Welcome to the beta launch of the Portfolio123 ETF section
Introduction to the Portfolio123 ETF Screener
Much of what you already know and do in the Portfolio123 stock screener is applicable to ETF screening as well.
• The basic rule-construction interface is the same, as are ancillary features such as identifying a universe, a benchmark, and the “As Of” date.
• The way you save, categorize, and access your saved screens, etc. is the same.
• The Quick Rank feature is the same as in the Stock screener.
• The Backtesting and Historical Screening interfaces are the same.
• Custom formulas are created and accessed the same way as in the Equity screener.
Other features are not yet available for ETF screening, but when they are introduced, they will work the same way as with Portfolo123’s Stock capabilities.
• Custom Reports
• Ranking
• Simulation
As to the differences, some relate directly to the interface and its capabilities. Others relate to context, ways in which inherent differences between the worlds of stocks and ETFs warrant different approaches on your part.
• Custom Universes (and a new ETF-oriented set of factors in the Groupings category)
• Distributions
• Liquidity considerations
• Wide variation in the amount of historical data available (many ETFs were created fairly recently)
• The kinds of rules you can create
• How many ETFs you should expect to see in your final result set
• How many rules you should expect to use in your screens
• Benchmarking considerations
• The role of market timing and the ability to go short
• Rebalancing considerations
While all of these differences are important, we consider the Custom Universe capabilities first among equals. We believe our approach gives you unrivaled flexibility in slicing and dicing the ETF world as broadly or as narrowly, as generically or as idiosyncratically, as you’d like.
Custom ETF Universes
Our goal
All Portfolio123 ETF activity starts with “All ETFs” as the default universe. If you are satisfied with that, you can skip the custom ETF Universe area. But many, perhaps most, users will want to add at least some level of universe definition, and Portfolio123 provides exceptional flexibility in the way you can do that.
Suppose you are interested in finding ETFs that invest in U.S. growth equities.
The ETF screener offered by one major provider also forces you to choose between large growth, mid-cap growth or small growth. If you’re agnostic regarding size, you’d have to create three separate growth screens and then, mentally combine the results on your own.
With another name-brand provider, you’re completely out of luck: the only choices you can make are all equity or all fixed Income.
Another major investment portal offers an ETF sort tool (not a screener) that lets you to identify the best performing Growth ETFs, but you could not narrow to small growth if you would be so inclined. And even if you are, as originally suggested, simply interested in just plain Growth, it’s not clear what criterion was used to assign ETFs to that category. For example, as of this writing, we noticed that this portal’s Growth category included PowerShares Dynamic Mid Cap Value (PWP) and Vanguard High Dividend Yield Index ETF (VYM).
First off, we reviewed each ETF, one at a time, to determine its classifications. With regard to style, we classify PWP as Value and VYM as Equity Income, not Growth.
Moreover we wanted to allow you to define your universe however you’d like. If you want all Growth regardless of size, you can choose that. If you want Mid-cap Growth only, you can get that, too. Or, you could choose Mid and Small-micro cap growth. Regardless of how you want to define growth, you can also add regional and/or country preferences.
Suppose you want something completely idiosyncratic: a universe consisting of currency ETFs, agricultural commodity ETFs, and short-term fixed income ETFs. On Portfolio123, you can create such a universe.
Defining A Custom Universe: The Categories
When you click to create a new custom ETF universe, you will see eight filters and an opportunity to edit each one. The Filters are:
• Family
• Asset Class
• Region
• Country
• Method
• Style
• Size
• Sector
In the default state, each filter category defaults to None. You can pick and choose which categories you want to filter. If you want Mid-cap and Small-micro-cap Growth stocks, you would add filters within the Style and Size categories. If you want to also restrict yourself to U.S. equities, you would add another filter in the Country category.
Portfolio123 users are accustomed to seeing N/A (Not Available) as a throwaway label indicating that something is to be discarded. It’s different with Custom ETF universes. N/A can be a very important designation and one which may, on many occasions, be actively chosen. We’ll explain at the end of this section. For now, though, here are the categories:
Universe Categories: Family
Many users will have no inclination to add any filters that constrain the universe based on ETF-family. But some will. Experienced ETF investors do think in terms of brand differences.
