Numerous marketing pieces circulating around the web show the detriment that trying to time the market can have on a portfolio. These pieces often look similar to this chart, which shows the cumulative growth of $1 invested in the S&P 500 ETF (SPY) assuming that a given number of best days are missed over the period from 1995-2014. Missing 0 days is equivalent to simply buying and holding SPY.

Best DaysHonestly, while this chart is technically accurate, it is a bit misleading. After all, if you were so skilled to miss all the best days, you could just invert your strategy – thereby identifying all the best days – and increase leverage on those days.

An equally misleading chart would be to show the returns from missing only the worst days:

Worst Days

This chart shows how much we stand to gain by missing the worst days. However, missing only the worst days would take as much skill (or luck) as missing only the best days.

But there is good news for a momentum-based approach…

Markets exhibit “clustered volatility,” which is a fancy way of saying that the big up days tend to coincide closely with the big down days.

So for strategies that seek to avoid drawdowns and the worst days of the market, the best days are often missed as collateral damage.

Best Worst Days Heatmap

But the net result of missing both the best and worst has historically been positive.

Best Worst Days

Looking at missing the best and worst days is an interesting exercise, but in all of our portfolios, the most frequently we would rebalance is generally weekly. So what happens if we simultaneously miss the best and worst weeks?

Best Worst Weeks

And monthly?

Best Worst Months

The process of missing only the worst days is like getting a 100% on a multiple choice test – you generally have to have nearly perfect knowledge, possibly paired with some good luck.

The process of missing only the best days is like getting a 0% on the test – you either knew every answer and did not choose it on purpose or were extremely unlucky. Both case are unrealistic in investing.

We do not claim to be able to time the market perfectly every time, but by using momentum to inform our investment decisions, our goal is for our models to learn enough about the current state of the market to identify the periods that are fruitful to miss.

We know enough about human behavior to know that we cannot fully understand human behavior. By focusing on periods of significant drawdowns, rather than on bad days, weeks, or months, we aim to sidestep the cycle of investor overreaction in both directions.

Nathan is a Portfolio Manager at Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Nathan is responsible for investment research, strategy development, and supporting the portfolio management team. Prior to joining Newfound, he was a chemical engineer at URS, a global engineering firm in the oil, natural gas, and biofuels industry where he was responsible for process simulation development, project economic analysis, and the creation of in-house software. Nathan holds a Master of Science in Computational Finance from Carnegie Mellon University and graduated summa cum laude from Case Western Reserve University with a Bachelor of Science in Chemical Engineering and a minor in Mathematics.