What a 2% Down Day After a Strong Quarter Has Historically Meant for the S&P 500
Wayne Whaley identified the setup. Here is what the numbers look like with a random entry baseline added.
Wayne Whaley posted an interesting study last week and I wanted to dig into it a bit further. Wayne is one of my favorite quantitative researchers. His work is data driven, straightforward, and worth following. The link to his original post is below. What I wanted to add is some context around the numbers using a random entry baseline. Same approach I use in my own research here.
The setup is simple. A 2% or greater down day that was preceded by a 10% or better 13 week quarter. That combination fired at Friday's close on June 5th. Thirty four prior instances going back to 1950. Small sample size. Worth keeping that in mind throughout. But 75 years of data across a wide range of market environments gives it some credibility.
The first thing worth noting is the short term. After a 2% down day the VIX spikes and near term noise increases. You can see that in the 1 week returns column. Big swings in both directions. In the current case VIX is sitting above 21. Worth keeping in mind.
But even in that noisy first week the numbers hold up better than you might expect. The setup produces a 67.6% win rate with an average winner of 2.31% and an average loser of 1.74%. A random 1 week hold in SPY produces a 57.43% win rate with an average winner of 1.64% and an average loser of 1.75%. The setup leads on every metric. Profit factor 2.77 versus 1.26 for random entry. That 1% average net gain in a single week annualizes to nearly 68%. There is real power concentrated in a short window of time here.
At 4 weeks the setup produces a 70.6% win rate with an average winner of 4.77% versus an average loser of 3.23%. Random entry at the same holding period produces a 64.09% win rate with an average winner of 3.31% and an average loser of 3.65%. Profit factor 3.55 versus 1.62 for random entry. That 2.42% average net gain over 4 weeks annualizes to 36.5%.
At 13 weeks the separation becomes dramatic. 88.2% win rate. Average winner 8.78% versus an average loser of only 3.70%. Only 4 losing instances in 34. Random entry at 13 weeks produces a 69.47% win rate with an average winner of 6.25% and an average loser of 6.07%. Profit factor 17.80 versus 2.34 for random entry. That 7.31% average net gain over 13 weeks annualizes to 32.6%. The sample size caveat applies here more than anywhere given only 4 losing instances driving that profit factor.
At 1 year the setup produces an 85.3% win rate with an average winner of 21.41% and an average loser of only 6.24%. That average loser of 6.24% against winners averaging 21.41% is the number that stands out. When this setup loses it tends to lose modestly. When it wins it tends to win big. Random entry at 1 year produces a 79.26% win rate with an average winner of 16.47% and an average loser of 14.34%. Profit factor 19.91 versus 4.39 for random entry. At 1 year the annualized returns converge. Setup 17.3% versus random 10.1%. The shorter holding periods are where the edge over random entry is most pronounced.
A few things worth keeping in mind before drawing any conclusions. Thirty four instances going back to 1950 is a small sample. The profit factors at 13 and 26 weeks are extraordinary but they are being driven by very few losing instances. That cuts both ways. The consistency across 75 years of market history is genuinely impressive. But past setups do not guarantee future outcomes and this is not a trading recommendation.
What I find useful about Wayne’s work is exactly this kind of simple clearly defined setup with a long historical record. No curve fitting. No complex rules. A specific condition that has shown up 34 times in 75 years and produced a consistent forward return profile worth understanding. Credit to Wayne for the original research. His post is worth reading in full.
Dave Johnson
Quantitative Developer
TradingTimeMachine.com