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.

Thematic (Sector/Industry)

This insight provides a summary of the key news developments impacting the largest countries and constituents of the Global Emerging Markets equity indices. Companies mentioned this week:<a href="https://www.smartkarma.com/entities/didi-chuxing-inc" target="_blank" data-smartkarma-citation="entities:27467:1" rel="noopener">DiDi Chuxing (DIDI US)</a>, <a href="https://www.smartkarma.com/entities/lukoil-pjsc-2bfe0130-461e-4ff3-af12-93bec12e6325" target="_blank" data-smartkarma-citation="entities:27148:1" rel="noopener">LUKOIL (LKOH RM)</a>, <a href="https://www.smartkarma.com/entities/bytedance-technology" target="_blank" data-smartkarma-citation="entities:27936:1" rel="noopener">ByteDance (1460967D CH)</a>, <a href="https://www.smartkarma.com/entities/tencent-holdings-ltd" target="_blank" data-smartkarma-citation="entities:26:1" rel="noopener">Tencent (700 HK)</a>

This insight provides a summary of the key news developments impacting the largest countries and constituents of the Global Emerging Markets equity indices. Companies mentioned this week:DiDi Chuxing (DIDI US), LUKOIL (LKOH RM), ByteDance (1460967D CH), Tencent (700 HK)

Global Emerging Markets Weekly: 9 July 2021

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 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.

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

With Value funds making gains on Growth funds this year, where are EM Value managers positioned?Country: U/W China &amp; HK and India, O/W South Korea and Mexico.  Note also the overweight in Turkey, with 71% of the Value funds in our analysis positioned overweight versus the iShares MSCI Emerging Markets ETF.Sector:U/W Consumer Discretionary, Communication Services and Health Care.  O/W  Materials, Industrials and Financials.Read on for further data and analysis on Industry and Stock positioning.Analysis taken from our Emerging Markets Research, covering 242 active GEM funds with combined AUM of $500bn

With Value funds making gains on Growth funds this year, where are EM Value managers positioned?Country: U/W China &amp; HK and India, O/W South Korea and Mexico.  Note also the overweight in Turkey, with 71% of the Value funds in our analysis positioned overweight versus the iShares MSCI Emerging Markets ETF.Sector:U/W Consumer Discretionary, Communication Services and Health Care.  O/W  Materials, Industrials and Financials.Read on for further data and analysis on Industry and Stock positioning.Analysis taken from our Emerging Markets Research, covering 242 active GEM funds with combined AUM of $500bn

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

Russia is the largest country overweight among active Emerging Market funds.  The average holding weight of 4.11% represents an overweight of 1.04% above the iShares MSCI Emerging Markets ETF weight (cash adjusted).In the wake of new US sanctions and troops gathering on the Ukraine border, the key stock risks to active investors are overweight positions in <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>, <a href="https://www.smartkarma.com/entities/lukoil-pjsc-ea76a137-f664-4066-ba45-32e13c8b007e" target="_blank" data-smartkarma-citation="entities:28476:1" rel="noopener">LUKOIL PJSC (LUKOY US)</a> and <a href="https://www.smartkarma.com/entities/yandex-nv-a" target="_blank" data-smartkarma-citation="entities:414:1" rel="noopener">Yandex (YNDX US)</a>, with over 80% of the funds in our analysis holding one or more of these stocks.The 2014 annexation of Crimea provides a useful insight in to the behavior of active investors during a period of political uncertainty.  EM investors were quick to react, reducing Russia exposure by 4.26% over the course of the year and over a quarter of funds moving from overweight to underweight.  Whether this situation reaches the heights of 2014 remains to be seen, but do not underestimate an active manager's willingness to cut or reduce allocations when the investment case becomes muddied by political events.

Russia is the largest country overweight among active Emerging Market funds.  The average holding weight of 4.11% represents an overweight of 1.04% above the iShares MSCI Emerging Markets ETF weight (cash adjusted).In the wake of new US sanctions and troops gathering on the Ukraine border, the key stock risks to active investors are overweight positions in Sberbank Of Russia Pjsc (SBER LI), LUKOIL PJSC (LUKOY US) and Yandex (YNDX US), with over 80% of the funds in our analysis holding one or more of these stocks.The 2014 annexation of Crimea provides a useful insight in to the behavior of active investors during a period of political uncertainty.  EM investors were quick to react, reducing Russia exposure by 4.26% over the course of the year and over a quarter of funds moving from overweight to underweight.  Whether this situation reaches the heights of 2014 remains to be seen, but do not underestimate an active manager's willingness to cut or reduce allocations when the investment case becomes muddied by political events. 

Russia:  Overweight Risk as Ukraine Tensions Rise

Russia

LUKOIL PJSC explores for, produces, refines, transports, and markets oil and gas mainly from Western Siberia. The Company also manufactures petrochemicals, fuels, and other petroleum products. LUKOIL operates refineries, and gasoline filling stations in Russia and the United States. The Company transports oil through pipelines, and petroleum products with its fleet of ships.

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LUKOIL PJSC (LKOD LI)

Global Emerging Markets Weekly: 9 July 2021

This insight provides a summary of the key news developments impacting the largest countries and constituents of the Global Emerging Markets equity...

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

EM Value Fund Positioning

With Value funds making gains on Growth funds this year, where are active Emerging Market Value managers positioned across countries, sectors,...

Steven Holden

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

Russia: Overweight Risk as Ukraine Tensions Rise

Russia is the largest overweight in EM as Ukraine tensions rise. Sberbank, Lukoil and Yandex are the largest stock overweights and the key risks...

Steven Holden

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