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>Russia suffered a significant reallocation of capital during the Crimea Annexation in 2014.  Average weights fell by -3.36%, 7.9% of funds closed positions and investors shifted from overweight to underweight</li>
<li>EM investors were well positioned in Russia heading in to 2022, not perhaps as heavily exposed as pre-Crimea, but enough to significantly effect portfolio returns for many funds.</li>
<li>Given the scale of the Ukraine conflict and the uncertainty surrounding western sanctions, this doesn’t feel like the time for catching a falling knife.</li>
</ul>


Russia suffered a significant reallocation of capital during the Crimea Annexation in 2014.  Average weights fell by -3.36%, 7.9% of funds closed positions and investors shifted from overweight to underweight
EM investors were well positioned in Russia heading in to 2022, not perhaps as heavily exposed as pre-Crimea, but enough to significantly effect portfolio returns for many funds.
Given the scale of the Ukraine conflict and the uncertainty surrounding western sanctions, this doesn’t feel like the time for catching a falling knife.


Russia: Echoes of the Past. Crimea Analysis and Positioning Update

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

<ul>
<li>An overview (and output) of our machine learning-driven GEM screening model, which seeks to find the best medium-term long ideas within the top100 stocks in the MSCI GEM Index.</li>
<li>The model is based on 1) a quantitative multifactor screening model, enhanced by two AI Technology-based approaches; 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model.</li>
<li>Developed over the past 3 years, and implemented in its current state for 20 months, the model has a solid real-world alpha-generating performance track record, set out in detail below.</li>
</ul>


An overview (and output) of our machine learning-driven GEM screening model, which seeks to find the best medium-term long ideas within the top100 stocks in the MSCI GEM Index.
The model is based on 1) a quantitative multifactor screening model, enhanced by two AI Technology-based approaches; 2) a Deep Neural Network model, and 3) a Recurrent Neural Network model.
Developed over the past 3 years, and implemented in its current state for 20 months, the model has a solid real-world alpha-generating performance track record, set out in detail below.


<p>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. </p><p>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. </p><p>This model, which has been developed and tweaked over the past 3 years (and implemented in its current state for 17 months), has a solid real-world performance track record, which I set out in the detail below. </p><p>Notable new entrants to the top decile this month include the beaten-down Chinese tech names such as <a href="https://www.smartkarma.com/entities/xiaomi-corp" target="_blank" data-smartkarma-citation="entities:24409:1" rel="noopener">Xiaomi Corp (1810 HK)</a>, <a href="https://www.smartkarma.com/entities/alibaba-group" target="_blank" data-smartkarma-citation="entities:28434:1" rel="noopener">Alibaba Group (9988 HK)</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> , and <a href="https://www.smartkarma.com/entities/jd-com-inc" target="_blank" data-smartkarma-citation="entities:29043:1" rel="noopener">JD.com Inc. (9618 HK)</a> as well as two Brazilian large caps in <a href="https://www.smartkarma.com/entities/vale-sa-7721577f-6c6a-4536-a829-92551c2bcd7f" target="_blank" data-smartkarma-citation="entities:27725:1" rel="noopener">Vale SA (VALE3 BZ)</a> and <a href="https://www.smartkarma.com/entities/ambev-sa" target="_blank" data-smartkarma-citation="entities:24454:1" rel="noopener">Ambev SA (ABEV3 BZ)</a>.</p><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/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;">10</td>
</tr>
<tr>
<td style="text-align: center;">2</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;">5</td>
</tr>
<tr>
<td style="text-align: center;">3</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;">16</td>
</tr>
<tr>
<td style="text-align: center;">4</td>
<td>
<a href="https://www.smartkarma.com/entities/xiaomi-corp" target="_blank" data-smartkarma-citation="entities:24409:1" rel="noopener">Xiaomi Corp (1810 HK)</a> </td>
<td>Information Technology</td>
<td>China</td>
<td style="text-align: center;">91</td>
</tr>
<tr>
<td style="text-align: center;">5</td>
<td>
<a href="https://www.smartkarma.com/entities/alibaba-group" target="_blank" data-smartkarma-citation="entities:28434:1" rel="noopener">Alibaba Group (9988 HK)</a> </td>
<td>Consumer Discretionary</td>
<td>China</td>
<td style="text-align: center;">34</td>
</tr>
<tr>
<td style="text-align: center;">6</td>
<td>
<a href="https://www.smartkarma.com/entities/vale-sa-7721577f-6c6a-4536-a829-92551c2bcd7f" target="_blank" data-smartkarma-citation="entities:27725:1" rel="noopener">Vale SA (VALE3 BZ)</a> </td>
<td>Materials</td>
<td>Brazil</td>
<td style="text-align: center;">38</td>
</tr>
<tr>
<td style="text-align: center;">7</td>
<td>
<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>Consumer Staples</td>
<td>Brazil</td>
<td style="text-align: center;">35</td>
</tr>
<tr>
<td style="text-align: center;">8</td>
<td>
<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>Communication</td>
<td>China</td>
<td style="text-align: center;">43</td>
</tr>
<tr>
<td style="text-align: center;">9</td>
<td>
<a href="https://www.smartkarma.com/entities/ping-an-insurance-grp-co-of-china-ltd" target="_blank" data-smartkarma-citation="entities:12880:1" rel="noopener">Ping An Insurance (H) (2318 HK)</a> </td>
<td>Financials</td>
<td>China</td>
<td style="text-align: center;">23</td>
</tr>
<tr>
<td style="text-align: center;">10</td>
<td>
<a href="https://www.smartkarma.com/entities/jd-com-inc" target="_blank" data-smartkarma-citation="entities:29043:1" rel="noopener">JD.com Inc. (9618 HK)</a> </td>
<td>Consumer Discretionary</td>
<td>China</td>
<td style="text-align: center;">79</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><p><em>As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions. </em></p>

