Chapter 3 - ETF Screening

Much of what you already know and do in the Portfolio123 stock screener is applicable to ETF screening as well.

 

• The Free-Form rule-construction interface is the same.
• The Backtesting interface is the same.

 

For more details on how rule construction and backtesting work, please take a look at chapter 1.

 

There are some key differences:

 

• Fundamental criteria analysis (valuation metrics, growth rates, balance sheets, etc.) are inapplicable so the numeric aspects of your models must be created using price- and volume-based factors (such as technical analysis)
• Unlike stock screens, which can use half dozen or more numeric rules, ETF screens will often wind up using just one or two such rules, and perhaps even none (you may wind up doing a taxonomy-based screen – see below – and using numeric rules only for a Ranking System or a Quick Rank)
• Taxonomy-based rules, often of minor significance in equity screening (industry, index membership, etc.), are likely to be dominant in ETF screening
• ETF result sets are likely to be much smaller than is the case for equities. With the latter, 10, 20 or more stocks may be considered desirable. For ETFs, five or fewer choices, perhaps even one, will usually be preferable.
• Establishment of backtest settings and interpretation of results must be approached with more of an artful spirit, since the ETF universe has expanded so much, both in number and variety of offerings, since 3/31/01, the earliest backtest date we use. Sometimes, clicking on the “Max” link will result in a prolonged period of meaningless results, as would be the case if you screened for leveraged ETFs, which didn’t exist until mid-2006. Other times, a “Max” backtest will be numerically meaningful but analytically challenging as might be the case if, for example, you screened using all equity ETFs (the nature of the universe of equity ETFs was considerably less varied, perhaps even blander, before 2005; when you interpret your results, you’ll have to keep in mind the larger palette from which you’ve been painting, so to speak, in latter years).

 

All of the differences are important and need to be kept in mind. Ultimately, however, there is one over-arching difference mastery of which will make it easy for you to create effective ETF screens: taxonomy-based rules. Once you get the hang of that, everything else will fall into place and you’ll have a very satisfying ETF screening experience.

 

Preliminary Review

 

Everything we do with ETFs involves the Free Form interface. Please make sure you are comfortable with it. If not, you may wish to review Part 2 of this tutorial, focusing on the discussions of interface and functions.

 

As to numeric rules, these are the same whether for stocks or ETFs and are covered in Part 3.

 

This Part of the Portfolio123 tutorial will be devoted to taxonomy-based ETF screening. Three topics will be covered: (i) a quick review of the Groupings functions that are used to articulate ETF taxonomy rules, (ii) explanations of the taxonomy classifications used by Portfolio123 and illustrations of how they can be used to build screens, and (iii) consideration of how taxonomy-based screens can be enhanced by numeric rules or rankings.

 

Keeping Informed

 

If you are serious about ETFs, it would be a good idea to find and regularly consult information sources that are most likely to provide sound insights. Well established investment media outlets, including the most popular web portals, are not nearly as sound in ETF coverage as their stature and branding might suggest.

 

Start with the ETF section of SeekingAlpha.com. The good ETF sites, those that really understand where the ETF industry has come, where it is, and where it’s probably going, tend to syndicate their articles on Seeking Alpha. You’ll quickly learn who’s who in ETF coverage and which commentators/sites you’d like to follow.

 

 

 

ETF Screening: The GROUPINGS Category of Functions

 

How annoying is it to go to an ETF screener and find that you can screen on Equities or Fixed Income, but that you can’t distinguish between large- or small-cap equities.

 

How annoying is it to go to a different ETF screener and find that you can screen on ETFs specializing in small-cap growth stocks, small-cap value stocks, mid-cap growth stocks, etc. But that you can’t screen on value in general, small cap in general, small and mid cap but not large, all kinds of value ETFs, etc.

 

This should resonate with anyone who has tried to screen for ETFs. Some platforms force you to define your ETF world in very general terms. Others force you to get very, often overly, specific.

 

Portfolio123 is different. You can be as general as you want. You can be as specific as you want. You can be as generic as you want. You can be as idiosyncratic as you want. Summing up, you can define your ETF world however you want.

