outside the box but not off the wall
[QUOTE=Pascal;20914]I only have the 9 XLI, XLY, etc and GDX. That makes 10 ETFs.
What do you call "signal by year"? I work on a 20D time frame.
Pascal[/QUOTE]
Good news, Pascal. I believe that I was able to prove my thesis. Before I report, I'd like to clarify my initial request that you (and others) suspend your disbelief.
In your book you stated more than once that when you refer to the assessment of value, you are speaking in terms of trading opportunity. You distinguish this from the fundamental or intrinsic value of a stock, which your book does not address. Similarly, Billy has stated more than once that it is simply not possible to trade on fundamentals.
I'm sure that you are both right in that fundamentals are useless as criteria for entering and exiting short-term trades. The robot cannot consider fundamentals. But the robot CAN trade the vehicles that you choose for it. Please consider the possibility that fundamental factors can inform your choice of trading vehicles in a way that is likely to improve the robot's overall returns without requiring any change in the model.
Refer to your post of 2/14 (subject: "XL Models, continuation"), which is the second-to-last post on p. 4 of the Model discussion. You published back-tested model returns for each of nine sector ETFs, along with corresponding benchmark sector returns. Your focus was on the 2010-11 period.
I ranked the model returns by sector in 2010-11, and then compared that to a ranking of the sectors. I found:
- The top-ranked model sector (XLY) was also the top-ranked sector.
- The bottom-ranked model sector (XLF) was also the bottom ranked sector (suggesting shorting potential).
- The three top-ranked model sectors (XLY, XLE, and XLI) were all among the four top-ranked sectors.
[CENTER][IMG]http://sweetspotinvestments.com/wp-content/uploads/model-vs-sectors-2010-11.bmp[/IMG][/CENTER]
Even with such a small sample size, these findings seem to answer my original question: "Would the robot's performance likely be enhanced if it traded ETFs that are expected to produce long-term excess returns in their own right?" The answer is yes.
But how do you identify sectors that are likely to outperform for a period of years? I have suggested a well-supported method with a solid [URL="http://sweetspotinvestments.com/?page_id=7"]real-time track record[/URL] (better than a backtest). Moreover, the method was not my own invention, but was independently suggested by Morningstar and Lipper in a 1998 WSJ article reporting on their research:
[INDENT][INDENT]Buying selected lagging categories and lightening up on the leading categories is essentially a form of buying low and selling high. While it sounds smart, though, it is tough psychologically. Indeed, investors feel far more comfortable jumping into categories that have done well and bailing out of the laggards. [Ed. note: When it comes to investing, comfort is overrated.]
The result: “People tend to buy particular segments of the market as they are topping out, and they tend to pull out of sectors of the market as they are bottoming,” says Susan Dziubinski, editor of Morningstar’s monthly Morningstar Fund Investor publication. “Investors tend not to have great timing.”
Intrepid bargain hunters might want to look not at funds with big losses, but rather at those categories that have seen the biggest outflows of investor dollars, Ms. Dziubinski suggests. That has been a winning strategy over the years, Morningstar has found…[/INDENT][/INDENT]
(See Damato, Karen, "Emerging Markets Trail Rally but May Be Bargains for the Intrepid," Wall Street Journal; New York; by Karen Damato (Dec. 7, 1998). Start page: A11; ISSN: 00999660.)
Sometimes you get lucky and the backtesting is done for you...
Implementing this idea may require tracking -- and entering trades based on -- MF going forward for an assortment of liquid ETFs drawn from more than just these nine sectors. Do you have that capability? That is, are you able to track MF in real time for all sectors in your universe? [The number to trade in any given year will be small, but the universe from which each year's candidates are drawn should be large. SweetSpot's universe is ~100 sectors, many or most of which include at least one highly liquid ETF (depending on how you define "highly liquid").]
Cheers,
Neil
Suggestion for performance RT graphs
It is just an idea...
One of the issues with viewing the RT graphs is the impact on the performance viewing it in a browser.
Would a solution not be to create a separate webpage for each model?
Now the 20DMF RT and the GDX RT are one page.
If on one (sunny?) day the 9 other RT models are joined for the different S&P sectors, that will mean 11 RT graphs.
Maybe a separate page for each RT model...
Just a thought...
PdP