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 3 years (and implemented in its current state for 17 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 the beaten-down Chinese tech names such as Xiaomi Corp (1810 HK), Alibaba Group (9988 HK), Tencent (700 HK) , and JD.com Inc. (9618 HK) as well as two Brazilian large caps in Vale SA (VALE3 BZ) and Ambev SA (ABEV3 BZ).









rank
Name
Sector
Location
prev.


1

New Oriental Education &amp; Technology Group (9901 HK) 
Consumer Discretionary
China
10


2

NetEase (9999 HK) 
Communication
China
5


3

Samsung Electronics (005930 KS) 
Information Technology
Korea (South)
16


4

Xiaomi Corp (1810 HK) 
Information Technology
China
91


5

Alibaba Group (9988 HK) 
Consumer Discretionary
China
34


6

Vale SA (VALE3 BZ) 
Materials
Brazil
38


7

Ambev SA (ABEV3 BZ) 
Consumer Staples
Brazil
35


8

Ambev SA (ABEV3 BZ) 
Communication
China
43


9

Ping An Insurance (H) (2318 HK) 
Financials
China
23


10

JD.com Inc. (9618 HK) 
Consumer Discretionary
China
79

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. 

<p>The last decade has seen EMEA allocations in active GEM portfolios drift ever lower, with Asia becoming the dominant weight.  However, the recent rhetoric from China has seen Asian allocations fall from their peak and EMEA allocations rally from their lows.  Average weights in EMEA stocks stand at 13.72%, marginally overweight the iShares MSCI Emerging Markets ETF by 0.24%, with just under half of the active managers in our analysis overweight. </p><ul>
<li>Over the course of the last decade, exposure to each EMEA sector has either fallen dramatically or at best stayed largely unchanged.  Those that have seen a big decline in investment are led by EMEA Industrials and Health Care, with over 40% of investors closing exposure.  </li>
<li>The largest sector exposure, EMEA Financials allocations sit towards the lower-end of their historical range.  Top holdings are <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> and <a href="https://www.smartkarma.com/entities/otp-bank-nyrt" target="_blank" data-smartkarma-citation="entities:28806:1" rel="noopener">OTP Bank Nyrt (OTP HB)</a> with 59% and 31% of funds invested respectively.  <a href="https://www.smartkarma.com/entities/tcs-group-holding-plc" target="_blank" data-smartkarma-citation="entities:28148:1" rel="noopener">TCS Group Holding PLC (TCS LI)</a> and <a href="https://www.smartkarma.com/entities/capitec-bank-holdings-ltd" target="_blank" data-smartkarma-citation="entities:27628:1" rel="noopener">Capitec Bank Holdings Ltd (CPI SJ)</a> saw ownership levels increase the most over the last 6-months.</li>
<li>Consumer Discretionary weights hit another all-time high this month of 1.46%, representing a 1.04% overweight versus the iShares MSCI EM ETF.  Underpinning the overweight are out-of-benchmark holdings in <a href="https://www.smartkarma.com/entities/prosus-nv" target="_blank" data-smartkarma-citation="entities:28104:1" rel="noopener">Prosus (PRX NA)</a> and Fix Price Group, though in general stocks in the sector are not widely held.  Funds with big overweights are led by Comgest's GEM Promising Companies Fund (8.98%) and Van Eck's Emerging Markets Fund (7.21%).  </li>
<li>With active ownership declining throughout 2020, <a href="https://www.smartkarma.com/entities/gazprom-pjsc" target="_blank" data-smartkarma-citation="entities:23749:1" rel="noopener">Gazprom PJSC (GAZP RX)</a> has finally been the recipient of some inflows.  Over the last 6-months, 2% of funds added Gazprom to their portfolios with a combined $217m in inflows.  Leading those were Fidelity (2.7%), Virtus Allianz (2.67%) and JP Morgan ($64m).</li>
</ul><p>The analysis looks in detail at all sectors in the EMEA region, together with some of the key stocks that underpin investment in the region.</p><p><em>Analysis taken from our GEM fund research, covering 250 active GEM strategies with combined AUM of $500bn</em></p>