 

 

You’ll notice that our ETF taxonomy includes quite a few classifications, some of which are visible above and others of which could be seen by scrolling down within in the Factor/Function window. Here are the ETF-specific classifications, or groupings:

 

• Asset Class

 

Equities, Currencies, Commodities, Fixed Income, Mixed Assets (which have become more popular lately, as some new ETFs try to imitate hedge funds), and Alternative (which had one ETF that folded, but we’re still keeping the category to accommodate the increasing creativity of ETF sponsors)

 

(Note; These and the items below are plain English references. Look at the Full Descriptions for the abbreviations you would type into Portfolio123)

 

• Country

 

You can choose among individual countries or multi-country.

 

• Family

 

iShares, PowerShares, ProShares, Direxion, Rydex, SPDR, Claymore, Vanguard, Wisdom Tree, etc., etc., etc.

 

• Method

 

Standard Long (once upon a time, this was the only method in use by ETFs), Standard Short, Leveraged Long, Leveraged Short, Hedged, Quant Model (PowerShares Dynamic XTFs and those that followed), Special Weights (Wisdom Tree, and those that followed)

 

• Region

 

ASIA, BRIC-Chindia (Brazil, Russia India and China, or just China and India), Developed, Emerging, Europe, Global (note that many of these ETFs have big stakes in the U.S.), Latin America, Mideast-Africa, North America, Pacific ex Japan

• Sector

 

We have the usual ones you’d expect for equities: Consumer, Energy, Financial, Healthcare, Housing, Industrials, Materials, Real Estate, Resources (General), Services, Technology, Telecomm, Transportation and Utilities

 

We also have some you might not expect: Agriculture, Alternative Energy, Infrastructure, Social (for socially conscious ETFs), Special Theme (this where we see a lot of creative ETFs, such some launched by claymore), Precious Metals, Timber and Water.

 

For fixed income, we have Municipal and Taxable.

 

We also have General Sector for when you want to eliminate sector-specialist ETFs.

 

• Size

 

We have Large-Mega Cap, Mid Cap, and Small-Micro Cap.

 

You can specify General if you want ETFs that don’t specialize.

 

 

• Style

 

For those who specifically want to exclude specialist ETFs, we have General.

 

For Equity ETFs, we have Equity Income, Growth, or Value

 

For fixed Income, we have General Fixed Income, High Yield, Short-term, Intermediate-term or Long-Term

 

You don’t always have to use these groupings. When you start a new ETF screen, your default universe consists of All ETFs. Use the groupings functions only to the extent you want to limit.

 

There are two ways to work with groupings, the basic functions or the set functions.

 

If you want include or exclude just one item from any particular collection, here are some examples:

 

ETFAssetClass = equity

 

ETFassetClass != equity

(Note that != means not equal to)

 

ETFMethod = stanlong

 

If you want to work with some items from a particular collection, but not necessarily all, use the set functions.

 

ETFMethodSet(stanshort, levshort)=true

 

ETFRegionSet(BRIC,Chindia,Pacific)=true

 

ETFMethodSet(levlong,levshort,stanshort)=false

 

You can mix and match very creatively by using screens containing several groupings functions. For example:

 

ETFAssetClassSet(equity,curr) = true

ETFCountry!=usa

ETFRegion!=global

 

This will give you a list of non-U.S. ETFs that specialize in equities or currencies.

 

ETFAssetClassSet(equity,commod)=true

ETFSector=agric

 

This will identify agriculture ETFs, whether they focus on agribusiness equities or commodity futures.

 

ETFCountry=USA

ETFAssetClass=equity

ETFMethod=stanlong

ETFSectorSet(financial, housing, realest)

 

Often, you’ll you can build many ETF screens using only the GROUPINGS functions in the manner illustrated above. Then, choose only the top ETFs based on your quick-sort or a ranking system included in Portfolio123. According to a Portfolio123 backtest, the last set of functions, if used as a complete screen and the Risk-Reward Aggressive ETF ranking system,  would have been a nice way to bottom fish among ETFs that got hammered by the financial crisis. Over a one-year period ending in mid-March of 2010, a portfolio consisting of the top 3 ETFs thusly selected would have risen 95.8%,versus 80.8% for the S&P 500, assuming four-week rebalancing.

