Emerging markets are a popular choice in investors’ portfolios – they can be resilient to global economic shocks, they can bring return in the form of compounded income, and they can offer true alphas, if you know what you’re doing. It is important, therefore, to look closer at each opportunity. Saeed Amen, Founder of Cuemacro, argues that the term “emerging markets” is too broad and from a quant perspective, some should be considered as “developed markets”. In this article, he explores the key differences between these terms and how they can impact your investment strategy.
In recent decades, emerging markets have become more important for investors’ portfolios. These days, it is not purely discretionary investors who have been looking at emerging markets, but also quants. Indeed, one of the biggest “quant” news stories this year revolved around a strategy which traded Indian options (see Jane Street’s $1 Billion Trade Puts Spotlight on Indian Options 22 Apr 2024).
From a quant perspective, what are the differences for modelling emerging markets, compared to developed markets? The first consideration is that whilst many countries might come under the official emerging market definition (say if we look at those countries which come under various well known EM equity indices), they are very different. Typically, one way to split them is by regions which are in the same time zone, with Latin America and Asia, as well as Eastern Europe, Middle East and Africa.
However, even within regions there can be large differences, in Asia, South Korea and Singapore are still classified as emerging markets, but in practice, by many metrics such as GDP per capita, they more resemble developed markets. This contrasts to many other Asian countries whose GDP per capita are considerably lower than developed markets at this stage (but are catching up).
The lifeblood of any quantitative models is data. On a broad basis, emerging markets tend to have a lot less data than a typical developed market. Of course, countries such as Brazil are very data rich, but they are more of an exception. Less data makes them more difficult to model, but of course other investors will have similar constraints.
There tend to be fewer players in emerging markets compared to developed markets. Just look at the survey of any major data release such as CPI in a developed market compared to that of an emerging market, and typically, you have fewer forecasters for forecasts.
Liquidity tends to be less in EM, hence, typically a quant will need trading strategies which do not trade as often, to reduce the impact of greater transaction costs. Generally speaking, the capacity of strategies in EM are also likely to be lower as a result.
We’ve seen already there are many differences between EM and DM. Hence, we need to be careful when trying to transfer strategies from DM to EM. Some strategies might be just as applicable in EM as they are in DM (one example is trend following, albeit with tweaks for liquidity). In some instances, strategies which once worked in DM, but are no longer profitable given more efficient markets, may continue to work in EM.
However, other strategies might be less easy to copy over. Indeed, there are some strategies that are unlikely to work in EM because of different dynamics. One example is using interest rate differentials to trade DMFX. Typically, higher inflation pushes yields higher in DM, and this can often result in a stronger DM currency, given the market’s expectations of central bank credibility. However, in EM, this relationship can often breakdown. Indeed, in EM, investors tend to dump local EM bonds (yields higher) and the EM currency at the same time during periods of risk aversion.
Emerging markets can present an interesting opportunity for quants, but the differing levels of data and also less liquidity, means that additional thought need to be put in, when trying to model them. A simple copy and paste approach from DM is unlikely to work in practice.
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