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May 4, 2018

Global Equity Allocation – Using StarMine Signals

by Tim Gaumer.

Recorded on: April 30, 2018

Authored by Brenda Zhang.

With the explosive growth in passive exchange-traded products (ETPs), constructing appropriate investing strategies in this area is more important now than ever. In recent years, investors’ interest in index mutual funds and ETPs has dramatically increased. The Investment Company Institute reports that between 2007 and 2016, index domestic equity mutual funds and exchange-traded funds (ETFs) received $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.[1]

StarMine quantitative models have a long and proven track record of being strong predictive tools to rank individual securities and generate alpha. Khela and Renick found in 2011 that aggregating StarMine Value-Momentum model scores within countries is a profitable methodology to determine future outperformance at a country level.[2]

Do StarMine models add value to country selection?

In earlier work, we showed that a monthly rebalancing long-short strategy using StarMine signals and Thomson Reuters Global Equity Indices successfully predicts which country indices will out- and under-perform.[3] With the success of that proof-of-concept we subsequently demonstrate how this strategy is profitable when applied to tradable liquid country ETFs. The research highlights that most aggregated StarMine signals show consistent ability to sort investable country proxies over the 9 year period from 2008 to 2016. The StarMine Earnings Quality (EQ) model dominates the performance and generates an average annualized quintile spread of 11.6% and a Sharpe ratio of 1.3. StarMine Price Momentum (Price Mo) model and Combined Alpha (CAM) model perform consistently solid and strongly outperform the benchmark – a buy-and-hold strategy that invests in iShares MSCI ACWI ETF.

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Figure 1: Cumulative quintile spread for the top performing StarMine signals over March 2008 – December 2016 assuming a monthly rebalancing long-short strategy. The dashed line represents the benchmark – iShares MSCI ACWI ETF.

A more convenient version of the strategy, which only requires examining the top holdings of each ETF rather than the full constituent list, also generates significant outperformance.

Do StarMine models add value to region selection?

A region ETF rotation strategy using StarMine signals obtains robust and persistent performance. The long-short strategy that employs StarMine EQ model and rebalances monthly achieves an annualized return of 7.4%, significantly in excess of the global market benchmark’s return of 2.4%.

Figure 2: Cumulative return for the top performing StarMine monthly rebalancing long-short region portfolios over March 2008 – December 2016. The dashed line represents the benchmark – iShares MSCI ACWI ETF. The table below lists the region ETFs considered in the StarMine portfolios.

Table 1: The ten region ETFs considered in the research.


StarMine models and components are available via Eikon, our Quantitative Database, QA Direct; and via FTP feed. The aggregated StarMine content grouped by business classification, geography, index and portfolio can be found in the Eikon Aggregates App. ETPs and indices are also available in Eikon.

[1] Investment Company Institute, 2017 Investment company Fact Book, P46.
[2] Khela, J., & Renick, D. 2011, StarMine Country Scores, StarMine Research Note.
[3] Zhang, B., & Genin, H. 2018, Using StarMine Signals for Country Selection, StarMine Research Note.
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