 

Finally, assuming you have some extremely idiosyncratic ways you’d like to define the world of ETFs and that even these functions don’t allow you to fine-tune as tightly as you wish. In that case, you could create custom list using tickers you hand select. There will be occasions when this can be useful and we will cover Lists in a later portion of this how-to series.

 

 

 

Reviewing the GROUPINGS Category Of Functions

 

We’ll keep it simple for the purpose of reviewing the functions and focus on only one aspect of the taxonomy, Asset Class.

 

There are two relevant functions, ETFAssetClass, and ETFAssetClassSet.

 

Note the ‘Full Desc” link all the way over on the right. As with all functions, it’s important to use these. For our present example, Full Desc (which stands for Full Description) will tell us what asset class choices we have. Here is the window that opens when you click on the link.

 

 

As emphasized in Chapter 1, there is no virtue to trying to memorize the acronyms. Get comfortable using these Full Description windows.

 

Suppose we want an equity only screen.

 

To choose one item from within a particular portion of the taxonomy, use the function that follows the ETFXxxx format, in this case, ETFAssetClass (by the way, this is not case sensitive;  you could also say etfassetclass).

 

Here’s how your rule would look.

 

 

That’s it. Now you’re ready to move onto other things if you like (region, country, style, etc.).

 

Suppose you want several items from the taxonomy:

 

 

Notice the numbers on the right. Those appear when I clicked on Totals. Since ETF taxonomy screens do tend to rely more than equity screens on typing, I click on totals after every line. When I see a number –  as opposed to an error message – I know there are no typos.

 

 

 

Speaking of false, suppose you want all asset classes except for fixed income. This can be done in a variety of ways.

 

 

Note: Use != to indicate not equal to.

 

 

Practice creating a variety of asset class groupings. Once you’ve mastered these two types of GROUPINGS functions, you’re ready for “the good stuff;” the different aspects of the Portfolio123 ETF taxonomy.

 

The Portfolio123 ETF Taxonomy

 

Taxonomy screening exists for stocks, but it’s often not that big a deal. Many screens do not include rules governing Industry or sector membership, presence or absence in an index like the S&P 500, etc. The most common taxonomy might be the NOOTC universe, or something like it, as part of a preliminary trading-liquidity-oriented rule.

 

With ETFs, taxonomy can be vital.

 

Consider for example, the plight of one who wants to screen for ETFs that invest in U.S. equities and are not specialized in a single sector and are neither leveraged nor short. Some ETF screeners force you to simply accept all stock ETFs or all bond ETFs. That won’t do. Other ETF screeners may force you to say you want large-cap value stocks or with small-cap value stocks or with mid-cap growth stocks, etc., but won’t let you work with just plain stocks or combinations of sub-groupings. That won’t do either.

 

ETF investors may not talk about taxonomy as glowingly as equity investors talk about their favorite fundamental or technical concepts. But they should never underestimate the importance of a strong taxonomy. In many cases, the inability of a screener to break down a taxonomy the way one wants will render a platform unusable.

 

The Portfolio123 taxonomy categories are as follows:

 

• Family
• Asset Class
• Region
• Country
• Method
• Style
• Size
• Sector

 

We recommend that you use this as a roadmap for building your ETF screens. When you start a new screen, you start out with all ETFs. To narrow down, go through each taxonomy category one at a time and decide what, if anything, you want to do in that area.

 

“Nothing” is a perfectly acceptable answer. If you are open to all ETF families, then skip the category and move to Asset class, the next one on the list. Just make a habit of pausing, even if only for a few seconds, on each category. You’ll be amazed at how often ideas come to mind that you might otherwise have missed.

 

Let’s now consider all the categories.

 

Taxonomy Category: Family

 

As noted, many users will have no inclination to add any filters that address this. But some will. Experienced ETF investors do think in terms of brand differences.

