Introduce country positioning into your strategy for improved investment returns
Some investors who adopt a quantitative approach struggle to identify which of the bewildering number of strategies, models and indexes actually improve performance. One antidote to this uncertainty is to start afresh, embracing alternative data and using it to build new factors -- such as country positioning -- that are statistically independent of the traditional quantitative mainstays.
A good example of this is the utilization of the country positioning data from mutual and exchange-traded funds. A variety of country-driven equity market predictors can be built using this kind of data. EPFR’s Allocation Skew is one of these.
Allocation Skew favours countries held with conviction by a small minority of managers. For a given country, Allocation Skew is defined as the percentage of managers, tracked by EPFR, in terms of Assets Under Management (AUM), who hold a smaller position in that country than the average for funds with similar geographic investment mandates. Since that average is measured from the fund sample itself, if many managers are underweight, the remaining minority must be massively overweight, making this factor a measure of conviction. By way of analogy, if ten students took an exam and nine out of ten students scored below average on a test, then the tenth student would have scored very high.
Historically investment strategies which buy and sell assets in countries with the highest and lowest scores in terms of Allocation Skew, have generated significant positive returns over a forward annual holding period. Table 1.0 labelled “Quintile Simulations”, shows annual forward returns on calendar-time portfolios formed by sorting countries using Allocation Skew and assigning to one of five quintile portfolios. Portfolios are rebalanced monthly to maintain equal weights. In Panel A, we report averages of Allocation Skew for each portfolio, the rightmost column showing the difference between the high and low countries in terms of that indicator. In Panel B, we report coefficients and t-statistics from the regression of log country returns over a forward year minus the log annual returns of the equal-weight universe against portfolio-membership dummies Q2, Q3, Q4 and Q1 - Q5. For any country in the ACWI universe at month t - 1, Q2, Q3 and Q4 are one if that country falls in the second, third or fourth quintile. Otherwise, these columns take on the value zero. Q1 - Q5 takes on the value 1/2, 0 or -1/2 if the country falls in the top quintile, one of the middle quintiles or the bottom quintile respectively. Coefficients have been exponentiated and expressed as percentages. t-statistics are shown below in parentheses.
As you can see from the column “Q1 – Q5” in the above table, there are statistically significant profits associated with the zero-cost strategy that goes long the top fifth of ACWI countries, in terms of Allocation Skew, and shorts the bottom fifth. The profitability of these strategies has proven to be independent of that momentum.
EPFR publishes monthly country rankings in terms of Allocation Skew. This metric is shown in Figure1.0 for emerging market countries at April 30, 2019.
We currently expect Argentine, Hungary, Egypt, Russia and Vietnam, on aggregate, to outperform Thailand, Chile, Taiwan, China and Korea over the coming year, based on this metric.
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