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EPFR Investment strategy 2019 : Established model telling investors not to climb on any EM bandwagons

 Quants Corner 0702

Quants Corner 0702

Table 1.0

Of the 40 fastest growing economies in 2018 only one, Ireland, was a recognized developed market. Emerging Markets also have something of a monopoly on reform stories. So, the persistent underperformance of emerging markets compared to the developed markets counterparts over the past 12 months presents quantitative and ‘quantamental’ investors with a dilemma when it comes to selecting and deploying strategies.

The macroeconomic factors that the market appears to be ignoring when assessing the relative value of emerging markets did assert themselves in 1Q19, when emerging markets indexes staged a rally. But the gap widened again during the second quarter.

What to make of this seeming contradiction? And how best to navigate it?

The chart below [Bar Chart 1.0] overlays the net monthly flows into all EPFR-tracked Developed Markets Funds with those going into and out of all Emerging Markets Equity Funds since 1Q12. It is important to note that the flows to DM Funds are consistently larger, which reflects their much larger AUM relative to EM Funds.

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The next chart [Bar Chart 2.0] also incorporates the cumulative flows for the two fund groups in % of AUM terms. What both charts show is that flows into DM and EM Funds tend to move in the same direction. But when they diverge, it often heralds a major ‘rotation’ by investors from one asset class to the other.

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Over the last 7 years, we have seen three great rotations between EM and DM Equity Funds. These occurred in 2012, 2014 and the current year.

To put what these charts illustrate in quantitative terms, we use the following equations to extract the basic directional signal:

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Building on these insights we have created a simple trading strategy that uses EPFR flows to predict which of the two fund groups will outperform in the future.

The strategy in based on flows as a % of AUM for the two fund groups. To get a number, we take dollar-denominated flows and divided them by total net assets of the respective fund groups. This gives you a relative measure of the assets flowing into or out of the asset classes – flow% -- and, to a significant degree, ’normalizes’ the size bias in favor of DM Funds.

The trading strategy follows a simple rule. If the flow% number for EM Funds is bigger than DM flow%, then investors should bias their allocations towards EM assets. If the DM flow% number is bigger, the investors should shift their focus to that asset class.

How has this worked out? The chart below illustrates the strategy’s performance (dark green line) versus and equally weighted portfolio.

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The result is a total return from the simulated trading strategy over the past seven-and-a-half years of 106.91%. That is almost double the return, 62.20%, from the equally weighted portfolio.

Another way to illustrate this strategy is the chart below, which builds a wall using the historical EM/DM signals as ‘bricks.’ The dark purple ones indicated weeks when the signal favored EM assets, dark green the weeks when DM was in the ascendency.

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On the right-hand side of this chart, the number represents the percentage of weeks that the strategy favored EM assets. In 2012, for instance, 73.1% of the weeks broke for EMs. Year-to-date, EM has been the call 60% of the time.

Of late, however, the needle has tilted firmly towards DM assets. With EM’s generally growing faster, the US Federal Reserve and European Central Bank expected to shift to easing biases in 3Q19 and Sino-US trade tensions showing signs of easing, this may seem counter-intuitive. But the track record for the EPFR model suggests that investors should resist the investment case for EM equities a while longer.

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