Asian Equities Analyst, Logistics & Transport, Tracking Traffic

Daniel Hellberg

Asian Equities Analyst, Logistics & Transport

Tracking Traffic

Focusing on Asian logistics, transportation, and travel companies

Equity Capital Markets

<ul>
<li>Estimated take rate up vs year ago and vs Q124, and EBITA margin turned +ive</li>
<li>However, revenue growth in core China market slowed considerably in Q224</li>
<li>Cash Flow and Liquidity both appear ample, little pressure to raise new funds</li>
</ul>


Estimated take rate up vs year ago and vs Q124, and EBITA margin turned +ive
However, revenue growth in core China market slowed considerably in Q224
Cash Flow and Liquidity both appear ample, little pressure to raise new funds


Didi Global Q224 Results: EBITA Margin Up | OpCF Still Strong | But Growth Slowed Noticeably

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>Russian overweight wiped out after initial phase of Russia drop.  China underweight contracts on the back of natural portfolio adjustments.</li>
<li>South Africa takes on new role as the top pure EM play in the region.  Saudi Arabia underweight increases.</li>
<li>Russian overweight causes underperformance of -0.64% for active managers compared to benchmark.  Saudi Arabia u/w is proving a costly position for active managers in the face of inflated oil prices.</li>
</ul>


Russian overweight wiped out after initial phase of Russia drop.  China underweight contracts on the back of natural portfolio adjustments.
South Africa takes on new role as the top pure EM play in the region.  Saudi Arabia underweight increases.
Russian overweight causes underperformance of -0.64% for active managers compared to benchmark.  Saudi Arabia u/w is proving a costly position for active managers in the face of inflated oil prices.


<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 nearly 4 years and implemented in its current state for nearly 2 years, the model has a solid real-world alpha-generating performance track record, discussed 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 nearly 4 years and implemented in its current state for nearly 2 years, the model has a solid real-world alpha-generating performance track record, discussed in detail below.


<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.


Netherlands

Nebius Group is a technology company providing infrastructure and services to AI builders globally. The group's core business, Nebius, is an AI-centric cloud platform built for AI workloads. Nebius Group serves customers worldwide.

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Nebius Group (NBIS US)

Didi Global Q224 Results: EBITA Margin Up | OpCF Still Strong | But Growth Slowed Noticeably

In this insight we review Q224 financial and operating performance of China's largest ridehailing firm, Didi Global. Despite slower top-line...

Tracking Traffic

EM Positioning After Russia’s Collapse

Russian overweight wiped out after initial phase of Russia drop. China underweight contracts on the back of natural portfolio adjustments.

Steven Holden

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: February 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: 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

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