px;">56</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">7</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/ncsoft-corp" target="_blank" data-smartkarma-citation="entities:5169:1" rel="noopener">NCSOFT Corp (036570 KS)</a> </td>
<td style="width: 185.766px;">Communication</td>
<td style="width: 147.797px;">Korea (South)</td>
<td style="text-align: center; width: 74.9531px;">18</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">8</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/tencent-holdings-ltd" target="_blank" data-smartkarma-citation="entities:26:1" rel="noopener">Tencent (700 HK)</a> </td>
<td style="width: 185.766px;">Communication</td>
<td style="width: 147.797px;">China</td>
<td style="text-align: center; width: 74.9531px;">61</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">9</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/hindustan-unilever-ltd" target="_blank" data-smartkarma-citation="entities:13828:1" rel="noopener">Hindustan Unilever (HUVR IN)</a> </td>
<td style="width: 185.766px;">Consumer Staples</td>
<td style="width: 147.797px;">India</td>
<td style="text-align: center; width: 74.9531px;">70</td>
</tr>
<tr>
<td style="text-align: center; width: 74.9062px;">10</td>
<td style="width: 262.578px;">
<a href="https://www.smartkarma.com/entities/maruti-suzuki-india-ltd" target="_blank" data-smartkarma-citation="entities:733:1" rel="noopener">Maruti Suzuki India (MSIL IN)</a> </td>
<td style="width: 185.766px;">Consumer Discretionary</td>
<td style="width: 147.797px;">India</td>
<td style="text-align: center; width: 74.9531px;">89</td>
</tr>
<tr><td colspan="5" style="text-align: left; width: 746px;">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 bottom-fishing in Chinese education through New Oriental Education (EDU US), Russian internet play Yandex (YNDX US), Brazilian beverages giant Ambev SA (ABEV3 BZ), and a rebound call on Tencent (700 HK).









rank
Name
Sector
Location
prev.


1

Tata Consultancy Svcs (TCS IN) 
Information Technology
India
19


2

Shenzhou Intl Group Holdings (2313 HK) 
Consumer Discretionary
China
12


3

New Oriental Education &amp; Technology Group (9901 HK) 
Consumer Discretionary
China
92


4

Yandex (YNDX US) 
Communication
Russian Federation
88


5

China Resources Land (1109 HK) 
Real Estate
China
33


6

Ambev SA (ABEV3 BZ) 
Consumer Staples
Brazil
56


7

NCSOFT Corp (036570 KS) 
Communication
Korea (South)
18


8

Tencent (700 HK) 
Communication
China
61


9

Hindustan Unilever (HUVR IN) 
Consumer Staples
India
70


10

Maruti Suzuki India (MSIL IN) 
Consumer Discretionary
India
89

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.

