In the Credit Suisse Global Investment Returns Yearbook 2015, there is a section dedicated to industry group analysis.  Beyond an interesting history lesson about the rise and fall of certain industries, the piece demonstrated the strength of both momentum- and value-based investing when it came to industry groups.

Credit Suisse Global Investment Returns Yearbook 2015 – Figure 1

This result was not particularly surprising, as it echoes prior research from Moskowitz and Grinblatt (1999), which demonstrates that industry momentum accounts for much of individual stock momentum, and of Scowcroft and Sefton (2005), who found that momentum is largely driven by industries for large-caps.

What did catch my eye, however, was how the performance analysis was performed.  To quote the article:

Each new year, we rank all then-existing industries by either their past year’s return

Frequent followers of this blog will know where this post is headed: right to my recent obsession with timing luck.  The question that naturally follows is: how dependent were these results on when the portfolios rebalanced.

First, I had to re-create the initial results.  So I headed over to the Kenneth French Data Library where I downloaded the 10 Industry Portfolios set.  Based on prior data in the Credit Suisse document, my guess is they used a set more akin to the 17 Industry Portfolio data – but I wanted to explore a strategy whose results could be easily implemented with current ETFs.

With the data in hand, I re-created the strategy: at the end of December, bucket the 10 industries into 5 quintiles and track the performance of those quintiles over the following year and reconstitute annually.  My results:

Industry Momentum Portfolios – Figure 1

 

The results aren't quite as extremely delineated as the work from Credit Suisse, but it has the same general theme: empirically, momentum works for industries.  And this isn't even 12-1 type momentum: we're buying and holding for a full year after reconstitution.  Note that the reduced relative performance variance in these results may be due to the truncated testing period (Credit Suisse went back to 1900 I believe), potential survivorship bias in the analysis (some industry groups have disappeared over time), or the use of fewer industry groups (which would mute variance through diversification.

But how much of this was simply due to when we reconstituted?  Performing the same test, but running it at the end of January gives the following results:

Industry Momentum Portfolios – Figure 2

Momentum, technically, worked here, but the results are not the 300bps of outperformance from the December reconstitution test – but a mere 10bps.  Let's broaden our scope to examine all months:

Industry Momentum Portfolios – Figure 3

 

The results are a bit jumbled.  To isolate what we care about – namely, "does momentum work?" – we can just look at returns of the 5th quantile minus the 1st:

Industry Momentum Portfolios – Figure 4

We can see that no matter the month we reconstitute our portfolio, there is a positive edge to this strategy.  However, on average, that edge is a full 100bps below the results we computed for December – so we might have had some timing luck in those results.

Looking back to the month-by-month results, we see something interesting though: the 1st quantile is the worst performer 75% of the time, but the 5th quantile is the best performer less than 50% of the time.  So perhaps the key here isn't buying the best performers, but rather just avoiding the worst.  Rerunning the test, I simply split the industries into two groups: the quintile representing the worst performing industries and everything else.

Industry Momentum Portfolios – Figure 5

The results look familiar: an average of ~200bps of outperformance.  In other words, picking the best may be less important to long-term momentum results than simply missing the worst.

Moskowitz, Tobias, and Mark Grinblatt, 1999, Do industries explain momentum, Journal of Finance 54, 1249–90.
Scowcroft, Alan, and James Sefton, 2005, Understanding momentum, Financial Analysts Journal 61, 64–82.

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.