Current market environment performance of dynamic, risk-managed investment solutions.
By Will Hubbard
Traffic in my neighborhood follows the same rhythm at the end of every workday. When a nearby company lets out around 5 p.m., congestion builds almost like clockwork—a reminder of how often today’s patterns can tell us something about tomorrow’s.
Markets behave the same way. Most of the time, returns drift higher in small increments, with one period linked to the next. But just as a construction project can suddenly reroute cars and disrupt the normal flow, markets hit their own roadblocks—tariffs, the global financial crisis, COVID-19—that cause losses to cluster. Bad days follow bad days, each one related to the last.
Most investors focus on two variables: returns and volatility. Put them together and you get the Sharpe ratio, the industry’s standard measure of risk-adjusted performance. But there’s a third factor that rarely gets enough attention: autocorrelation, or the tendency for returns to follow one another.
Why autocorrelation matters
The industry uses a simple rule to translate daily or monthly returns into annual volatility: multiply by the square root of the number of periods per year for the return frequency you’re measuring. The rule works fine when returns are independent—when each period has no relationship to the one before it.
The problem? Markets rarely behave that way.
When returns exhibit positive autocorrelation, the square-root rule systematically understates risk. Newedge researchers show that the conventional approach produces biased and riskier results because it fails to account for clustering. When good months follow good months—or bad months follow bad months—realized volatility over longer periods exceeds what the simple square-root rule predicts.
Owen Lamont of Acadian Asset Management illustrates the practical implications. Imagine two strategies with identical Sharpe ratios—meaning the same returns and the same volatility—just with different autocorrelation structures. The one with higher positive autocorrelation is riskier and faces larger expected drawdowns.
How autocorrelation changes real-world risk
During the global financial crisis, the U.S. stock market had a drawdown of roughly 50%. But here’s the striking part: Lamont shows that if you take the exact same monthly returns from 2007 to 2011 and simply reorder them—like shuffling a deck of cards—you can generate negative autocorrelation instead of positive. In that reshuffled scenario, the maximum drawdown would have been only about 34%, not 50%.
Same mean. Same volatility. Completely different outcome.
This is the difference between panic and being merely uncomfortable. The global financial crisis wasn’t just about high volatility and negative returns. It was about bad months following each other, one after another, with losses clustering together.
We remember history through big events like the Lehman Brothers collapse on September 15, 2008. But the real damage came from the sequence of bad days from September 2008 through February 2009. It wasn’t one terrible day. It was many days moving in the same direction, stacking losses on top of losses.
As Lamont notes, history usually doesn’t “jump”—it crawls in the same direction for extended periods. That’s the danger of positive autocorrelation. It works like leverage, magnifying both upside and downside. The NASDAQ rose nine straight years from 1991 to 1999, encouraging many to leave their jobs to become day traders or stockbrokers before the bubble burst. The U.S. stock market fell four straight years from 1929 to 1932, wiping out a generation of investors.
When negative autocorrelation becomes an advantage
Not all strategies suffer from positive autocorrelation. Newedge also finds that trend-following CTAs— commodity trading advisors, or systematic strategies that use futures contracts and other derivatives to identify and capture trends across global markets—typically have negative autocorrelation around -0.25. This negative relationship can act as a natural shock absorber on drawdowns, making these strategies potentially more attractive as portfolio diversifiers than their Sharpe ratios alone suggest.
For investors looking to explore strategies that may behave differently when losses begin to cluster, CTAs can offer a powerful way to introduce that kind of adaptability into a portfolio. Flexible Plan Investments (FPI) incorporates specialized mutual funds offering exposure to alternative asset classes into accessible investment solutions (to learn how, read Jerry Wagner’s piece, “FPI brings Eckhardt’s elite trading strategies to your portfolio”).
Why sequences matter for drawdowns
Just as traffic around my house piles up in familiar patterns, market losses can cluster in ways that averages alone don’t capture—and that’s what makes sequences matter. It’s about how bad days cluster into bad weeks and bad months and eventually into real drawdowns.
When you’re building portfolios, don’t focus only on expected returns and volatility. Look at autocorrelation. If the strategies you’re analyzing have negative autocorrelation, they may offer natural downside protection that isn’t captured by standard risk metrics. But if a strategy has positive autocorrelation, you may be carrying a hidden risk that will only reveal itself when you need protection most.