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 Russian energy plays <a href="https://www.smartkarma.com/entities/gazprom-pjsc-6a7231a9-d5e4-4da4-bf21-aa1b9d50fbf8" target="_blank" data-smartkarma-citation="entities:27578:1" rel="noopener">Gazprom PJSC (GAZP RM)</a> and <a href="https://www.smartkarma.com/entities/lukoil-pjsc" target="_blank" data-smartkarma-citation="entities:24141:1" rel="noopener">Lukoil PJSC (LKOD LI)</a>, Brazilian securities exchange <a href="https://www.smartkarma.com/entities/b3-sa-brasil-bolsa-balcao" target="_blank" data-smartkarma-citation="entities:24580:1" rel="noopener">B3 Sa - Brasil Bolsa Balcao (B3SA3 BZ)</a>, and <a href="https://www.smartkarma.com/entities/china-construction-bank-h" target="_blank" data-smartkarma-citation="entities:45:1" rel="noopener">China Construction Bank H (939 HK)</a>.<table class="table table-striped">
<colgroup>
<col>
<col>
<col>
<col>
<col>
</colgroup>
<tbody>
<tr>
<td style="text-align: center; width: 74.7344px;">rank</td>
<td style="width: 263.203px;">Name</td>
<td style="width: 185.312px;">Sector</td>
<td style="width: 147.938px;">Location</td>
<td style="text-align: center; width: 74.8125px;">prev.</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">1</td>
<td style="width: 263.203px;">
<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 style="width: 185.312px;">Information Technology</td>
<td style="width: 147.938px;">Korea (South)</td>
<td style="text-align: center; width: 74.8125px;">3</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">2</td>
<td style="width: 263.203px;">
<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 style="width: 185.312px;">Communication</td>
<td style="width: 147.938px;">China</td>
<td style="text-align: center; width: 74.8125px;">9</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">3</td>
<td style="width: 263.203px;">
<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 style="width: 185.312px;">Energy</td>
<td style="width: 147.938px;">China</td>
<td style="text-align: center; width: 74.8125px;">1</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">4</td>
<td style="width: 263.203px;">
<a href="https://www.smartkarma.com/entities/posco" target="_blank" data-smartkarma-citation="entities:208:1" rel="noopener">Posco (005490 KS)</a> </td>
<td style="width: 185.312px;">Materials</td>
<td style="width: 147.938px;">Korea (South)</td>
<td style="text-align: center; width: 74.8125px;">21</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">5</td>
<td style="width: 263.203px;">
<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 style="width: 185.312px;">Consumer Discretionary</td>
<td style="width: 147.938px;">Korea (South)</td>
<td style="text-align: center; width: 74.8125px;">4</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">6</td>
<td style="width: 263.203px;">
<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 style="width: 185.312px;">Financials</td>
<td style="width: 147.938px;">China</td>
<td style="text-align: center; width: 74.8125px;">5</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">7</td>
<td style="width: 263.203px;">
<a href="https://www.smartkarma.com/entities/gazprom-pjsc-6a7231a9-d5e4-4da4-bf21-aa1b9d50fbf8" target="_blank" data-smartkarma-citation="entities:27578:1" rel="noopener">Gazprom PJSC (GAZP RM)</a> </td>
<td style="width: 185.312px;">Energy</td>
<td style="width: 147.938px;">Russian Federation</td>
<td style="text-align: center; width: 74.8125px;">14</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">8</td>
<td style="width: 263.203px;">
<a href="https://www.smartkarma.com/entities/lukoil-pjsc" target="_blank" data-smartkarma-citation="entities:24141:1" rel="noopener">Lukoil PJSC (LKOD LI)</a> </td>
<td style="width: 185.312px;">Energy</td>
<td style="width: 147.938px;">Russian Federation</td>
<td style="text-align: center; width: 74.8125px;">32</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">9</td>
<td style="width: 263.203px;">
<a href="https://www.smartkarma.com/entities/b3-sa-brasil-bolsa-balcao" target="_blank" data-smartkarma-citation="entities:24580:1" rel="noopener">B3 Sa - Brasil Bolsa Balcao (B3SA3 BZ)</a> </td>
<td style="width: 185.312px;">Financials</td>
<td style="width: 147.938px;">Brazil</td>
<td style="text-align: center; width: 74.8125px;">70</td>
</tr>
<tr>
<td style="text-align: center; width: 74.7344px;">10</td>
<td style="width: 263.203px;">
<a href="https://www.smartkarma.com/entities/china-construction-bank-h" target="_blank" data-smartkarma-citation="entities:45:1" rel="noopener">China Construction Bank H (939 HK)</a> </td>
<td style="width: 185.312px;">Financials</td>
<td style="width: 147.938px;">China</td>
<td style="text-align: center; width: 74.8125px;">41</td>
</tr>
<tr><td colspan="5" style="text-align: left; width: 746px;">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 Russian energy plays Gazprom PJSC (GAZP RM) and Lukoil PJSC (LKOD LI), Brazilian securities exchange B3 Sa - Brasil Bolsa Balcao (B3SA3 BZ), and China Construction Bank H (939 HK).









rank
Name
Sector
Location
prev.


