As noted by Paststat, yesterday marked the Dow Jones Industrial Average’s 18th straight winning Tuesday. How rare an event is that? Very…
The chart below shows the longest active day-of-week winning streaks for the DJIA since 1900 (regardless of the day of the week).
The red arrow marks our current winning streak of 18. It hasn’t been since the 1920’s that we’ve seen anything close when the DJIA posted multiple 15 day-of-week winning streaks.
I should note that had we looked at the S&P 500 (which for the purpose of an analysis like this is more or less the same thing as the DJIA) the streak stands at just 8 Tuesdays, a much less impressive feat.
Of course this is all just polite dinner conversation and chart porn, and I put no stock in this as a useful timing indicator.
Happy Trading,
ms
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Filed under: Time-based | 6 Comments
There has been an abundance of “Sell in May” analysis out of the quantitative blogosphere this week. A quick roundup:
– Bloodhound Exchange + follow up
– Plus 99% of posts from the Stock Trader’s Almanac.
Hat tip to The Whole Street for all links. Feel free to comment with any I might have missed.
Happy Trading,
ms
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Filed under: Time-based | 1 Comment
Day of Month Seasonality for May
A topic recently on me noggin has been day-of-month seasonality (read more, more, and more). Using a simple walk-forward test to minimize hindsight bias, I showed that trading the days of the month that have been strong historically has consistently led to much stronger returns in the future. That’s as true today as it was in 1950.
Below is the DOM seasonality calendar for next month, broken out by quartiles (read why), with quartile 1 indicating the strongest days and quartile 4 the weakest.
Results in April were (beyond) abysmal, but real-time results since I began sharing the calendar in October have been mostly inline with the historical test.
The S&P 500 has averaged 0.09% (24% annualized) on the best half of days versus 0.05% (14% annualized) on the worst half.
Quartile 4 days (the worst of days) have been particularly bad, with an average return of -0.17% (-35% annualized).
As I stressed when I introduced the concept, day-of-month seasonality never justifies a trade all by itself, but I do think it deserves to be one of many tools in the trader’s toolbox.
Happy Trading,
ms
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Filed under: Time-based | 1 Comment
Inspired by Hyperq (hat tip The Whole Street)…
The graph below shows how each day is weighted in a 10-day simple moving average (grey) versus exponential moving average (red). For the uninitiated, the SMA and EMA are the two types of moving averages most commonly employed by traders.
In the 10-day SMA, each day from 0 (the most recent) to 9 (the most distant) is equally weighted (10%).
In the EMA, day 0 makes up 18.2% of the average. That falls to just 3.0% by day 9. The left tail on the graph (day 20 and beyond) extends indefinitely, but in total makes up just 1.8% of the average.
The next graph show the daily weighting for a 50-day SMA/EMA, and the graph below that compares a 10-day EMA to a 50-day EMA.
For those familiar with how these averages are calculated, none of this is new information, but I thought it was interesting to see it visually.
For me personally it’s a reminder of how arbitrary an SMA is, weighting the very last day in the average equally with the most recent day, and how much of an impact it can have when that last day falls out of the average despite not telling you much about what’s going on today.
Happy Trading,
ms
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Filed under: Random Stuff | 5 Comments
VXX’s Brief Moment in the Sun
Random thought for the day…
I (humbly) disagree with folks who poo-poo VXX (long 1-month VIX) as a bad investment.
Let me rephrase that. I think it’s very worthwhile to educate investors about why buying VXX is usually a bad choice (read more), and why buying VXX for the long-term is always a bad choice, but VXX isn’t in and of itself broken. It’s just an investment whose brief moment in the sun hasn’t come yet.
Investors would be forgiven for losing sight of that. Behold the horror show that has been VXX since inception in 2009:
[growth of $1, logarithmically-scaled]
But there’s a lot more data to consider than most realize. Recall the graph below that I’ve shown previously estimating VXX back to 2004, adding an additional 5 years of data prior to the ETF launch (1).
[growth of $1, logarithmically-scaled]
Some notable VXX runs: a 97% gain within 2 months (2007), a 183% gain within 3 months (2011), and the big daddy, a 336% gain within 3 months (2008).
The problem is of course that investors too often try to trade VXX by timing the market. They preemptively buy VXX when the market gets overbought, and then get decimated by the water torture that is contango if the market does anything but go straight down.
A much better approach is to let the state of the VIX futures term-structure (i.e. backwardation) be the guide as to when VXX might be a viable play, and then (and only then) attempt to time the broader market.
That day will come, because the return of big volatility is necessary and inevitable. And when it happens, investor darlings like XIV (inverse 1-month VIX) will get crushed. Contrary to what is becoming conventional wisdom, XIV is only slightly more appropriate as a blind long-term play than VXX is.
To illustrate, the same extended historical data set for XIV back to 2004:
[growth of $1, logarithmically-scaled]
The difference between them is that XIV is usually the wise choice, but when it’s a bad choice, it’s really a bad choice. Flip that on its head for VXX. VXX is usually the unwise choice, but when it’s the right choice, it’s really the right choice.
VXX isn’t broken, it’s just an investment whose next brief moment in the sun hasn’t come yet.
Shameless self-promotion: to see MarketSci’s own approach to timing VXX and XIV, check out our Volatility ETF Strategy.
Happy Trading,
ms
(1) VXX data through 12/2005 estimated based on VIX futures, through 01/2009 based on the underlying VIX Short-Term Futures Index, and to date based on actual VXX ETF data.
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Filed under: VIX & Volatility | 8 Comments