 

Some investment advisors, for example, may want to confine themselves to iShares and, perhaps, the SPDR family of ETFs, and maybe Vanguard as well. 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). Special situations investors might want to zero in on the Claymore product line. Wisdom Tree is a class almost unto itself; some may want to focus entirely on it; others may want to filter it out.

 

Are any of these ETF brands unfamiliar? If so, that would underscore the importance of going to SeekingAlpha.com and developing a new set of preferences and habits regarding the way to keep up with the world of ETFs. (For now, though, we’ll cover several of them later as we get to aspects of the taxonomy where they make their respective marks).

 

As with all the other categories, you can ignore this one, you can choose one family, or you can mix and match as you wish.

 

Taxonomy Category: Asset Class

 

This sounds so easy: stocks or bonds.

 

That used to be so. Now, however, as the ETF industry has spread its wings, giving us more classes to consider. Here are the ones in the Portfolio123 ETF screener:

 

• 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 screen that includes commodities and relevant stock sector ETFs if they so wish.

 

By the way, do you know what contango and backwardation mean? Be careful about investing in commodity ETFs until you do. There’s plenty of information out there; just Google the phrases.

 

For starters, contango refers to a tendency of futures contracts to be priced above “fair value.” At the point of expiration, the contract price and market price must be equal. So when contango is present, there will be gradual downward pressure on the prices of the futures as we get closer to expiration.

 

Backwardation is the reverse; a situation where the futures are priced blow fair value. Here, there is upward pressure on the futures prices as expiration nears.

 

None of this is as simple as it seems. The downward bias of contango and the upward bias of backwardation need to be balanced against ongoing changes in the commodity’s spot price. (Arguably, contango might reflect accurate expectations of a rise in the spot price and backwardation might reflect expectations of decline.) If this sounds complicated, it is. Those who create investing/indexation protocols for use by ETFs live every day with, and even have nightmares about, issues like these.

 

For you, however, it might actually be easier. Just view the price trend of a commodity ETF as a phenomenon in and of itself that reflects a blend of spot price, contango/backwardation and expectations all rolled up into one.

 

You can create rules around the share price trends. It is not necessary that you unravel the factors underneath the trends. Just know that they are there. So if, for example, oil ETFs are failing your technical tests even though oil is rising, don’t assume you’re doing anything wrong. It may be the impact of contango on the futures held by the ETFs. Just accept the ETF price trend for what it is; put aside any preconceptions you might have.

 

• 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, was not well represented in the early days of the development of ETFs. Now, ETF offerings 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 new “target-maturity” trend, which aims to see fixed income grow as the ETF approaches a planned liquidation date.

 

Taxonomy 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.

 

Taxonomy Category: 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. Now, however, we have some stark choices and it’s critical that you address this, one way or another, in each ETF screen you create. If you backtest an ETF screen and determine that your results are a mess, check to see what you did in terms of ETFMethod or ETFMethodSet. You’d be amazed at how many problems can be fixed with more attention, TLC perhaps, to this area.

 

Here are the 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 four ETFs in this group. 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.

 

These can be incredibly useful products, but they are also incredibly controversial and misunderstood. This is one reason why it’s vital that you look for sources of commentary that really understand ETFs, something that is not the case with much of the mainstream financial media.

 

It’s especially important for anyone working with these ETFs to recognize that the doubling (or tripling in some cases now) 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 unexpected results depending on the daily pattern of ups and downs, more 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 IndexUniverse.com and in the ETF section of SeekingAlpha.com ETF section.

 

• Leveraged Short

 

These work like the Leveraged Long ETFs but the portfolios go short, directly and/or via use of derivatives.

 

These became huge headline-grabbers during the recession 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. It can also be a lot of fun to use the backtester to compare the performance of quant ETFs to those of your own Portfolio123 stock models.

