As a brief sneak-preview of a white-paper I am writing, I wanted to share the following graph.
Using the current constituents of the S&P 500 (technically, 422 of them based on data availability), this graph plots the rank stability of 1-month realized returns, volatility, and average pairwise correlation compared to historical ranks over an N-month look-back period. The worst-case scenario occurs when realized ranks are exactly flipped historical ranks (i.e. 422 becomes 1, 421 becomes 2, ..., 2 becomes 421, 1 becomes 422), leading to a total sum of rank changes of 97,241. For a given look-back length, realized rank-differences are measured as a percent of this worst-case then averaged over all walk-forward cases. The result is plotted below.
Not surprisingly, more data leads to greater stability in ranks -- though, in this case, it may simply be an effect of data availability, as the 10-month look-back can walk-forward nearly ~120 months, where the 80-month look-back can only walk-forward ~30 months. Nevertheless, we see that volatility rankings are much more stable than correlation rankings, which are much more stable than return rankings.
This sort of data analysis is a great way to understand the assumptions and vulnerabilities of quantitative methods: a strategy can only be as robust as its weakest link. Perhaps that is why S&P's SPLV has had demonstrably lower volatility than MSCI's USMV ETF: SPLV takes only the 100 lowest volatility equities and equally weights them whereas USMV uses factor models to project the variance-covariance matrix and performs an optimization. Since correlations exhibit less stability than volatilities, we may be able to conclude that USMV may exhibit less consistency in the low volatility it achieves relative to SPLV (especially since the equities in question have fairly high correlations anyway).
Corey Hoffstein
Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.
Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn.
Corey is a frequent speaker on industry panels and contributes to ETF.com, ETF Trends, and Forbes.com’s Great Speculations blog. He was named a 2014 ETF All Star by ETF.com.
Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University.
You can connect with Corey on LinkedIn or Twitter.
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