You probably could: could you thirnk of a simple one information page for the 9 ETFs. What should be put in so that everyone has an easy access and decisions can be quickly taken?Originally Posted by nickola.pazderic
Thanks
Pascal
You probably could: could you thirnk of a simple one information page for the 9 ETFs. What should be put in so that everyone has an easy access and decisions can be quickly taken?
Thanks
Pascal
Keeping it super simple, I suggest a basic design of this sort:
Actions could be colored: Green/Buy; Yellow/Cash; Red/Sell.
My idea is to make it simple enough so that professional simpletons, like me, can catch the message without a scratch of the scalp.
The wonderful engineering developments, percentages, philosophies, etc., should be provided as links.
I would be more than happy to help write any prose section that describes, for example, the EV logic behind the models.
This is my suggestion; others, of course, may well be superior.
Looking at the suggestion above, I see where some confusion could emerge.
Date/Time and Price-- These should all refer to the time and price recorded when the signal changed.
Pascal,
In item #1 of my proposal I recommended limiting the universe to liquid ETFs. Wouldn't that still leave a sizable universe?
For purposes of testing the concept, what if you were given each year's picks and only had to test their performance as robot vehicles? This could be accomplished by using the liquid ETFs that were available as SweetSpot picks in 2007-2012. You would have to make a leap of faith that the abandoned sectors identified by SweetSpot's method for calculating fund flows would be similar in character to those identified by looking at year-by-year MF for EV sectors. To give you a feel for whether such a leap would be reasonable, here's SweetSpot's simple method:
1) The data points are beginning-of-year (BOY) and end-of-year (EOY) sector assets, and sector returns for the year-just-ended.
2) Adjust BOY assets for returns (+% gain or -% loss) to calculate hypothetical EOY assets as if there were no fund flows (MF).
3) Calculate MF by subtracting these hypothetical EOY assets from actual EOY assets.
4) Calculate percentage MF by dividing MF into BOY assets.
5) Enter long-term positions in the sectors with the highest-percentage negative MF in the year-just-ended.
I share the view expressed here by Billy and others (most recently Nickola) that we should keep things simple. SweetSpot is nothing if not simple, and its historical excess returns offer the potential to significantly enhance the robot's performance. Wouldn't it be worthwhile to try to find a way to test this potential? For my part, in addition to the publicly available completed SweetSpot trades entered in 2007, 2008, and 2009, I am willing to share proprietary open positions that were entered in 2010, 2011, and 2012.
Neil
Neil, let's put this the other way: what sort of data would you need from me in order to test this concept?
I only have a limited set of ETFs with available MF models.
Pascal
Pascal,
For now I would need a list of the ETFs for which you have available MF models. Then, if there's enough overlap between your list and mine, I would need robot signals by ticker and year for the ETFs that I identify. I would calculate returns to enable an "apples to apples" comparison to other sector-rotation models.
Neil
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
On one hand, ten may not be enough for a meaningful test. On the other, it would be easy to test such a small number. (We may want to look at IWM as #11, even if it's not exactly a sector fund.)
As for "signals by year:" At the beginning of each calendar year, new ETFs would be added and old ones dropped. I suppose a dropped fund could continue to be held until the robot issues a signal to go to cash (if it's not already in cash).
I hope I'm making sense.
Neil
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.
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 real-time track record (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:
(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.)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…
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
Neil,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.
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 real-time track record (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:
(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.)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…
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
Thank you for your work.
I do not doubt the sweetspot theory. What I understand though is how impractical its implementation would be with the MF method, because the MF method is calculation intensive and requires much preparation time for each ETF.
To build one trading model on one ETF, I indeed need to have a few years of EV data for each component of that ETF (This means that I cannot do anything for ETFs that are pure derivative products, such as leveraged ETFs or ETF that track the price of a commodity future such as GLD.) I then need to find the weight of each component and the weight method of the ETF. After that, it is a matter of number crunching by applying the existing trading model.
Compared to the potential benefits, the work that I'd have to carry out to bring this idea to life does not make this project one of the most attractive.
Pascal
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