Some advisors, for example, may want to confine themselves to iShares and, perhaps, the SPDR family of ETFs. Others may find special appeal in the PowerShares offerings. Traders nowadays, particularly those who engage in market timing, may be interested in restricting their efforts to ProShares (and, possibly, Rydex and Dimension).
As with all the other categories, you can ignore it, you can choose one family, or you can mix and match as you wish.
Universe Categories: Asset Class
Here are the asset classes you can choose (individually or in whatever combinations you wish):
• Alternative
This is a catch-all category for ETFs that are not amenable to traditional classifications. As of this writing, the only ETF in this category is one that invests in European carbon emissions credits. More alternative ETFs were on the drawing board before the financial crisis erupted. The extent to which this category will be further developed by ETF sponsors remains to be seen.
• Commodities
These are ETFs that invest in commodity futures contracts. We gave careful consideration as to whether we should combine these with equity ETFs that invest in commodity-producing companies. We decided against it because futures investing really does have a different flavor, mainly in the way contango or backwardation can impact performance separate and apart from (and often in opposition to) commodity price trends. Another factor in our decision to restrict this category to futures-only ETFs is the ease with which users can create a custom universe that includes commodities and relevant stock sector ETFs if they so wish.
• Currencies
This is a fairly new group of ETFs that have emerged to mimic currency trends via use of direct investment and/or derivatives.
• Equity
This, of course, is the largest and most traditional category of ETFs.
• Fixed Income
Fixed income, huge in the world of open-end mutual funds, but was not well represented in the early days of the development of ETFs. Now, ETF offering here are growing and starting to become more segmented (term structure, municipal versus taxable, etc.).
• Mixed Assets
This is a fairly new asset-class category, but one which seems slated for growth in the years ahead, judging by indications from ETF sponsors. Simply put, these are ETFs that invest in more than one asset class, usually a combination of stocks and bonds. Sometimes, asset decisions are based on an allocation model. In other cases, they reflect the emerging “target-maturity” trend, which aims to see fixed income grow as the ETF approaches a planned liquidation date.
Universe Categories: Region and Country
Generally, these categories are self explanatory.
As you peruse the available Regional choices, you’ll notice the obvious geography-based choices (North America, Europe, etc.). We also include some choices that are thematic: Emerging, Developed, Pacific Ex Japan and BRIC-Chindia. With regard to the latter, BRIC stands for Brazil, Russia, China and India. Chindia refers to just China and India.
When it comes to countries, we list all that are available in the database. Be aware, though, that in many cases, there are no country-specific ETFs. If you want to get a sense of what individual countries are available now, choose a region and then examine the list of ETFs that are included.
Universe Categories: Method
A few years ago, there would have been no need for us to address this. Everything would have been in the category we refer to here as Standard Long. But now, there are more choices:
• Hedged
These are ETFs that attempt to go long or short, and/or use derivatives, in order to give hedge-fund like performance (at least the ideal). At present, there are only two ETFs in this group, both of which use options to hedge. Our decision to articulate this as a separate category reflects the likelihood that such ETFs like these will be introduced in the future.
• Leveraged Long
These are ETFs that use leverage and/or derivatives to try to double the daily moves of their benchmarks. In other words, a leveraged S&P 500 ETF will aim to rise 2% in a day when the S&P 500 rises 1%.
It won’t always hit the mark precisely. But for the most part, they have come close enough to satisfy investors.
It’s especially important for anyone working with these ETFs to recognize that the doubling (or tripling in the case of one family) is meant to be operative on a daily basis. If you are aiming at a one-week holding period, the ups and downs of individual days may vary such that the overall five-day performance of the benchmark won’t translate to an overall five-day doubling of that leveraged ETF. Mathematical compounding can produce varied results depending on the daily pattern of ups and downs, mre so as holding periods stretch out.
Aggressive users may be tempted to jump right into these leveraged ETFs. We suggest a gradual approach. If you are unfamiliar with these products (mainly Proshares ETFs, but also Rydex and Dimension), take some time to watch them and get accustomed to how the doubling plays out over the holding periods that interest you. And, of course, make sure your personal risk tolerance can cope with a deliberate quest for volatility.