 

• 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 the 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 (or as a former colleague once quipped, “God’s investment portfolio”). 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 God’s-portfolio approach. 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 quietly nudging their way into 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.). Robert Arnott, the inventor and primary spokesman for this category, is a prolific writer and speaker. You can find many of his papers through IndexUniverse.com (one of the new breed ETF sites whose content you’ll find on Seeking Alpha).

 

As of this writing, the Special Weights Method also includes some Rydex equally-weighted ETFs, and some by RevenueShares, that weight on the basis of, as you might guess, revenues.

 

Because of the nebulous and often subjective relationship between these ETFs 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 securities 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.

 

 

Taxonomy 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 term, 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. There’s also a General category. Use it to eliminate the sector specialists.

 

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.

 

 

Putting The Taxonomy Together In A Screen

 

By the time you have gone through all the categories addressing the ones that interest you and ignoring those that don’t, you’ll find that most of your screening work has been accomplished. You may want to add a rule addressed to trading liquidity. Beyond that, you may need one or two at the most to filter based on price trends. Add in a Ranking system or Quick Rank, select the top1-5 ETFs, and you’re done.

 

This may differ from other ETF screening experiences you’ve had. Elsewhere, ETF screeners tend to be modeled along the lines of stock screeners; light on taxonomy and long on numeric tests (which, in the case of ETFs, would typically be based on mainly on price trends).

 

You certainly can replicate such screens on Portfolio123 (actually, we probably give you considerably more flexibility regarding technical analysis, price trends, etc.). Try it that way. Often, though, you’ll not be satisfied with your lists because of the presence of the kinds of ETFs you didn’t really want to see.

 

You don’t have to sacrifice numeric rules. You can have all you want. Ultimately, though, we believe thoughtful decisions regarding taxonomy rules (their inclusion or omission) are what will make the results you get here most relevant to your needs.

 

Here are some sample taxonomy rule sets (in the next section, we’ll combine them with numbers to wind up with complete models).

 

 

Deconstructing The Stock Market

 

Often, as soon as the word “fund” is uttered, thoughts regarding style immediately rush to mind: growth, value, large-cap, etc. The traditional open-end mutual fund industry has long been sliced and diced this way and it’s only natural for the ETFs to follow suit.

 

You don’t absolutely have to think along these lines, as you’ve seen from the above examples. But you can do it if you want. Here’s an example:

 

ETFAssetClass=equity

 

ETFCountry=USA

 

ETFMethod=StanLong

 

ETFStyleSet(value,eqincome)=1

 

 

If you’re interested in size-based categorization, you can do something like this:

 

ETFAssetClass=equity

 

ETFCountry=USA

 

ETFMethod=StanLong

 

ETFSizeSet(midcap,smallcap)=1

 

 

Needless to say, you can combine style- and size-based preferences:

 

ETFAssetClass=equity

 

ETFCountry=USA

 

ETFMethod=StanLong

 

ETFStyle= growth

 

ETFSize=largcap

 

As usual, you need not stick with the StanLong method. More and more short as well as style- and size-based leveraged ETFs are becoming available.

 

 

Deconstructing The Bond Market

 

ETFs got off the ground in a big way with stocks. It would be an overstatement to say now that fixed-income ETFs have caught up, but the area is growing and it has already become significant, enough so to allow fixed income strategies and blended strategies (involving equities and fixed income) to be implemented entirely through ETFs. Part of that comes from the number of fixed income ETFS available. Also, on Portfolio123, you can slice and dice this area just as effectively as you can with equities.

 

Here’s an example:

 

ETFAssetClass=FixInc

 

ETFMethod=StanLong

 

ETFSector=TXFIXINC

 

ETFStyleSet(INTFIXINC,LTFIXINC)

 

These rules will produce a list of domestic taxable fixed income ETFs that emphasize intermediate- and long-term maturities. Under style, you could also have opted for short term or  high yield (i.e. junk bond). We saw above that under sector, you could also have chosen municipal bonds.

 

Fixed-income investors should note that all ETF prices used in Portfolio123 are adjusted for dividends paid by the funds. So any percent changes you compute will, actually, reflect total return (price change for the ETF itself plus the impact of income received).

