Can a simple set of exchange-traded funds (ETF), weighted judiciously, mimic the behaviors of most financial assets? In their January 2018 paper entitled “Mimicking Portfolios”, Richard Roll and Akshay Srivastava present and test a way of constructing mimicking portfolios using a small set of ETFs as investment factor proxies. They define a mimicking portfolio as a weighted set of tradable assets that match factor sensitivities of a target, which may be a specific asset, a fund or a non-tradable variable such as an economic indicator. They state that mimicking portfolios should: (1) consist of liquid, easily tradable assets; and, (2) exhibit little return volatility not explained by the factors used. They first winnow a large number of potential factor proxy ETFs spanning major asset classes and geopolitical regions by retaining only one ETF from any pair with daily return correlation greater than 0.70. They begin mimicking portfolio tests at the end of January 2009, when enough reasonably unique ETFs become available. They test this set of ETFs by creating portfolios from them that mimic each NYSE stock that has daily returns over the full sample period. Specifically, on the last day of each month, they reform a mimicking portfolio for each stock via a regression of stock return versus factor proxy ETF returns over the prior 300 trading days (or as few as 250 if 300 are not yet available) to reset coefficients for the ETFs. They perform an ancillary test by attempting to mimic iShares iBoxx $ Investment Grade Corporate Bond (LQD) and SPDR Dow Jones International Real Estate (RWX) ETFs, which are not in the factor proxy set. Using daily returns for the large number of ETFs and 1,634 NYSE stocks from the end of January 2009 through December 2016, they find that:
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The eight ETFs selected to be global financial market factor proxies, as described above, are:
iShares Core U.S. Aggregate Bond (AGG)
iShares U.S. Real Estate (IYR)
iShares International Treasury Bond (IGOV)
iShares Europe Developed Real Estate (IFEU)
iShares J.P. Morgan USD Emerging Markets Bond (EMB)
iShares MSCI Japan (EWJ)
iShares MSCI Hong Kong (EWH)
iShares S&P GSCI Commodity-Indexed Trust (GSG) - With monthly reformation, mimicking portfolios match the factor betas of target stocks reasonably
well. Moreover, mimicking portfolios:- Have on average about 80% lower return variance than targets.
- Have much smaller idiosyncratic volatility (unexplained by market volatility) than targets.
- Require only modest rebalancing actions.
- In most cases, mimicking portfolios track target stocks well for 100 trading days after portfolio formation, with half of tracking deviations within 0.2 target stock standard errors.
- However, results are discouraging for mimicking of LQD and RWX (particularly the former), perhaps because one of the factor proxy ETFs is highly correlated with the target in each case.
In summary, evidence suggests that investors can achieve factor exposures of target financial assets via regression-based weighting of a small set of ETFs, perhaps realizing large reductions in overall and idiosyncratic volatilities.
Cautions regarding findings include:
- The process for selecting factor proxy ETFs employs the same sample period as the mimicking portfolio tests. An investor operating in real time could not have known which ETFs to select as factor proxies. In other words, the selection process incorporates look-ahead bias, and the selected set of ETFs may not be effective factor proxies out-of-sample.
- The study addresses the accuracy with which mimicking portfolios replicate conventional factor beta exposures of target assets, not investment performances of mimicking portfolios. Any analyses of performance should account for costs of monthly rebalancings of ETFs within mimicking portfolios.
- The methodology presented is beyond the reach of most investors, who would bear fees for delegating to an investment/fund manager.
- As noted in the paper, excluding stocks with incomplete data may introduce survivorship/IPO biases in results. The authors claim such biases are not pertinent to research objectives, but they may be pertinent to investor objectives.
