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 a host of energy plays (<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/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/ptt-pcl" target="_blank" data-smartkarma-citation="entities:197:1" rel="noopener">PTT PCL (PTT TB)</a>,  and <a href="https://www.smartkarma.com/entities/cnooc-ltd" target="_blank" data-smartkarma-citation="entities:175:1" rel="noopener">CNOOC Ltd (883 HK)</a>) as well as a return for South African banking group <a href="https://www.smartkarma.com/entities/firstrand-ltd" target="_blank" data-smartkarma-citation="entities:24971:1" rel="noopener">FirstRand Ltd (FSR SJ)</a>, and a recovery call for Chinese online gaming powerhouse <a href="https://www.smartkarma.com/entities/netease" target="_blank" data-smartkarma-citation="entities:29030:1" rel="noopener">NetEase (9999 HK)</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/gazprom-pjsc-6a7231a9-d5e4-4da4-bf21-aa1b9d50fbf8" target="_blank" data-smartkarma-citation="entities:27578:1" rel="noopener">Gazprom PJSC (GAZP RM)</a> </td>
<td>Energy</td>
<td>Russian Federation</td>
<td style="text-align: center;">12</td>
</tr>
<tr>
<td style="text-align: center;">2</td>
<td>
<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> </td>
<td>Energy</td>
<td>Russian Federation</td>
<td style="text-align: center;">28</td>
</tr>
<tr>
<td style="text-align: center;">3</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;">30</td>
</tr>
<tr>
<td style="text-align: center;">4</td>
<td>
<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>Real Estate</td>
<td>China</td>
<td style="text-align: center;">5</td>
</tr>
<tr>
<td style="text-align: center;">5</td>
<td>
<a href="https://www.smartkarma.com/entities/netease" target="_blank" data-smartkarma-citation="entities:29030:1" rel="noopener">NetEase (9999 HK)</a> </td>
<td>Communication</td>
<td>China</td>
<td style="text-align: center;">47</td>
</tr>
<tr>
<td style="text-align: center;">6</td>
<td>
<a href="https://www.smartkarma.com/entities/firstrand-ltd" target="_blank" data-smartkarma-citation="entities:24971:1" rel="noopener">FirstRand Ltd (FSR SJ)</a> </td>
<td>Financials</td>
<td>South Africa</td>
<td style="text-align: center;">68</td>
</tr>
<tr>
<td style="text-align: center;">7</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;">25</td>
</tr>
<tr>
<td style="text-align: center;">8</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;">51</td>
</tr>
<tr>
<td style="text-align: center;">9</td>
<td>
<a href="https://www.smartkarma.com/entities/mmc-norilsk-nickel-pjsc-dcb09449-eb24-4747-b1c9-77fbf35e8398" target="_blank" data-smartkarma-citation="entities:33242:1" rel="noopener">MMC Norilsk Nickel PJSC (GMKN RX)</a> </td>
<td>Materials</td>
<td>Russian Federation</td>
<td style="text-align: center;">22</td>
</tr>
<tr>
<td style="text-align: center;">10</td>
<td>
<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>Consumer Discretionary</td>
<td>China</td>
<td style="text-align: center;">3</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 a host of energy plays (Gazprom PJSC (GAZP RX), LUKOIL (LKOH RM), PTT PCL (PTT TB),  and CNOOC Ltd (883 HK)) as well as a return for South African banking group FirstRand Ltd (FSR SJ), and a recovery call for Chinese online gaming powerhouse NetEase (9999 HK).









rank
Name
Sector
Location
prev.


1

Gazprom PJSC (GAZP RM) 
Energy
Russian Federation
12


2

LUKOIL (LKOH RM) 
Energy
Russian Federation
28


3

PTT PCL (PTT TB) 
Energy
Thailand
30


4

China Resources Land (1109 HK) 
Real Estate
China
5


5

NetEase (9999 HK) 
Communication
China
47


6

FirstRand Ltd (FSR SJ) 
Financials
South Africa
68


7

CNOOC Ltd (883 HK) 
Energy
China
25


8

Bank Of China Ltd (H) (3988 HK) 
Financials
China
51


9

MMC Norilsk Nickel PJSC (GMKN RX) 
Materials
Russian Federation
22


10

New Oriental Education &amp; Technology Group (9901 HK) 
Consumer Discretionary
China
3

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.

