An EM-phatic Advantage for Quantitative Investors?

Introduction

BRICS. GEM.¹ Bimchip.² (The latter one, we had to look up. Stands for Brazil, India, Mexico, Chile, Indonesia and Peru.)

This alphabet soup when it comes to emerging markets (‘EM’) is becoming increasingly redundant. Any attempt to group a subset of EM countries together as similar to one another is perhaps missing the fact that the differences in companies across EM countries are highly pronounced.

In today’s market, our view is that compared with history, it is much more difficult to generate equity outperformance by getting a handful of top-down country calls correct. Indeed, what is clear to us is that EM equity investing must go beyond the traditional – and often over-simplified – model of investing in a subset of countries. The c5,000 stocks in the EM universe are affected by idiosyncratic micro and macro factors alike. The solution? Applying the large-scale analysis that covers the full range of EM countries and that is only available via a robust quantitative investment process.

In addition, the data revolution in emerging markets is now putting the best quantitative investors on at least the same footing as, and arguably on more solid ground, than local professionals.

Finally, a quantitative approach has the potential to reward investors as it aims to remove emotional biases from decision-making, which can be particularly important in volatile markets like EM.

An Alphabet View Is No Longer Enough in EM Equity

Two decades ago, it would have been much easier to get a top-down call in a subset of countries correct in emerging markets and outperform the index. However, aside from the recent bump triggered by the collapse of the Russian market, the country dispersion in EM has narrowed significantly and is in fact approaching that of developed markets (Figure 1). It is no longer enough just to have a country view in EM.

Figure 1. Country Dispersion in EM Has Narrowed Significantly

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Source: Bloomberg; between 31 January 1991 and 31 March 2023.

Indeed, most of the return dispersion in EM is idiosyncratic i.e. not explained by country or sector returns (Figure 2).

Figure 2. EM Return Dispersion is Idiosyncratic

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Source: Man Numeric; as of 1 March 2023.

It is thus as important as ever to have an informed view of each of the c.5,000 stocks in the EM universe.

While it is almost impossible for a discretionary manager to have an informed opinion on hundreds or thousands of stocks, this is standard for a quantitative manager. We would argue that quantitative processes inherently provide this breadth and diversification given the disciplined, repeatable investment approach focusing on a variety of complementary alpha drivers and featuring a heightened focus on risk control.

The Data Revolution in Emerging Markets

The breadth and depth of data available in EM are increasing by the day. Lagging developed markets a few years ago the breadth of EM datasets have been increasing steadily, with only about 35% of alternative datasets³ not having any EM coverage, and some Asian markets now arguably ahead of many developed markets when it comes to consumer-related data.

The widespread deployment of technology is the primary driver, with data on consumers expanding in particular. This reflects the acceleration of internet-based activities, as data come not just from social media but also from the digitisation of local news, financial services and ecommerce in general (Figure 3). Users of this data can maintain their edge by using their modelling expertise and market knowledge to overcome holes and gaps in information available to investors, to adjust methodologies as needed, and to conduct robust backtests that take into account any integrity issues.

Figure 3. Data Is Coming From a Variety of Sources

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Source: Neudata, Man Numeric; as of December 2022.

Let’s take the China-A market as an example.

Despite gradual improvements since MSCI index inclusion, IBES data – which relies primarily on global brokers – has relatively limited coverage for China-A shares, particularly for small- and mid-cap stocks. Locally, however, China has a large domestic brokerage community (c.130 brokerage firms) that provides more extensive coverage of the onshore equity market. These local estimates significantly improve both breadth (i.e. number of stocks covered) and depth (i.e. number of covered analysts for a particular stock), contributing to stronger estimate revision and estimates-based valuation models (Figure 4).

While the space continues to evolve, today NLP can also be applied when scraping local news sources to extract sentiment data that are distinct from traditional survey gauges or anecdotal accounts, providing quantitative investors with an important edge.

Figure 4. China’s Domestic Brokerage Community Outsizes International One in Terms of Breadth and Depth of Stocks Covered

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Source: IBES, Suntime, Man Numeric; as of 31 December 2022.

Removing the Emotional Bias

Take the fourth quarter of 2021 as an example. Russia experienced negative returns during that time period, which were then exacerbated in the first quarter of 2022 as the country invaded Ukraine. For a quantitative model that includes valuation, the outcome could easily have been to go overweight Russia because the country became cheaper (local share prices went down without – yet – a material deterioration in their fundamentals, Figure 5), rather than focusing on the geopolitical events, which would require human oversight.

Figure 5. MSCI Russia Index – Price-to-Earnings Ratio

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Source: Bloomberg; as of 8 March 2022.

Quant Versus Discretionary in EM Equity Investing

The final point we will make here is that when it comes to EM equity investing, quant managers compare favourably to discretionary managers.

Figure 6. Quants Have Historically Provided a Higher Return in EM Equity Than Discretionary Managers…

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Source: eVestment; as of December 2022.
Note: Using the eVestment emerging-markets large cap core universe, the median monthly manager return of the quantitative and discretionary subgroups was used.

Figure 7. …With a Lower Tracking Error

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Source: eVestment; as of December 2022.

Conclusion

Ever since Jim O’Neill coined the terms Brics in 2001, investors have been quick to group EM countries together. However, our view is that this alphabet soup when it comes to emerging markets is becoming increasingly redundant.

As EMs become more driven by idiosyncratic, bottom-up opportunities; and as the breadth – and depth – of available data in the region increases, we believe it has never been more important to have an informed view of each of the c.5,000 stocks in the EM universe.

While it is almost impossible for a discretionary manager to have an informed opinion on hundreds or thousands of stocks, this is standard for a quantitative manager. We would argue that quantitative processes inherently provide both this level of insight and diversification given the disciplined, repeatable investment approach focusing on a variety of complementary alpha drivers and featuring a heightened focus on risk control.

In addition, we believe a quantitative approach has the potential to reward investors as it aims to remove emotional bias from decision-making, especially as it pertains to volatile markets like EM.

1. i.e. Global Emerging Markets.
2. Emerging market investors: drop a Bric and pick up a Bimchip | Financial Times (ft.com)
3. Based on datasets available through data scouting firm Neudata.