 

Traveling Abroad

 

This is a rapidly growing area of ETF issuance with many country-specific and regional funds now available and more continuing to be issued. To date, there aren’t yet many non-US fixed income ETFs, but foreign stock funds are plentiful.

 

You can categorize by country using the ETFCountry or ETFCountrySet functions. Here are some examples:

 

ETFCountry=Mexico

 

ETFCountrySet(Mexico,Brazil,Chile)=1

 

Some are interested in building models geared toward country rotation. (If that would be the case for the above examples, one would need to think carefully about the list. For example, is the omission of Argentina from the CountrySet rule intentional or inadvertent?)

 

But many investors who want non-US ETFs don’t have a single country in mind and are instead, thinking in terms of some sort of international theme. In that case, the country-oriented functions are not necessarily the way to go. The above examples, for instance, most likely reflect an interest in Latin America or emerging markets as a whole. So it would be much more efficient to implement the approach using one of these rules:

 

ETFRegionSet(Latin,Emerg,BRIC)

 

ETFRegion=Latin

 

ETFRegion=Emerg

 

ETFRegion=BRIC

 

One thing you need to be aware of is the nature of ETFs categorized as Global. These portfolios really and truly are global, meaning they include U.S. equities. Since the U.S. is the premier world stock market, U.S. stocks can be heavily weighted in the portfolios.

 

If you want a broad-based international flavor excluding U.S., you might want to try a screen like this:

 

ETFAssetClass=equity

 

ETFRegionSet(namerica,global)=0 and ETFcountry!=USA

 

ETFMethod=stanlong

 

By the way, leveraged and short ETFs have lately been issued in increasing number with focus outside the U.S. So don’t forget to make a decision, one way or the other, on how you want to handle Method.

 

Building A Complete ETF Model

Taxonomy-based screening rules as presented in the preceding section can take you quite far. Indeed, many who approach ETF screening with very specific goals will be able to stop right there and eyeball the lists they’ve created.

 

Others who are not tied to specific goals or who would like to try as best they can to refine their choices within the constraints imposed upon them by their goals can add numeric criteria to their taxonomy-based rule sets. There are two ways to do this. One is to simply add one or more additional screening rules. The other is to use rankings.

 

In all cases, however, the screen should include a liquidity rule. For most investors, it need not be a stringent one, just something that will be sufficient to indicate that you will be able to buy and sell when you need to on terms that are no worse than tolerable.

 

Liquidity

 

For most who aren’t active traders, something simple like AvgVol(60)>=25000 should suffice. (This means average daily volume over the past 60 days was at least 25,000 shares.)

 

Needless to say, if you don’t like the 25000 number, you can adjust it.

 

When you deal with liquidity, there is one important tradeoff you need to keep in mind. The more interested you are in exotic ETFs (hedged, alternate asset classes, and so forth), the more lenient you’ll need to be when it comes to liquidity filtering since many of these ETFs are not heavily traded.

 

Numeric Screening Rules

 

Additional screening rules can look like the basic performance-based tallies such as you see elsewhere. Here, for example, is what a complete model seeking international ETFs that performed better than most in the past six months might look like.

 

AvgVol(60)>=25000

 

ETFAssetClass=equity

 

ETFRegionSet(namerica,global)=0 or ETFcountry!=usa

 

ETFMethod=stanlong

 

FRank("close(0)/close(180)",#all,#desc,#previous)>=90

 

The most interesting rule is the last one, which uses the Frank function to compare each ETF to the others. This function is discussed in Part 4 of this series, on page 7.

 

The last rule evaluates ETFs based on share price performance over approximately six months (180 days). Specifically close(0)/close(180) means the latest closing share price divided by the price 180 days ago. It limits results to ETFs whose percentile scores were at least 90; in other words, the ETFs have to rank in the top 10%.

 

The other parameters are as follows:

 

#all, the second parameter, tells the screener to consider the 180-day share price change for all ETFs. You could substitute any of the taxonomy categories if you wish. For example if you say #AssetClass, you’d be comparing equity ETFs only to other Equity ETFs. Check the Full Description of the Frank function for the list of choices.

