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

<ul>
<li>The long-term decline in Thailand allocations among Asia Ex-Japan funds appears to be stabilising.</li>
<li>Over the past 6-months, Thailand weights have increased by 0.29%, with 6.5% of the funds in our analysis adding exposure, though managers remain underweight and underinvested.</li>
<li>CP ALL PCL is the most widely held stock, but recent fund activity has favoured Central Pattana Pub, Bangkok Dusit Medical Services and Asian Sea.</li>
</ul>


The long-term decline in Thailand allocations among Asia Ex-Japan funds appears to be stabilising.
Over the past 6-months, Thailand weights have increased by 0.29%, with 6.5% of the funds in our analysis adding exposure, though managers remain underweight and underinvested.
CP ALL PCL is the most widely held stock, but recent fund activity has favoured Central Pattana Pub, Bangkok Dusit Medical Services and Asian Sea.


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. 

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/tencent-holdings-ltd" target="_blank" data-smartkarma-citation="entities:26:1" rel="noopener">Tencent (700 HK)</a>, <a href="https://www.smartkarma.com/entities/netease" target="_blank" data-smartkarma-citation="entities:29030:1" rel="noopener">NetEase (9999 HK)</a>, <a href="https://www.smartkarma.com/entities/bilibili-inc-83a27302-cd7d-4e6a-a1de-bcf93eb022c3" target="_blank" data-smartkarma-citation="entities:32215:1" rel="noopener">Bilibili (9626 HK)</a>, <a href="https://www.smartkarma.com/entities/foxconn-technology-co-ltd" target="_blank" data-smartkarma-citation="entities:11950:1" rel="noopener">Foxconn Technology (2354 TT)</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:Tencent (700 HK), NetEase (9999 HK), Bilibili (9626 HK), Foxconn Technology (2354 TT)

Global Emerging Markets Weekly: 13 August 2021

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, 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.

Thailand

PTT Public Company Limited operates as an oil and gas company. The Company produces, transports, and sells natural gas, crude oil, lubricants, aviation and marine, and petrochemical products. PTT offers fleet cards, storage, electronic bill presentment, and other related services. PTT serves customers in Thailand.

Price Gauge
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PTT PCL (PTT TB)

The Thailand Turnaround

The long-term decline in Thailand allocations among Asia Ex-Japan funds appears to be stabilising. We analyse the shift in sentiment among investors.

Steven Holden

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

Global Emerging Markets Weekly: 13 August 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

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: June 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

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