In the past we've written about the luck and misfortune of rebalance timing.  As I reviewed the 2013 results from Scott's Investments for the Ivy10 portfolio, I couldn't help but wonder how much of the lack-luster performance could be attributed to this timing misfortune.

So I ran a simple test: I constructed 21 portfolios that would be rebalanced on the 21 different days of the month.  The trading rules were based on a 210 day moving average (10 months x 21 days per month).

Here are the equity curves for the 21 portfolios:


What we see is one equity curve which dramatically underperformed the rest.  What were the allocation dates of this unlucky portfolio?  12/26/2012, 1/28/2013, 2/27/2013, 3/28/2013 ... in other words, the end of the month, which lines up almost perfectly with the Ivy10 Portfolio timing model!

The dispersion in total return between these 21 portfolios is in excess of 500bp; the worst performing portfolio returned 4.22% for the year and the best returned 9.93%.  500bp is a high cost to pay for "unfortunately" selecting the end of the month to rebalance.

If we instead invested equally in these 21 portfolios on 12/31/2012, our aggregate performance would have been 8.04%.  Of course, this requires trading every day -- or, at least sampling trading signals every day -- which is unrealistic for most people trying to follow these simple models.  However, we can use a simpler model: holding only 4 portfolios (offset by 5 days) and trading once a week.  Our results would be a 8.71% return for the year.

The point here is that 2013 was a great demonstration of realized rebalance timing risk for this portfolio.  The lackluster results of the Ivy10 portfolio were due less to the trading signals themselves, but rather based on when we chose to (and know to) sample them.  A small change helps drastically reduce this timing luck and brings the 2013 return for the Ivy10 portfolio back a fairly respectable return level for a GTAA portfolio.

Edit: Performing similar calculations for the Dual ETF Momentum strategy; the worst portfolio returned 3.61% for the year and the best returned 8.71%; combining all 21 strategies returned 6.70% and combining only 4 returned 5.93%.  Again, the misfortune of rebalance timing rears its ugly head.

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 Trends, and’s Great Speculations blog. He was named a 2014 ETF All Star by

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.