 

#desc, the third parameter, tells the screener to do a descending sort; i.e. to assume that higher numbers are better.

 

#previous, the fourth third parameter, is something we didn’t discuss in Part 4. It can be very useful for ETFs where sorts based on the entire universe can be less meaningful since so many ETF sub-groups are so different from others. With this screen, for example, do you really want to compare the performance of these global ETFs to others that use short or leveraged strategies, to municipal bond funds, etc. By specifying #previous, the screener will work only with those ETFs that pass the screen up through and including the previous rule.

 

The disadvantage of using Frank is that you can’t be sure of the exact number of ETFs that will pass your screen. As of this writing, the above screen produced 10 ETFs. For stocks, that would be a very manageable number. For ETFs, the list may be too big.

 

The FOrder function can solve this problem. It is similar to Frank, but looks for a rank order position rather than a percentile score.

 

Here’s how the last rule of this sample screen might be re-written:

 

 

FOrder("close(0)/close(180)",#all,#desc,#previous)<=3

 

Now, instead of seeing however many ETFs maket the top 10% based on 180-day share price change, we’ll just see the top three.

 

Actually, the list produced by the above screen could just as easily have been accomplished by adding a rank to a screen (and it would have been simpler to accomplish). It’s only worth it to bother using screening rules if you want more than one relative test, as in the example below.

 

AvgVol(60)>=25000

 

ETFAssetClass=equity

 

ETFRegionSet(namerica,global)=0 or ETFcountry!=usa

 

ETFMethod=stanlong

 

FRank("close(0)/close(180)",#all,#desc,#previous)>=80

 

FOrder("close(0)/close(20)",#all,#desc,#previous)<=3

 

Here, we’re looking for international ETFs whose 180-day share price performance ranked in top 20% (the 80^th percentile or better). From among the 19 ETFs that pass, we select only the top three based on twenty-day performance.

 

This is all well and good, and as of this writing, all variations outperformed the S&P 500 in one-year backtests (assuming four-week rebalancing).

 

Actually, though, you can do a lot more. All the power contained in the Portfolio123 price/volume, technical, and mathematical functions are available for use in ETF screening. Consider, therefore, the following variation on the above example:

 

AvgVol(60)>=25000

 

ETFAssetClass=equity

 

ETFRegionSet(namerica,global)=0 or ETFcountry!=usa

 

ETFMethod=stanlong

 

CrossOver(#sma,10,5,20)

 

FOrder("close(0)/close(90)",#all,#desc,#previous)<=3

 

The next to last rule seeks ETFs for which the 5-day simple moving average crossed above the 20-day average at some point within the last 10 days.

 

Ranking

 

Notice that the last rule in each of the above samples is designed solely to get the final list down to a reasonable number of ETFs.

 

If you have only one sort criterion in mind, there’s an easier way to accomplish this result. You don’t use a Quick Rank.

 

You could, therefore, do something like this to produce a screen that, as of this writing, showed 19 passing ETFs.

 

AvgVol(60)>=25000

 

ETFAssetClass=equity

 

ETFRegionSet(namerica,global)=0 or ETFcountry!=usa

 

ETFMethod=stanlong

 

FRank("close(0)/close(180)",#all,#desc,#previous)>=80

 

Then, you could go into Settings and use 20-day share price change to narrow down to three ETFs.

 

 

If you’d like a more complex sort criterion, you could use a full-blown ranking system in lieu of a Quick Rank.

 

Below, the 19 passing stocks are sorted, and the top three selected, based upon the pre-defined ranking system known as “ETF Rotation – Basic.”

 

 

This ranking system is calculated on the basis of three factors:

 

• The five-day change in the share price (factor weighting: 70%)
• The 120-day change in the share price (factor weighting: 15%)
• The Share Ratio computed over the course of one year (factor weighting: 15%)

 

Since Ranking Systems and Quick Ranks allow you to precisely control the number of ETFs that will pass your model, many will find it effective to screen using only liquidity- and taxonomy-based rules and then go directly into ranking to finalize the lists.