1

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


2

China Mobile (941 HK) 
Communication
China
9


3

CNOOC Ltd (883 HK) 
Energy
China
1


4

Posco (005490 KS) 
Materials
Korea (South)
21


5

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


6

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


7

Gazprom PJSC (GAZP RM) 
Energy
Russian Federation
14


8

Lukoil PJSC (LKOD LI) 
Energy
Russian Federation
32


9

B3 Sa - Brasil Bolsa Balcao (B3SA3 BZ) 
Financials
Brazil
70


10

China Construction Bank H (939 HK) 
Financials
China
41

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.

Alpha-Centric ESG Research

Kyle Rudden

Shortly after publishing <a href="https://www.smartkarma.com/insights/engine-versus-exxon-activist-hedge-fund-s-proxy-fight-win-reverberates-through-big-oil-boardrooms" target="_blank" data-smartkarma-citation="insights:52318" rel="noopener">Engine Versus Exxon: Activist Hedge Fund's Proxy Fight Win Reverberates Through "Big Oil" Boardrooms</a>, I decided that a follow-up report (such as this one) would be a good supplement. So, consider this an addendum of sorts to that first Insight.Whereas "Engine Versus Exxon" was a mostly qualitative discussion of recent events for Exxon and six other supermajors, this is a more data-driven look of a much larger universe of oil stocks; 50 in total, representing 24 countries (mainly in the Americas, Asia Pacific, and Europe).Together, they are a good proxy for the broader Energy sector – an aggregate market capitalisation of US$4.6 trillion, and responsible for an astounding 20.5 billion MtCO2e in emissions, about 65% of all energy-related greenhouse gas (GHG) emissions.This is a relatively focused Insight, covering four related areas:<ol>
<li>
Carbon Footprints: Scope 1, Scope 2, and Scope 3 emissions for each (ranked and sorted).</li>
<li>
Climate Policies: Where each stands vis-à-vis implementation of climate-related policies.</li>
<li>
Emissions Targets: Which companies are claiming to go net-zero emissions, and by when.</li>
<li>
Capital Investment: How future CapEx is (or is not) aligned with global warming targets.</li>
</ol>

Shortly after publishing Engine Versus Exxon: Activist Hedge Fund's Proxy Fight Win Reverberates Through "Big Oil" Boardrooms, I decided that a follow-up report (such as this one) would be a good supplement. So, consider this an addendum of sorts to that first Insight.Whereas "Engine Versus Exxon" was a mostly qualitative discussion of recent events for Exxon and six other supermajors, this is a more data-driven look of a much larger universe of oil stocks; 50 in total, representing 24 countries (mainly in the Americas, Asia Pacific, and Europe).Together, they are a good proxy for the broader Energy sector – an aggregate market capitalisation of US$4.6 trillion, and responsible for an astounding 20.5 billion MtCO2e in emissions, about 65% of all energy-related greenhouse gas (GHG) emissions.This is a relatively focused Insight, covering four related areas:

Carbon Footprints: Scope 1, Scope 2, and Scope 3 emissions for each (ranked and sorted).

Climate Policies: Where each stands vis-à-vis implementation of climate-related policies.

Emissions Targets: Which companies are claiming to go net-zero emissions, and by when.

Capital Investment: How future CapEx is (or is not) aligned with global warming targets.