Another matter that surfaced in a big way late in 2008 was tax efficiency. Some leveraged ETFs, mainly from Rydex but ProShares as well, wound up making massive capital gain distributions, something that is not usually expected in the ETF world. You should familiarize yourself with these issues, which tend to be well covered in the and in the ETF section of ETF section.
• Leveraged Short
These work like the Leveraged Long ETFs but the portfolios go short, directly and/or via use of derivatives.
These have been huge headline-grabbers since they make it so easy for any investor to sell short. From the point of view of the trade, all you’re doing is making a long stock purchase. If, for example, you want to short the S&P 500, you make a long purchase of an ETF that is designed to deliver the inverse of the S&P 500’s performance, or in the case of a leveraged short, double the inverse (i.e. if the S&P 500 drops 1% in a day, these leveraged ETFs are expected to rise 2%, and vice versa).
The ETF buyer need not deal with the usual baggage of short selling (paying dividends, the uptick rule, potentially infinite losses) since these are handled within the ETF portfolio. However, if one is using leveraged short funds, refer to the comments above (regarding Leveraged Long) about daily leverage in general and compounding issues as well as tax efficiency.
• Quant model
This is a fairly new development in the ETF world that was popularized by PowerShares and subsequently followed by others. It involves the use of quantitative models to try to identify stocks with strong prospects for relative performance.
This approach has induced much in the way of rhetorical gymnastics for attorneys who work for ETF sponsors, since we don’t usually think about passive benchmark-tracking funds seeking to outperform anything. Here’s how they do this: First, a proprietary index is created to be passively tracked by the ETF. That way, the ETF fits smoothly into the usual structure as a passive investment. Second, the unique wrinkle, is how stocks are selected for this index. Instead of relying on decisions by a selection committee to pick what they hope will be a “representative” (whatever that means) sample of the overall U.S. business world, the decisions are made by quantitative models that are designed to outperform the standard indexes like the S&P 500. (These models can be conceptually similar to the ones built by Portfolio123 users for stocks.)
That’s how outperformance enters the passive world of ETFs. These ETFs are as passive as any when it comes to tracking their nominal benchmarks. The outperformance enters as the index sponsor aims for a “my index is better than your index” situation.
It’s too early to say how all this will work out. But it does seem to be a briskly growing area within the ETF world. Portfolio123 users can ignore the distinction simply by combining Quant model with Standard Long. But many may find it interesting to look at the quants alone and try to design screens that identify which ones have the hot hand.
• Special Weights
This is a close cousin of Quant Model and is another case where attorneys and marketers, have had to jump through some verbal hoops.
The usual situation, evidenced most notably in S&P 500, is for an index to include stocks on a market-capitalization weighted basis. This does not necessarily reflect an assumption that bigger-is-better but is instead based on an ivory-tower effort to have the index represent the investment world as it is. If Company X is five percent of the world, than an index that depicts the state of the world should give Company X a five percent weighting.
Enter Wisdom Tree in the mid-2000s, which decided to weight its “index components” (i.e. stocks held in the ETF portfolios) based not on market capitalization but instead on the total dollar amount of dividends paid by the companies.
Clearly, this departs from the academic represent-the-world situation. That raises the question of what it is designed to do. One can find some marketing rhetoric suggesting that dividend-weighted indexes are supposed to be better. A particular point made by Wisdom Tree is how market-cap weighted indexes can exaggerate market trends and how dividend weighting can produce more stable results. While they don’t talk overtly of pursuing alpha, they still seem to be in the “my index is better than your index” camp.
Accordingly, one might argue that these ETFs ought to be included in the Quant Model category. We strongly considered doing just that. But as models go, these weighting rules seem much more . . . excuse the rhetorical blurring . . . passive. Just as some might suggest dividend weighting is better, others might suggest it’s just different.
Adding to the mix is another group of ETFs (FTSE RAFI, distributed through PowerShares) that seeks to weight indexes/portfolios not simply based on dividends but instead based on fundamentals (a proprietary combination of factors such as revenues, book value, etc.).
