Active investing in emerging markets

Gerardo Rodriguez| Gordon Fraser, CFA |May 17, 2019

Q&A: The new landscape in EM

Emerging markets (EM) are in the midst of a structural transformation. As many EM economies have moved up the income chain, the composition of their equity markets has shifted, first toward manufacturing and then to services, with technology one of the fastest-growing sectors.

Meanwhile, the continuing integration of China into the global financial order, highlighted by the rapidly increasing weight of A-shares in the MSCI Emerging Markets Index, means increased liquidity and enhanced investment opportunities for EM investors.

But, by some measures, capturing alpha is becoming increasingly complex. In the past, many fundamental and quantitative EM managers were able to harness investment factors to deliver above-benchmark returns. As adoption of factors has become more mainstream, there has been a natural erosion of their ability to deliver investment performance. See the Decreasing efficacy chart.

Against this backdrop of increasing opportunity coupled with greater complexity in generating alpha, we’ve asked senior BlackRock investors from our systematic and fundamental EM active equity teams to shed some light on how they attempt to deliver alpha today and what they think is in store for EM investors in the future.

  • Gerardo Rodriguez is an emerging markets portfolio manager for BlackRock’s Systematic Active Equities Team
  • Gordon Fraser is an emerging markets portfolio manager for BlackRock’s Fundamental Active Equities Team


Decreasing efficacy: Information ratio for style factors in emerging markets

Information ratio for style factors in emerging markets

Source: BlackRock, April 2019. 756-day trailing IR (daily returns); no t-costs. Averaged across Momentum (+); Value (+); Growth (+); Size (–); and Volatility (–) from Barra GEM2 risk model for firms in SAE EM investable universe. Data covers January 2013 through February 2019. For illustrative purposes only.

As investing in EM has grown both more complex and more competitive, how have your processes evolved?

Gerardo: When we first started investing in EM, almost all the data that was available to analyze came from the companies themselves, and it’s no secret that data is not always the most reliable. But in the past few years there has been an explosion in alternative data sources, so that we no longer have to rely exclusively on company information. This is true across developed and emerging markets, but given the rapid pace of adoption of digital technologies in the developing world, it’s really been a sea change for active management in EM.

There are many examples to cite—from using satellite imagery to track relative movement around manufacturing facilities to utilizing natural language processing to see what employees are saying about their employers on career websites—but one that I’d highlight as particularly relevant to EM is geolocation data. We can use this data to measure the patterns of consumer footprints for each of the companies in, say the retail or banking sector, and use that information as a leading indicator for which of those companies is likely to report greater sales.

Gordon: The biggest change that we’ve made in the past few years has been to increase our use of a proprietary macro framework, so that it’s now a core part of our investment process. Given the increasingly heterogeneous nature of today’s EM landscape, we believe that it is imperative to track where each country is in the economic cycle and to monitor movement through the cycle using four key macro indicators: external accounts, fixed income, liquidity supply and economic activity. This allows us to consistently move capital out of countries we believe are in a later stage (activity surge) and into others that are in an earlier stage (healing). See the Macro matters graphic.

Macro matters: Four stages of the emerging market cycle and key macro indicators

Four stages of the emerging market cycle and the key macro indicators.

Source: BlackRock, April 2019. For illustrative purposes only.


Developing countries are often at very different stages of the economic cycle and many variables drive returns of emerging market equities, but we believe the macro indicators we use enable us to track the cycle effectively. Not all indicators are equally significant across countries, and some indicators could be key at one stage but less so in another. We believe that understanding the cycle and direction countries are travelling can be instrumental in generating excess returns over an EM benchmark. We would rather be long a country with a bad current state and an improving macro picture than one with a good but deteriorating economy.

What’s your philosophy on managing factor exposures within your portfolios?

Gordon: We’ve made a very deliberate decision not to run persistent style biases that would tilt the portfolio too heavily in the direction of any single factor. Many EM managers self-identify as either value investors or growth investors, but that’s not the tactic we take. We don’t believe in restricting our opportunity set only to a certain set of stocks that are driven by specific underlying factors.

That said, we sometimes express style views in our portfolio when we believe they will be rewarded. But the core of our process is a true fundamental, bottom-up approach that’s focused on individual security selection, and we overlay the macro process that I just described on top of that.

Gerardo: Systematic investing has evolved significantly over the years. It started mainly with factor investing but it has moved beyond that to pure alpha strategies, that is, excess returns that cannot be explained by traditional risk models. Factors can still play a role in delivering outperformance—especially if you can adjust your exposures as different market environments support different factors—but the bottom line for us is that factors don’t play as big a role as they used to. Fortunately, the rise of alternative data has largely coincided with the decline in factor efficacy, so we’ve moved heavily in that direction.

Turning back to economic cycles, many investors are looking to make their portfolios more resilient, given where we are in the global cycle. Does high volatility in EM hinder overall portfolio resilience?

Gordon: Volatility is simply a fact of life as an emerging market investor. But we believe the right way to think about volatility is that it is a feature of the asset class, not a hindrance. When we see prices deviate from fundamentals due to increased market volatility, we view that as a prime opportunity for our fundamental, security-level research process to add potential alpha.