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

EM Energy allocations are at record lows.  A big exodus in the first half of 2020 pushed allocations to their lowest levels since our records start in 2008.   The aggressive scaling back of positioning had a dramatic effect on prices, with the MSCI EM Energy sector suffering one of the worst 12 month periods of underperformance in nearly 2 decades.  But the ownership picture is beginning to stabilize.  On a stock level, <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> appears to be the GOAT, with its backing of almost half of the funds in our analysis, well spread across the style spectrum in addition to being a net overweight.  <a href="https://www.smartkarma.com/entities/reliance-industries-ltd" target="_blank" data-smartkarma-citation="entities:198:1" rel="noopener"> Reliance Industries (RIL IN)</a> is also a high conviction stock, held by fewer funds than Lukoil but in greater size.  It also represents a significant underweight to many EM investors.  The most depressed stock of the larger holdings is <a href="https://www.smartkarma.com/entities/cnooc-ltd" target="_blank" data-smartkarma-citation="entities:175:1" rel="noopener">CNOOC Ltd (883 HK)</a> , once held by over 60% of the funds in our analysis, now by just 24%.Analysis taken from our EM research, covering the collective allocations of 242 Global Emerging Market active funds, with combined AUM of $460bn

EM Energy allocations are at record lows.  A big exodus in the first half of 2020 pushed allocations to their lowest levels since our records start in 2008.   The aggressive scaling back of positioning had a dramatic effect on prices, with the MSCI EM Energy sector suffering one of the worst 12 month periods of underperformance in nearly 2 decades.  But the ownership picture is beginning to stabilize.  On a stock level, LUKOIL (LKOH RM) appears to be the GOAT, with its backing of almost half of the funds in our analysis, well spread across the style spectrum in addition to being a net overweight.   Reliance Industries (RIL IN) is also a high conviction stock, held by fewer funds than Lukoil but in greater size.  It also represents a significant underweight to many EM investors.  The most depressed stock of the larger holdings is CNOOC Ltd (883 HK) , once held by over 60% of the funds in our analysis, now by just 24%.Analysis taken from our EM research, covering the collective allocations of 242 Global Emerging Market active funds, with combined AUM of $460bn

EM Energy: Big Underperformance - Positioning at Record Lows, but Stabilizing.

Equity Bottom-Up

<ul>
<li>Ownership levels in Magnit are depressed. Today's average weights and funds held % are at multi-year lows.</li>
<li>Recent sentiment looks positive when compared to other EM stocks, though this is partly a function of low ownership rather then any large scale buying.</li>
<li>Although lightly owned at present, it is a well known stock having being owned by 151 of the 224 in our analysis.</li>
<li>Max ever combined AUM from the funds in our analysis is ~20x the current level, most funds are at their lowest ever levels of ownership.</li>
<li>Prices are highly correlated to EM fund ownership.  Higher ownership levels are likely to drive prices higher.</li>
<li>Given Magnit's strong performance in 2020 relative to broader EM and other favoured Russian stocks, it could be appearing back on the radar of active EM managers. If it does, expect performance to follow.</li>
</ul>


Ownership levels in Magnit are depressed. Today's average weights and funds held % are at multi-year lows.
Recent sentiment looks positive when compared to other EM stocks, though this is partly a function of low ownership rather then any large scale buying.
Although lightly owned at present, it is a well known stock having being owned by 151 of the 224 in our analysis.
Max ever combined AUM from the funds in our analysis is ~20x the current level, most funds are at their lowest ever levels of ownership.
Prices are highly correlated to EM fund ownership.  Higher ownership levels are likely to drive prices higher.
Given Magnit's strong performance in 2020 relative to broader EM and other favoured Russian stocks, it could be appearing back on the radar of active EM managers. If it does, expect performance to follow.


Emerging Markets Cross-Asset Strategist, Emvest Partners

Guillaume Coutant

Emerging Markets Cross-Asset Strategist

Technical Analysis

Russian stocks have been consolidating since the beginning of 2017, thus lagging other emerging countries by 23% on a relative basis.Oil has experienced erratic moves despite production cuts agreements, other commodities suffered from a slowing Chinese demand, and geopolitical turmoil in the Middle East put the country in a delicate position and feed investors concerns.However, Russian banks have been quite resilient, benefiting from the financial sector recovery as a whole and improved price to…

Russian stocks have been consolidating since the beginning of 2017, thus lagging other emerging countries by 23% on a relative basis.Oil has experienced erratic moves despite production cuts agreements, other commodities suffered from a slowing Chinese demand, and geopolitical turmoil in the Middle East put the country in a delicate position and feed investors concerns.However, Russian banks have been quite resilient, benefiting from the financial sector recovery as a whole and improved price to…

Lukoil & Sberbank : Two Russian Stocks, Two Different Stories

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.

Price Gauge
52-week Range	0.00 - 0.00
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LUKOIL PJSC (LKOD LI)

Screening for GEMs: September 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

EM Energy: Big Underperformance - Positioning at Record Lows, but Stabilizing.

EM Energy exodus drives worst performance of EM Energy sector in over 15 years. EM active funds are now at their lowest ever exposure to EM Energy...

Steven Holden

Magnit PJSC : Mind the Gap.

Ownership levels in Magnit are depressed. Today's average weights and funds held % are at multi-year lows. Recent sentiment looks positive when...

Steven Holden

Lukoil & Sberbank : Two Russian Stocks, Two Different Stories

Russian stocks have been consolidating since the beginning of 2017, thus lagging other emerging countries by 23% on a relative basis.Oil has...

Guillaume Coutant

Price Gauge

52-week Range

Market Cap (USDm)

P/E

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