Because of the nebulous and often subjective relationship between Wisdom Tree and FTSE RAFT on the one hand and PowerShares model-based (“Dynamic”) ETFs on the other hand, and indications that more fundamentally-weighted ETFs may be forthcoming, we decided to establish Special Weights as a separate category. However, Portfolio123 users could, if they wish, build a universe that combines Special Weights with Quant Model.
• Standard Long
This is the traditional approach to funds; buy the stocks and hold them pending rebalancing.
• Standard Short
This is similar to leveraged long, except that here, ETF daily performance is targeted at the inverse of the benchmark, rather than a doubling of the inverse.
Universe Categories: Style, Size and Sector
These include traditional, familiar choices.
Style includes such old standbys as Growth and Value as well, here, as Equity Income. It also includes some choices relevant to fixed income (short term, intermediate term, long terms, high yield).
Size includes General (for ETFs that are agnostic regarding the issue of company size), Large-mega cap, Mid cap and Small-micro cap.
Sector includes many familiar choices, but also a few new wrinkles. Those interested in fixed income can choose Taxable Fixed Income or Municipal Fixed Income as sectors. For equity investors, there’s also a category called Special Theme. Examples of this currently-small but potentially growing group include ETFs designed to invest in companies with big share buyback programs and an ETF designed to hold companies whose shares are being bought by corporate insiders. Social, as a sector, refers to ETFs that invest in companies the sponsor believe are socially conscious.
The role of N/A
Suppose you want to create a universe consisting of Currency ETFs and Taxable Fixed Income ETFs.
Start by going to Asset Class. Select Currencies and Fixed Income. As of this writing, that would establish a 78-ETF universe.
Next, go to sector and select Taxable Fixed Income. That will narrow your ETF universe to 44. Unfortunately, you’ll find there are 44 non-municipal fixed-income ETFs, but no currency ETFs. The latter vanished when you made your Sector choice. None of the currency ETFs met the sector requirement of Taxable Fixed Income.
In this exercise you correctly did what filtering is supposed to do; you narrowed the universe by a set of progressive filters. But the result is not what was desired.
Go back now to Sector. Continue to check Taxable Fixed Income. But this time, add a second check to N/A, which you’ll find at the top of the list. After you update, you’ll find that your ETF universe grew from 44 to 61 (as of this writing). By checking N/A for sector, you brought the Currency ETFs (for which sector is N/A) back into your universe.
N/A is offered as a choice whenever it is possible for that designation to appear for an ETF. Rather than seeing it as an error condition, which is often the case with stocks, learn here to view it as a bona fide data-point.
Experiment a bit in the early going. Create universe and then look at your results. If the universes strike you as being too small, if it appears that you inadvertently eliminated some ETFs you would rather have kept, chances are you will have failed to check N/A as a choice, as in the first pass with the currency-fixed income example presented above.
Distributions
This isn’t supposed to be an issue with ETFs. One of the hallmarks of this area was supposed to be tax efficiency.
2008 was the year when we learned we need to take a fresh look at this issue. As ETFs become increasingly exotic, it’s becoming more possible for these funds to accumulate gains that need to be paid out at the end of the year. We saw that a lot in 2008 with short and ultra-short ETFs.
This is a complex and emerging topic, one that is beyond the scope of this Help material. For now, be aware that this is something one should think a about when moving into the less generic offerings.
A good way to follow this issue, and others impacting the world of ETFs is to get into the habit of checking and the ETF section of (which publishes, among other things, the IndexUniverse features).
Meanwhile, be aware the prices used by Portfolio123 are adjusted not only for splits but also for all dividends and distributions.
Liquidity Considerations
We’re not dealing with exotic derivatives held by hedge funds. Strictly speaking, all of these ETFs are liquid with trading done exactly as you would for normal common stocks. But like the stock market in general, some ETFs trade more frequently and easily than others. You can always buy or sell any ETF, but with some of the more obscure ones, you risk noteworthy price slippage.
This is an important consideration. The financial media has ably chronicled the rapid rise of the overall ETF universe. Less clear (but starting to get more attention now) is the fact that many recently-introduced ETF products have not achieved commercial success (i.e. they have not attracted much in the way of assets nor do they trade often).
