ETF Tracking Quality
The tracking quality of ETFs may be characterised by several indicators, including not only the tracking error but also the tracking difference. The tracking difference is the difference between ETF total return and the total return of the replicated index, while the tracking error evaluates the volatility of the difference in return between an ETF and its benchmark.
Bonelli (2015) shows that depending on whether we consider the level of tracking error or the level of tracking difference, the ranking of ETFs that track the same index may greatly differ. For example, he observes that tracking error varies significantly across the different ETFs that all track the MSCI World Index (from 0.02% to 0.22%). The ETF with the lowest tracking error relative to the index has one of the highest tracking differences (-0.42%), and thus greatly underperforms its benchmark, while an ETF which has one of the highest tracking errors (0.21%) is also the one with the lowest tracking difference (-0.19%).
Similar results were obtained for two other indices, namely the MSCI Emerging Markets Index and the MSCI Europe Euro Index. Bonelli (2015) concludes that tracking error is not representative of the under- or outperformance of ETFs with respect to their benchmark, but serves first of all to evaluate the relative risk of daily deviations and is of more concern for short-term, rather than for mid-term or long-term, investors. Long-term investors may be more interested by tracking difference, as its level provides information about ETF costs. Indeed, if ETF replication was perfect, the tracking difference would be equal to the ETF expense ratio. Thus, the lower the tracking difference, the lower the expense ratio is.
It is a common belief that ETFs tracking ‘smart beta’ indices (non-market cap weighting schemes and/or factor exposure) exhibit weak replication quality due to friction costs implied by possible more frequent and wide index rebalancing.
Below is an illustrative analysis of the performance of smart beta vs traditional exposure ETFs vis-à-vis their respective benchmarks. It shed lights on replication accuracy with no consideration of the risk/return profile of the associated benchmarks. The analysis covers a universe of 732 Europe-domiciled ETFs that exhibit a three years track record (Jan 2014 – Dec 2016) that can be analysed on www.TrackInsight.com.
The analysis tends to demonstrate there is NO evidence that Smart Beta ETFs would possibly exhibit poor performance relative to their benchmarks that are tracking non-market cap weighting schemes.
Average Tracking Difference is strictly the same on the two universes, medians are close and dispersion around the mean is comparable. This analysis tends to contradict the common belief that smart beta benchmarks imply higher replication frictions due to more frequent or sizeable rebalancing.
When it comes to the Tracking Error, we can however observe a higher level of daily volatility for smart beta ETF relative returns which can be explained by the need for rebalancing the portfolios outside of the rebalancing windows of market cap ETFs on the one hand, but possibly by a bias towards less liquid securities for some smart beta strategies resulting in higher volatility in execution costs, with no significant impact on net costs in the long run.
The above table outlines summary results of a replication quality comparison between 3 ETFs; one following the regular version of the S&P 500 (in light blue), and two tracking smart beta versions of this same index.
As expected, the initial cost of smart beta ETFs seems to be higher than the iShares Core S&P 500. However, the annualized tracking differences of the 3 ETFs are very close. Even better, thanks to tax optimisation mechanism, PowerShares outperforms its benchmark by 36 bp per year, 8 bp better than the ETF on the classic version of the S&P 500, net of fees.
In return, as mentioned in our previous analysis, investors have to bear a slightly higher volatility for smart beta ETF relative returns, due to a more frequent rebalancing. This also applies in the example above where ETFs mimicking smart beta indices show an annualized TE around 10 bp vs 4 bp for the iShares Core S&P 500.
Bonelli, Maxime, Exchange Traded Funds: Toward a Tailored Selection Approach (April 2015). Working paper. Available at SSRN: https://ssrn.com/abstract=2516374
This article is an extract from “EDHEC European ETF and Smart Beta Survey 2016” published by the EDHEC-Risk Institute on May, 2017 which can be found here.