As passive ETFs continue their explosive growth, we examine the predictive power of StarMine signals in building profitable trading strategies using country ETFs.
- Between 2007 and 2016, the Investment Company Institute estimates that index domestic equity mutual funds and ETFs received US$1.4 trillion in net new cash and reinvested dividends.
- StarMine has created a number of unique and highly effective quantitative equity alpha models. These StarMine models have a long and proven track record of being strong predictive tools to rank individual securities and generate alpha.
- We have discovered that a linear combination of multiple StarMine signals achieves long-short quintile spread returns of 13.7 percent annually over a ten-year period.
Download the full research note: Using StarMine signals for country selection
In recent years, passive investment and interest in index mutual funds and exchange-traded funds (ETFs) has dramatically increased.
Between 2007 and 2016, the Investment Company Institute estimates that index domestic equity mutual funds and ETFs received US$1.4 trillion in net new cash and reinvested dividends.
In contrast, actively managed domestic equity mutual funds experienced a net outflow of $1.1 trillion.
In light of this rapid growth, constructing appropriate investing strategies using ETFs is now more important than ever.
StarMine models
StarMine has created a number of unique and highly effective quantitative equity alpha models. These StarMine models have a long and proven track record of being strong predictive tools to rank individual securities and generate alpha.
In 2011, research by Jasmeet Khela and Dirk Renick found that aggregating StarMine Value-Momentum (Val-Mo) model scores within countries is a profitable methodology to determine future outperformance at a country level.
Can the other StarMine alpha models be used to select country index products? And how can these signals be used to construct profitable trading strategies using index ETFs?
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Portfolio out-performance
To find out the answer, our research covered the StarMine Analyst Revisions model and the Price Momentum, Intrinsic Valuation, Relative Valuation, Value-Momentum, Earnings Quality, Smart Holdings and Combined Alpha models.
We created a long-short strategy based on a market capitalization-weighted average of StarMine signals within each country, where the individual securities comprising each aggregate are selected using Global Equity Indices.
With the success of that proof-of-concept we subsequently demonstrate how this strategy is profitable when applied to tradable liquid country ETFs.
Download the report: StarMine Signals for Country Selection
Based on monthly data from 2007 to 2016, most aggregated StarMine signals show consistent ability to sort country proxies and outperform an equally weighted portfolio of all country proxies on both a long-only and long-short basis.
The StarMine Earnings Quality model dominates the performance and generates an average annualized quintile spread of 10.4 percent and a long-only quintile return of 2.3 percent.
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Top performing StarMine signals
The StarMine Price Momentum model and Combined Alpha model are consistently solid and outperform the benchmark as well. The chart below delineates the cumulative quintile spread for the five top performing StarMine signals.
Moreover, we have discovered that a linear combination of multiple StarMine signals achieves long-short quintile spread returns of 13.7 percent annually over a ten-year period.
Furthermore, we show that a simplified and more practical version of this strategy, which requires knowing only the top holdings of ETFs, is also profitable.