Less successful ETFs raise two kinds of issues. One, as noted, is difficult tradability leading to price slippage. The other is ongoing viability. ETFs generate revenues based on assets and in some cases, trading volume. If either is and stays too low, the sponsors will lose money and eventually liquidate the ETF. We’re starting to see that happen now. The NETS family is liquidating (though it will stay in the database for purposes of point-in-time backtesting). Ditto most of the Healthshares ETFs and a few of the Claymore products.
It’s probably a good idea for each screen to include a liquidity-oriented test, based on AvgDailyTot or AvgVol. Depending on the kinds of ETFs you seek, such tests may be very tolerant (i.e. AvgVol(20)>1000). But even a tolerant test that bends over backwards toward inclusion is better than no test at all.
Availability of Historical Data
When it comes to backtesting, many Portfolio123 users click quickly on “Max” in order to get as big a sample period as possible. With ETFs, one should think carefully before doing this.
As with stocks, “Max” will take you back to 3/31/01. But the ETF world back then was much smaller than it is today.
To get a sense of how your strategy is impacted by changes in the ETF population, you may want to create a preliminary screen applying the following rule to your chosen universe: Close(x)>=0, with x being 250 times the number of years you wish to consider. So, if you’d like to consider a 7 ½ year backtest, use Close(1875)>=0. This will tell you how many ETFs in the universe had shares trading 7 ½ years ago.
The number of ETFs passing such a test is 102. It’s a perfectly usable universe, including, as it does, all the big well-established names. But none of them are, for example, in the BRIC or emerging regions, so if you are interested in a strategy like that, your early Max backtests will feature a long initial stretch with zero of few ETFs. Cutting the history to five years provides only 2 BRIC-Emerging ETFs. Only 4 such ETFs are available for two-year backtests. In fact, only 20 exist today, 17 of which were around long enough for a one-year backtest.
Once you’ve experimented with this preliminary screen and determined how many years back you could plausibly backtest, eliminate this rule and go about building your screen as you normally would.
It is important that you avoid including the close(x)>=0 test in your final screen. Suppose, for example, you decide, based on Close(500)>=0, that your universe will support a two-year backtest. Suppose, too, that it is now 3/1//09. That means your backtests will begin 3/1/07. If your screen includes the Close(500) rule, the screen you create on 3/1/07 would include only those ETFs that were trading on 3/1/05. That is not the result you want; your list may even come in at zero. That’s why it’s essential that you use the Close(500) test to confirm the existence of a reasonable number of ETFs back in 2/07, but then, eliminate the rule so as to make sure all those ETFs are available to the historic 3/1/07 screen that will be created in the backtester.
The Kinds Of Rules You Can Create
Challenges loom large as an important reason why fund screening in general and ETF screening in particular has not gained nearly as much of a following as is the case with stock screening. Generally speaking, the main factor categories relate to taxonomy (asset class, region, style, etc.) and past price performance.
As explained elsewhere in this Help section, we see the Portfolio123 taxonomy as a major strength for our ETF screener based both on relevance (offering a range of choices that realistically reflects the kinds of ETFs that are actually available to investors) and flexibility (allowing you to slice and dice the universe however you wish).
We also believe the way we use price (and volume) data is another plus.
As with other ETF screeners, you can look at past 1-month performance Close(0)/Close(20), past 3-month performance, Close(0)/Close(60), etc. We can use FRank to translate all these to percentile ranks, and percentile ranks can be computed relative to sector, style, etc.
But we go much further, We have the entire suite of technical analysis functions. Other ETF screeners are not likely to feature moving averages, gap up and down, crossover, MACD, oscillators, and so forth.
The Size Of A Result Set
ETFs and stocks are different animals. It certainly is possible for you to get result sets comparable to what many see with stock screens (10-30 companies), but in the context of ETFs, it’s not clear how constructive that is. Remember, each ETF is, in and of itself, a diversified portfolio. So from the standpoint of basic risk control, putting all of your money into a single ETF can be perfectly fine.
