Emerging Market Investing: Five Myths Dispelled

1. Generating emerging market alpha requires top-down analysis
   
   Traditional emerging market (EM) investing has long relied heavily on macro, top-down country analysis for making decisions. However, dynamism within each market means that company-specific, idiosyncratic fundamentals — management quality, competitive positioning, growth prospects etc. — increasingly have a greater impact on returns than macro trends. Indeed, a significant portion of return dispersion in EM appears to be idiosyncratic i.e. not explained by country or sector returns (Figure 1).

   Figure 1: EM return dispersion is idiosyncratic

   Problems loading this infographic? - Please click here

   Source: Man Numeric, as of December 2024.

2. Quantitative strategies can’t compete with the local presence that gives discretionary managers a competitive edge
   
   The belief that quantitative strategies are inherently disadvantaged compared to fundamental or discretionary managers in EM is outdated. Two decades ago, it was significantly easier to generate returns by leveraging top-down country analysis because there was higher level of dispersion in country returns. Today, however, that top-down approach is less effective and it’s no longer enough to just have a country view. Amid a convergence of country returns and dispersion patterns aligning more closely with those in developed markets, it’s important to have an informed view of each of the roughly 5,000 stocks in our EM investable universe to exploit idiosyncratic opportunities. As stock selection becomes increasingly important, some discretionary managers in EM have embraced that approach and generated strong track records. But quants can gain an edge with alternative sources of data, such as the digitalization of local news, e-commerce data, etc. This approach has helped quants to add diversification: The average excess return correlation of quants versus discretionary has been approximately 15% since the Great Financial Crisis, as in Figure 2.

   Figure 2. Quants have generated higher excess returns than discretionary managers in EM equity …

   Problems loading this infographic? - Please click here

   Source: eVestment, as of February 2025. Past performance is not indicative of future results.

   Figure 3. … with a lower tracking error

   Problems loading this infographic? - Please click here

   Source: eVestment, as of February 2025. Past performance is not indicative of future results.

3. EM data quality is significantly worse than developed markets data
   
   There have been major advances in data sourcing and analytics, enabling investors to access granular information about EM firms (Figure 3). Every day, billions of people in EM countries share details about everything from their health and finances via apps, websites and their phones. Quantitative processes use this flood of data to seek an edge.
4. Machine learning is a purely statistical black-box and cannot process local language data in EM
   
   Today’s machine learning models can incorporate a wide variety of fundamental data sources, allowing these strategies to uncover meaningful economic relationships and causal drivers of company performance. Historically, translation and nuances of local culture made textual data harder to parse in bulk, but now the combination of natural language processing, improved computing power, and greater experience in handling non-English data is yielding more promising results. For example, natural language processing models can scrape local news sources to extract sentiment data that are distinct from traditional survey gauges or anecdotal accounts, giving quants an important edge.

   Figure 4. EM offers a growing flood of data

   Problems loading this infographic? - Please click here

   Source: Neudata, Man Numeric, as of December 2024.

5. Quant models can’t handle geopolitical risk and related EM volatility
   
   Quantitative models can be responsive to heightened geopolitical risks through both nuanced alpha signals and risk management techniques, but the models don’t operate in isolation. Human portfolio managers can also play a critical role in overseeing model-driven strategies, bringing their market expertise and nuanced understanding of regional dynamics and risks to the table. By combining human oversight with advanced quantitative techniques, these approaches not only process vast datasets, but also can adapt to sudden shifts and uncertainties that arise from significant geopolitical events. The end result is a well-balanced, disciplined, systematic investment process that leverages both technological power and appropriate human insight to help augment the machine.

Risk Disclosures: Emerging market investments are subject to greater risks than investments in developed markets, including political instability, currency fluctuations, differing accounting standards, and economic volatility. Quantitative strategies may underperform in certain market conditions and are subject to model risk.