Emerging Markets Solutions

Overview

These models are based on the MSCI EM Index of Emerging Markets country indices. There are 23 countries in the EAFE Index of which we use 20. We have developed a solution which has been able to differentiate performance between countries over a one to two year horizon.  The model ranks countries based on a combination of macroeconomic, fundamental and momentum factors.  There is a consistent and sequential relationship between our model rankings and country performance.

The Big Picture

The graphic below plots each country’s performance ranking for each of the past 21 years, shaded by top and bottom third. Casual observation of the graphic below reveals that countries take turns to outperform, with a small pattern of momentum persistence; countries in the top or bottom 20% have a 24% probability of remaining in that bracket the following year. The columns on right hand side show each country’s 21 year average ranking and average annual returns. The standard deviation of the 21 year ranking averages is only 0.89 implying that over the long term, rankings are generally similar. The average annual 21 year returns for all countries over the 21 year period was 11.8% with a standard deviation of 6.73%.

The Opportunity Set

The standard deviation of annual returns between countries averaged 29% per year over the past 21 years. It spiked up during the tech bubble years of the late 1990’s but has since been more stable at about 20% average per year. This represents significant opportunity for a framework that can identify the winners and losers from year to year.

Factors affecting country allocation

The main theme behind our thesis is to identify countries which are cheap on a Purchasing Power Parity basis and have growth momentum. In an increasing globalized world, capital will flow to countries that offer a competitive advantage in resource and labor costs.

By looking at only emerging markets as a separate entity from ex-US developed markets, we take the view that the risk profile within the emerging markets country group is similar, and thus we do not concern ourselves with risk in this model. Emerging markets generally entail a higher risk than developed markets, but this is considered in our initial allocation model which allocates between the US, developed and emerging markets.

Our model uses five factors which fall into three general categories; fundamental, macroeconomic, and momentum. Countries are ranked on each factor, and then each factor is assigned a weight, and then a consolidated ranking score is calculated for each country. The consolidated rankings are measured against the subsequent returns for each country to assess the level of correlation. There is a 25% positive correlation between our consolidated rankings and subsequent one year ahead returns. We also look at the percentage of years where there is a positive correlation between percentile ranking and performance percentile; the combined model has a score in excess of 84%. Lastly, countries are conviction weighted based upon their percentile ranking.

Separating winners from losers

There is a consistent and sequential relationship between our model rankings and country performance. Performance improves sequentially with ranking score improvement, validating the model methodology. The following chart shows the average returns and standard deviations by quintile ranking for one and two years ahead (15 years, 2000-2014).

Based on the models ability to separate winners from losers we provide two implementation options.

Implementation Solutions

The model is implemented using 20 country ETF’s and is available in two solutions:

• Emerging Markets EM Best Ideas Solution #4. A long only model with our top six picks, conviction weighted and benchmarked against the MSCI EM Index.
• Emerging Markets EM Long/Short Solution #10. A market neutral long/short model utilizing the top and bottom six countries.

Note that neither of these two solutions includes a market timing component.