One of the biggest hurdles in executing tactical models is when to rebalance.  When a signal changes?  Weekly?  Monthly?  The choices can have a dramatic effect upon strategy results: the more timely the rebalance to the signal, the more of the movement that tends to be captured -- but the more whipsaw and trading costs that are generally incurred.

While we believe our model of dynamic, volatility-adjusted momentum is a more efficient method of capturing momentum opportunities, rebalance and timing discussions are still relevant in overall portfolio composition.

I wanted to dig in to this issue and show how the decision of when to rebalance can make an incredible difference in long-term performance.

To examine the effects, I chose to play with one of the more famous tactical risk management models: Mebane Faber's 10-month simple moving average timing model, popularized in his 2006 paper "A Quantitative Approach to Tactical Asset Management".  In the paper, Faber utilizes a simple methodology for determining whether an asset was eligible for inclusion in the portfolio based on whether it is above or below its 10-month moving average.

One of the issues is that in using the 10-month moving average, Faber's model implicitly trades on the first day of each month.  But what happens if we rebalance the 2nd day, or the 3rd?  The 15th?  Did choosing the 1st day end up materially changing the results?

In the interest of simplicity, I decided to model months as 21-day periods, and compared 21 different strategies using 1-day offsets, running the model on the S&P 500 ETF "SPY".  Each strategy rebalanced every 21 days; the 21st strategy rebalanced 20 days after (or, 1 day before, depending on your perspective) the 1st strategy.  Signals occurred after close and trading occurred at the next opens.  No trading costs or slippage effects were estimated.

The results are interesting to say the least.  Strategies for days 19 and 20 highlight the difference a single day can make:comparisonA single day changed the max drawdown from 19.03% to a 30.22%; annualized returns drops from 11.33% to 10.51%.  The full performance results for each strategy can be seen below:


While overall volatility levels remain fairly consistent, there is a 25,425bp spread between the total return for the best and worst returning strategies (717.14% and 462.89% respectively).

Obviously, when you chose to rebalance can have a huge impact on the whipsaw you incur.

So how can we fix this?  Well, one of the ways is to put 1/21st of our portfolio in each of these strategies -- rebalancing 1/21st of our portfolio every day -- and rebalancing back to equal-weight at the beginning of every year.  The results?

  • A total return of 625% (an annualized return of 10.98%)
  • Annualized volatility of 13.37%
  • A max drawdown of -19.03%

Now this analysis doesn't take into account trading costs -- but since we are rebalancing only 1/21st of our portfolio every day, the total turn-over ends up nearly identical to the turn-over from the original strategy.  It's certainly a bit more work -- but it also helps limit the impact of choosing the wrong date to rebalance.

By being smart about when we choose to rebalance, and how we choose to rebalance, we can remove the "luck of the timing" -- be it good or bad -- from our strategy and capture the pure quantitative effects.

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.