TAA Model for June, 2012
This is a monthly feature at the MarketSci Blog.
Our Tactical Asset Allocation (TAA) model selects up to four assets from a diverse basket of asset classes on the final trading day of each month. Below is the new allocation for today’s close. Click to read more about the TAA model.
I eat my own cooking, so I’ve devoted a healthy share of my own net worth to the TAA model (read why). On the last day of each month I share my new allocation (see above) and real-time performance (see below).
The model underperformed its benchmark in May, returning (as of 05/30) -2.6% versus -1.7%.
For June, the model will be dropping gold (GLD) in favor of a larger position in U.S. Treasuries (IEF).
The model has stacked up well against similar active strategies like Cambria’s ETF GTAA and the Permanent Portfolio (PRPFX), but has lagged what I think is the most important benchmark, a passive investment in equities and Treasuries rebalanced monthly (see stats below).
As I’ve discussed before, the model would historically have gone through extended periods of underperforming its benchmark, especially when the benchmark has been strong. This is a “generational” model and I’m much more concerned with returns over the next decades than any one month or year (read more). Real-time results have most definitely been in line with backtests.
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Happy Trading,
ms
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Filed under: Tactical Asset Allocation | 2 Comments
Out and About
I’ll be travelling for the next few weeks.
Business and trading will roll on, but the blog will be napping while I’m away.
Looking for your fix of trading geekery? Check out our most popular posts of the last month:
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Strategy #1 for Trading Volatility ETPs: Term-Structure Following
Strategy #2 for Trading Volatility ETPs: Timing the VIX
The Evolution of “Sell in May”
Trading Volatility ETPs: Obey Thy Stops
VXX/XIV Performance in Advancing & Declining Markets
EconomPic’s Seasonality Strategy
Happy Trading,
ms
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Filed under: Random Stuff | Leave a Comment
Revisiting an old observation…
Most of the stock market’s volatility comes in the daytime (open-to-close), but most of the market’s gains come overnight (close-to-open).
[growth of $1, logarithmically-scaled]
The graph above shows two hypothetical traders. The first (grey) is only long the SPY from each day’s open to close (daytime), and the second (red) from each day’s close to the following open (overnight).
Note that (a) the daytime session is about 60% more volatile, and (b) all of the SPY’s long-term gains since 1993 have come overnight and most of its long-term losses during the day.
That’s not the case this year, with all the market’s gains coming in the daytime session and both sessions being about equally volatile. Same chart, YTD…
[growth of $1, linearly-scaled]
Significance? Not sure.
In my past tests, I’ve never found a strong use for the daytime versus overnight relationship, other than polite dinner conversation.
I was digging back into the subject, and thought an updated chart was in order. As always, more to follow.
Happy Trading,
ms
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Filed under: Stock Market Mechanics | 6 Comments
The Evolution of “Sell in May”
One last bit of follow up to my previous post questioning the “sell in May” rule.
In the table to the right (click to zoom) I show how the stock market’s best 6 months of the year has evolved over the last 80+ years. Each row represents 20 years. Red cells denote the “best” 6 months.
Recall that the “sell in May” rule is based on November through April being the best half of the year.
Given the results in my previous post, I was surprised by how consistently Nov – Apr (or at least Dec – May) was the best performing period.
Happy Trading,
ms
OOPS: in my rush to get this post out I botched the table I initially posted. I was showing the best 6 months from 1930 to whatever year was listed on the table (rather than that particular 20 year period). Sincerest apologies!
Geek note: the “best” 6 month period was chosen based on volatility-adjusted (not absolute) dividend-adjusted S&P 500 returns.
Filed under: Time-based | 12 Comments
“Sell in May” Debunked?
This time every year the “sell in May and go away” strategy rears its head again.
It’s hard to buy in to the idea that such a simple approach could have such divining powers, but the results are (on the surface) compelling.
I’ve been pondering how best to put the strategy through the paces. Thoughts…
[growth of $1, logarithmically-scaled]
Usually a graph like the one above accompanies these discussions. Here I’ve shown the S&P 500 (dividend-adjusted) from Nov-Apr (red) vs May-Oct (grey), since 1950.
Awesome results. Great strategy.
The problem is of course that this is all prepared with the benefit of hindsight. Surely in 1950, we wouldn’t have known that Nov-Apr would turn out to be such fortuitous months for stocks. So in the next graph I’ve taken a different approach.
I’ve assumed that each year the investor only looked at the data available from 1930 up to that point in time, and invested in whatever 6 months of the year had been the best for stocks.
This is called “walking the test forward”, and (to some degree) removes the benefit of hindsight.
[growth of $1, logarithmically-scaled]
The graph shows that most of the benefit of choosing seasonally strong months disappears because the investor wouldn’t have made the “right” choices given the information available at that time.
The investor would have done well since the 1990’s, but that’s a much less robust observation than the first graph would imply.
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So what if rather than choosing seasonally strong months based on ALL data available up to that point in time, the investor only looked at say the last 10 years?
Same conclusion.
20 years?
Same conclusion.
If we go out to about 30 years (i.e. the investor is choosing seasonally strong months based on the previous 30 years of S&P 500 data), the strategy soars again…
But the fact that only 30 years (as opposed to say, 20) works so well is most likely because it’s a curve-fit solution.
So does the data totally debunk “sell in May”?
No. I wouldn’t base a trading decision solely on the rule, but results in all tests were impressive enough in recent history that the observation at least deserves to be on the radar.
But that really misses what I think is the more important point:
The graph like the first I showed would lead the reader to think that the “sell in May” rule is much more robust than it actually is. In truth the rule is at best a questionable observation, and at worst, simply a product of randomness.
Happy Trading,
ms
P.S. This post isn’t meant to dump on the quantitative minds who I respect very much that have discussed this subject recently. I’m just one nerd with $0.02 and I recognize that on this one, I am probably out on a long branch all by myself.
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Filed under: Time-based, Trading Strategies | 15 Comments