Greenhouse Gas Emissions Profiles of 50 of the World's Largest Oil Companies

This insight provides the output, and an overview of, our proprietary GEM screening model, which takes a three-pronged approached to finding 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 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 <a href="https://www.smartkarma.com/entities/gazprom-pjsc" target="_blank" data-smartkarma-citation="entities:23749:1" rel="noopener">Gazprom PJSC (GAZP RX)</a>, <a href="https://www.smartkarma.com/entities/mediatek-inc" target="_blank" data-smartkarma-citation="entities:772:1" rel="noopener">Mediatek Inc (2454 TT)</a>, and <a href="https://www.smartkarma.com/entities/sberbank-of-russia-pjsc" target="_blank" data-smartkarma-citation="entities:24142:1" rel="noopener">Sberbank Of Russia Pjsc (SBER LI)</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>CNOOC LTD</td>
<td>Energy</td>
<td>China</td>
<td style="text-align: center;">25</td>
</tr>
<tr>
<td style="text-align: center;">2</td>
<td>GAZPROM</td>
<td>Energy</td>
<td>Russian Federation</td>
<td style="text-align: center;">89</td>
</tr>
<tr>
<td style="text-align: center;">3</td>
<td>CHINA RESOURCES LAND LTD</td>
<td>Real Estate</td>
<td>China</td>
<td style="text-align: center;">45</td>
</tr>
<tr>
<td style="text-align: center;">4</td>
<td>PTT NON-VOTING DR PCL</td>
<td>Energy</td>
<td>Thailand</td>
<td style="text-align: center;">39</td>
</tr>
<tr>
<td style="text-align: center;">5</td>
<td>MEDIATEK INC</td>
<td>Information Technology</td>
<td>Taiwan</td>
<td style="text-align: center;">81</td>
</tr>
<tr>
<td style="text-align: center;">6</td>
<td>PAO NOVATEK GDR</td>
<td>Energy</td>
<td>Russian Federation</td>
<td style="text-align: center;">48</td>
</tr>
<tr>
<td style="text-align: center;">7</td>
<td>GEELY AUTOMOBILE HOLDINGS LTD</td>
<td>Consumer Discretionary</td>
<td>China</td>
<td style="text-align: center;">2</td>
</tr>
<tr>
<td style="text-align: center;">8</td>
<td>SBERBANK ROSSII</td>
<td>Financials</td>
<td>Russian Federation</td>
<td style="text-align: center;">59</td>
</tr>
<tr>
<td style="text-align: center;">9</td>
<td>SAMSUNG ELECTRONICS LTD</td>
<td>Information Technology</td>
<td>Korea (South)</td>
<td style="text-align: center;">41</td>
</tr>
<tr>
<td style="text-align: center;">10</td>
<td>FIRSTRAND LTD</td>
<td>Financials</td>
<td>South Africa</td>
<td style="text-align: center;">30</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 proprietary GEM screening model, which takes a three-pronged approached to finding 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 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 Gazprom PJSC (GAZP RX), Mediatek Inc (2454 TT), and Sberbank Of Russia Pjsc (SBER LI).









rank
Name
Sector
Location
prev.


1
CNOOC LTD
Energy
China
25


2
GAZPROM
Energy
Russian Federation
89


3
CHINA RESOURCES LAND LTD
Real Estate
China
45


4
PTT NON-VOTING DR PCL
Energy
Thailand
39


5
MEDIATEK INC
Information Technology
Taiwan
81


6
PAO NOVATEK GDR
Energy
Russian Federation
48


7
GEELY AUTOMOBILE HOLDINGS LTD
Consumer Discretionary
China
2


8
SBERBANK ROSSII
Financials
Russian Federation
59


9
SAMSUNG ELECTRONICS LTD
Information Technology
Korea (South)
41


10
FIRSTRAND LTD
Financials
South Africa
30

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.

Russia

Gazprom PJSC operates gas pipeline systems, produces and explores gas, as well as transports high pressure gas in the Russian Federation and European countries. The Company also engaged in oil production, oil refining, gas storage, and electric and heat energy generation.

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Gazprom PJSC (GAZP RX)

EM Fund Positioning: All You Need to Know

In this analysis, we provide a detailed overview of investor positioning among 250 active Global Emerging Market funds with combined AUM of $500bn.

Steven Holden

Screening for GEMs: August 2021

This insight provides an overview of our, machine learning-driven, proprietary GEM screening model, based on a Multifactor model, a Deep Neural...

Wium Malan, CFA

Screening for GEMs: July 2021

This insight provides an overview of our, machine learning-driven, proprietary GEM screening model, based on a Multifactor model, a Deep Neural...

Wium Malan, CFA

Greenhouse Gas Emissions Profiles of 50 of the World's Largest Oil Companies

A quick overview of the carbon footprints and climate policies of 50 of the world's largest oil stocks (mainly Asia Pacific, Americas, and Europe...

Kyle Rudden

Screening for GEMs: May 2021

This insight provides an overview of our proprietary GEM screening model which is based on a multifactor screening model, a Deep Neural Network...

Wium Malan, CFA

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