To the extent any of us would like to hold more than one ETF, we’re really dealing with a different kind of risk, one that addresses strategic execution rather than financial optimization. If you want value stocks, there’s more than one way to identify these. Diversifying among value ETFs lets you avoid the risk of choosing the one fund whose implementation of this style is least effective.
The broader the custom universe you choose, the larger the number of ETFs you could reasonably hold in an effort to diversify your exposure to execution risk. As the universe grows, you also start diversifying against top-down risk (i.e. the risk that U.S stocks may underperform U.S. fixed income).
But depending on the nature of the universe form which you draw, the bigger the portfolio, the harder it may be for you to get a meaningful backtest as you rely more on more on newly issued ETFs.
We suggest starting ETF portfolios that consist of three to five names. For most strategies, that can provide ample risk-control. Go higher if and when, and to the extent necessitated by a particular custom universe you create.
How Many Screening Rules?
With stocks, some Portfolio123 users create many rules in an effort to have the screen produce as optimal a result set as possible. Others use few rules relying instead on ranking or quick ranks to narrow the list.
ETF users have the same choices (the full Portfolio123 ranking capabilities will be available soon, but quick rank can be used immediately).
The difference comes from the size of the universe. The total ETF universe presently consists of less than 800 funds. Those who use custom universes will have even smaller starting points.
The size of the likely ETF result set therefore becomes a simple matter of numbers. With ETFs, your result set will decline to zero a lot more quickly than is the case with stocks. So be prepared to use fewer rules than you would in a stock screen.
Also relevant is the role of custom ETF universes. These play a very large role in the success of a particular ETF strategy.
On the whole, don’t be at all surprised if you find that a well-created custom universe combined with a quick rank and even a single screening rule, or even a quick rank alone, can serve you well, better than you might expect from stock screening.
If you find yourself using more rule and getting good results, by all means continue doing that. The point, here, is that you should not feel you’re doing anything wrong, or that you’re missing out on anything, if you find yourself using fewer rules than you use with stocks.
Benchmarking Considerations
From our work with stocks, we’ve become well accustomed to benchmarking our test results against the S&P 500 or some other well-known market index. We do that with ETFs as well.
But with ETFs, there’s an added layer of analysis. We need to benchmark our screens against our custom universes. If, for example, you work with a Custom Universe that, as a whole, has substantially underperformed the S&P 500, it may not be reasonable for you to expect to produce a screen that generates market-beating results.
Whether your screen is above the market, or below, it’s important that you know how much of the strength or weakness is the result of your screen and how much is the result of your universe. This is not just a matter of curiosity. If you are not satisfied with the overall result, you need to know where to focus your improvement effort. Portflio123 users are accustomed to doing such work with the screen. With ETFs, it may turn out that the screen is excellent but you may need to reassess your top-down (universe creation) decisions.
When assessing a strategy, it’s probably a good idea to start by setting the universe, and then backtesting all stocks that make a Close(0)>=0 screen. Download the results to a spreadsheet and refer this universe backtest as you test your complete screen.
Market Timing – Going Short
As you’ve seen elsewhere on the site, we have introduced our own market timing model and show you how you can use the pertinent data sets to create your own version of a timing model. Up till now, timing was used mainly to decide whether to be in cash or stocks, and we recently introduced a regime-switching model that goes back and forth between two stock universes depending on market conditions.
Introduction of ETFs brings a new dimension to market-timing efforts. You can use market timing rules to adjust your custom universe to switch between stocks and fixed income, or between long and short ETFs.
The key to doing something like this is the new set of functions under the Groupings category. These allow you to establish all custom universe inclusions and/or exclusions via screening rules, in lieu of (or in addition to) the Custom ETF Universe interface.
Here, for example , is how you might use timing rules to switch your universe back and forth between the ProShares long or short ETFs based on market conditions.
ETFFamilySet(PROSH)=1
ETFMethodSet(LEVSHORT,STANSHORT,LEVLONG,STANLONG)=1
Eval(sma(5,0,#SPEPSCY)>=sma(21,0,#SPEPSCY) and close(0,#SPRP)>=1,ETFMethodSet(LEVSHORT,STANSHORT)=0,Close(0)>=0)
Eval(sma(5,0,#SPEPSCY) ................
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