The last decade has seen EMEA allocations in active GEM portfolios drift ever lower, with Asia becoming the dominant weight.  However, the recent rhetoric from China has seen Asian allocations fall from their peak and EMEA allocations rally from their lows.  Average weights in EMEA stocks stand at 13.72%, marginally overweight the iShares MSCI Emerging Markets ETF by 0.24%, with just under half of the active managers in our analysis overweight. 
Over the course of the last decade, exposure to each EMEA sector has either fallen dramatically or at best stayed largely unchanged.  Those that have seen a big decline in investment are led by EMEA Industrials and Health Care, with over 40% of investors closing exposure.  
The largest sector exposure, EMEA Financials allocations sit towards the lower-end of their historical range.  Top holdings are Sberbank Of Russia Pjsc (SBER LI) and OTP Bank Nyrt (OTP HB) with 59% and 31% of funds invested respectively.  TCS Group Holding PLC (TCS LI) and Capitec Bank Holdings Ltd (CPI SJ) saw ownership levels increase the most over the last 6-months.
Consumer Discretionary weights hit another all-time high this month of 1.46%, representing a 1.04% overweight versus the iShares MSCI EM ETF.  Underpinning the overweight are out-of-benchmark holdings in Prosus (PRX NA) and Fix Price Group, though in general stocks in the sector are not widely held.  Funds with big overweights are led by Comgest's GEM Promising Companies Fund (8.98%) and Van Eck's Emerging Markets Fund (7.21%).  
With active ownership declining throughout 2020, Gazprom PJSC (GAZP RX) has finally been the recipient of some inflows.  Over the last 6-months, 2% of funds added Gazprom to their portfolios with a combined $217m in inflows.  Leading those were Fidelity (2.7%), Virtus Allianz (2.67%) and JP Morgan ($64m).
The analysis looks in detail at all sectors in the EMEA region, together with some of the key stocks that underpin investment in the region.Analysis taken from our GEM fund research, covering 250 active GEM strategies with combined AUM of $500bn

EMEA Sectors Deep-Dive.  Active Positioning in the EMEA Region

<p>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. </p><p>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. </p><p>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. </p><p>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>.</p><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;"><em>Source: FactSet financial data and analytics, my estimates and calculations, Tensorflow, Keras, Google Colaboratory, pandas</em></td></tr>
</tbody>
</table><p><em>As usual, please feel free to reach out for the full output table, any questions, clarifications needed or suggestions. </em></p>

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. 

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
Market Cap (USDm)	0.00
P/E	0.00
Dividend (Yield)	0.00

LUKOIL PJSC (LKOD LI)

Russia: Echoes of the Past. Crimea Analysis and Positioning Update

Russia suffered a significant reallocation of capital during the Crimea Annexation of 2014. It took 2 years for sentiment to recover. This time...

Steven Holden

Screening for GEMs: January 2022

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: October 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

EMEA Sectors Deep-Dive. Active Positioning in the EMEA Region

The last decade has seen EMEA weights drift ever lower. We look in detail at all sectors in the EMEA region, together with some of the key stocks...

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

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