One way to approach volatility in EM is to think about what you are doing as investing in stocks, as well as investing in companies. Over the past decade nearly two-thirds of the stocks in the MSCI EM Index moved by at least 40% annually, according to MSCI data. Whatever your long-term view might be on a company whose stock moves that much in a year, that kind of volatility creates shorter-term opportunities—if a stock moves 40% a year, you’ve got an opportunity to realize a 40% return. At the end of the day, I think you need to accept and embrace the volatility that comes with investing in these markets, and you can’t be afraid to turn over a portion of your portfolio when shorter-term opportunities arise.

Gerardo: I completely agree that you need to be comfortable with volatility if you’re going to invest in EM, and for that reason EM isn’t going to be for everyone. But volatility and resiliency are two different things, and we believe that EM as an asset class is as resilient as it has ever been. Floating exchange rates and central bank autonomy are now the norm, and the ability of governments and companies to fund themselves in their own currency is a real game-changer. A more flexible macroeconomic arrangement has allowed for financial variables (FX, interest rates) to absorb external shocks, taking away some of the volatility from real economic variables and reducing significantly the risks of systemic financial crises, in our view.

Even if the risk of systemic crises has abated, aren’t individual country shocks still prevalent? How do you deal with them as an investor?

Gerardo: It’s true that there always seem to be negative headlines somewhere in EM, and we still see a fair amount of country-specific dislocations. As with single-stock volatility, this is simply a feature of the asset class that investors need to embrace. That said, we do spend a significant amount of time modeling how past geopolitical events like elections and armed conflicts have impacted markets. We can combine those insights with big-data analysis of upcoming events—think of elections or trade wars—to try to gain insights as to how these events are likely to unfold and how markets may react, so that we can position our portfolios accordingly.

Gordon: For us, the macro process that I described earlier really helps with what we sometimes call heart attack situations in specific countries. We use a dashboard with about 130 different indicators to track where each country is in the economic cycle, and this process can help us identify which countries are in that late-cycle danger zone and, hopefully, to get ahead of those heart-attack events. And this same data-intensive, macro-focused process allows us to continue to monitor countries that have experienced a major dislocation and can aid us in spotting an inflection point when things should start to improve.

We started this conversation by talking about how your processes have evolved in the recent past. What changes do you see in the future?

Gordon: One of the biggest things we’re working on right now is utilizing big data and artificial intelligence (AI) to test the validity of our fundamental investment theses. In order to do this in a very systematic way, we’ve restructured our team a bit. We now employ a data lead that is responsible for leveraging our in-house team of data scientists and AI researchers to complement our fundamental research. Essentially what we’re looking to do, wherever possible, is to use big data to either verify or refute our fundamental analysis, to give us an edge in predicting earnings.

To build on an example that Gerardo mentioned earlier, say we have a fundamentally positive view on the prospects for a specific retailer in the current quarter. Before we invest our clients’ money in that company, it would be great to know if geolocation data supports our thesis. To be clear, this type of analysis isn’t driving returns for our fundamental strategies yet, but looking ahead a year or two, I think it will become increasingly important.

If incorporating big data and AI is the next frontier for fundamental investing, what does the future of systematic investing look like?

Gerardo: We’re focusing a great deal of our time and energy on unsupervised machine learning to improve our country selection analysis1. We recently used this technique to analyze sixteen years of raw equity data and eleven years of raw fixed income data, with the objective of finding clusters of countries that have similar characteristics in terms of security returns and risks. See the Country cluster chart.

This exercise has given us two insights that could be important for active management. One is that China, despite being the largest component of emerging market indexes, is evolving in a direction that makes it difficult to cluster with other EM countries. If anything, it most resembles the U.S., in that the data are telling us that China is gradually becoming a standalone asset class. And as the weight of Chinese A-Shares continues to grow in benchmark indexes, we think the case for a standalone allocation to China is only going to grow.

Second is that the way countries cluster impacts how much alpha you may be able to capture by allocating among them. For example, say you’ve got a view that Brazil is overvalued, and Russia is undervalued, so you decide to shift money from the former to the latter. While it might sound like a move that could deliver a good deal of alpha if your thesis is correct, it turns out these two countries cluster together very closely, thus limiting the alpha potential of the investment decision as both countries are likely to move together. This work we’ve done on clustering is not part of our investment process yet, but I think it holds great promise for the future.

1 For an in-depth explanation of this work, see the forthcoming white paper Reconstructing Emerging and Developed Markets Using Hierarchical Clustering by Gerald Garvey and Ananth Madhavan.

Country cluster: Groupings based on unsupervised machine learning analysis of return data

Groupings based on unsupervised machine learning analysis of return data.

Source: BlackRock, April 2019. For illustrative purposes only.


Gerardo Rodriguez
Managing Director
Gerardo Rodriguez, Managing Director, is an emerging markets portfolio manager for the Systematic Active Equity (SAE) group.
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Gordon Fraser
Managing Director
Gordon Fraser, CFA, Managing Director, is a portfolio manager on the Global Emerging Markets Equities Team within the Fundamental Active Equity division.
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