Finding Investment Ideas By Screening Stocks and ETFs Marc H. - - PowerPoint PPT Presentation

Finding Investment Ideas By Screening Stocks and ETFs

Marc H. Gerstein mgerstein@yahoo.com

June 14, 2008

About me

• Joined Market Guide in February 1999 as director of investment research.

Market Guide was acquired by Multex, which in turn was acquired by Reuters.

• Worked as a securities analyst since 1980 and has analyzed stocks across a

wide variety of industries and sectors, including household products, specialty retail, restaurant, mining, energy, hotel/gaming, homebuilding, airlines, railroads, and media.

• Managed a high-yield (“junk”) bond mutual fund during the 1980s.
• Created, managed and authored much of the Ideas & Screening section of

Reuters.com from 2003-06, which provided analysis and stock selection strategies to investors.

• Designed indexes in anticipation of use for ETFs
• Author of two books, Screening the Market and The Value Connection, and

appears periodically on CNBC,USA Today, CBS.MarketWatch, The Wall Street Journal, Money Online, and The New York Daily News

• Has an MBA in finance from New York University, a JD from Brooklyn Law

School, and a BA in social science from Ohio State University.

Today’s Agenda

• Stocks
• Finding ideas using rules-based protocols
• Screening
• Ranking
• Testing your ideas
• Evaluating individual situations
• Exchange Traded Funds (ETFs)
• Making sense of the jumble
• Finding ideas
• Simple screening
• Interpreting available information

Stocks

Rules: What We Are Trying To Do

• We want to create mathematical expressions of concepts that are well

established as sound stock selection criteria

• The key is the phrase “well established”
• All of out rules should be consistent with good common sense
• We are not trying to turn the world upside down with exotic notions
• Start with sensible verbal expressions: Look for …
• Shares of companies that are growing briskly
• Reasonably priced stocks
• Financially sound companies
• Translate them to mathematical expressions: Consider stocks if …
• Trailing 12 Month EPS growth rate is greater than 15 and above industry average
• P/E less than 25 and less than industry average
• Latest long term debt ratio and trailing 12 month return on equity above industry

average

Benefits Of Using Fundamental Rules – 1

• See past market buzz
• “If I could avoid a single stock, it would be the hottest stock in

the hottest industry, the one that gets the most favorable publicity, the one that every investor hears about in the car pool

• r on the commuter train — and succumbing to social pressure,
• ften buys.”
• Peter Lynch, Once Up On Wall Street, Chapter 9

Benefits Of Using Fundamental Rules – 2

• Focus us on merit
• “Stock screening is absolutely, positively the best way to find

investment ideas…. No other approach can match screening when it comes to calling stocks to your attention based on at least some objective showing of merit….”

• Page 1, Screening The Market: A Four-Step Method to Find,

Analyze, Buy and Sell Stocks by Marc H. Gerstein, Director of Investment Research, Multex (John Wiley & Sons, July 2002)

Benefits Of Using Fundamental Rules – 3

• Effectively Articulate subtle and promising numerical stories
• Example

Operating Margin comparison ABC Co Industry Average Trailing 12 Months (TTM) 8.6% 8.8% 5-year average 9.0% 11.5% Even though ABC’s margins are slipping and were consistently below the peer average, we see that ABC’s “relative comparison” has become less unfavorable. This may reflect something uniquely positive at ABC We can design and test rules that capture situations like this

Benefits Of Using Fundamental Rules – 4

• De-emphasize popular but generally unanswerable lines of inquiry
• We do not chase vague (and often impossible to solve) puzzles about

management talent, customers, suppliers, employees, proprietary technologies, and so forth

• Few, if any, outside investors can reliably and consistently assess such

issues based on publicly available information.

• Notice how Wall Street tends to praise or criticize management based not
• n inherent talent but on who well they “delivered” in the latest period
• Notice how dramatically stocks move in response to estimate revision and

earnings surprise, analysis of which has grown to become a significant sub-industry

• All based on the inability of highly trained Wall Street analysts to translate efforts with

such questions into reasonable earnings estimate even for the next three months

Types of Rules – Screens

• Stocks selected based on data-oriented tests
• All stocks in universe are rated “yes” or “no” depending
• n whether they pass or fail the complete set of tests
• In an 8,000 stock universe, we may see 40 passing stocks and

7,960 fails.

A Simple Screening Example

• Screening for Value
• Stocks pass the screen if . . .
• P/E is below industry average, and
• Price/Sales is below industry average, and
• Price/Book is below industry average

Pros and Cons of Screening

• Pro
• This is a good way to narrow an overly broad universe
• Con
• We cannot control the number of passing stocks we’ll

have

• All tests are considered to be of equal importance

Types of Rules – Ranks

• Based on tests similar to those used in screens
• But rather than seeking yes/no answers, ranks aim

to classify each stock in the universe on a best-to- worst scale

A Simple Ranking Example

• A Value Rank
• Rank all stocks in the universe from best to worst in terms of
• P/E
• Price/Sales
• Price/Book
• Calculate an overall value score based on
• 0.50 times P/E rank, plus
• 0.30 times Price/Sales rank, plus
• 0.20 times Price/Book rank
• Rank all companies from best to worst based on overall value score
• Determine, for example, that companies whose value scores are in

the top 10% are eligible for consideration

Pros and Cons of Ranking

• Pro
• This allows us to address every stock in the universe and decide in

advance how many passing stocks we will have.

• We can assign different levels of importance (weights) to each

criterion

• Con
• Used on its own, this technique may strain the capabilities of

statistical probability

• We may feel comfortable saying the top 10% of the universe is

better than the next 10%

• But we may hesitate to say stock number 25 is better than stock

number 26, that stock number 26 is better than stock number 27, and so forth

Combining screens and ranks

• This involves using a rank to identify a broad, appealing,

portion of the overall universe, and following up with a screen to make more precise selections from this “subset.”

• Continuing with the previous example, we would . . .
• Consider stocks that pass the value screen and have value ranks in

the top 10%, and

• Make our final selection by eligible choosing stocks with the 30

highest value ranks

• The previously mentioned probabilities strain is no longer troubling

since we also used a screen to narrow the universe

Criticisms against rules-based investing

• Rules are based on data from the past and we all know past performance is not

necessarily indicative of what is likely to occur in the future

• Rules cannot capture qualitative factors such as brand image, management

experience, economic “moats,” patents, competitors, etc.

• Rules often tend to contain systematic biases against certain kinds of

companies

• i.e. a heavily earnings-based screen is not likely to give a fair shake to biotech or

cable TV or real estate companies, or energy exploration companies whose merits depend on proved reserves

• Rules cannot capture every individual situation.
• All they can do is establish probabilities, which means we know we’ll have some

wrong answers (although we don’t know, in advance, which specific decisions will be the ones that go sour)

• Rules tend to be un-sexy
• Professional investors interviewed on TV tend to sound a lot less guru-like if they say

they picked their winners because of mathematical rules

Deconstructing rules critiques: past performance

• Assuming we ignore historic data and just look ahead, are we REALLY nearly

as good at forecasting as we like to think we are?

• As of 3/7/05, there were 3,945 estimates of quarterly EPS for the current

fiscal quarter

• Of these, 2,169 (55%) are equal to where they stood 4 weeks ago
• Only 1,084 (28%) still stand where they stood 8 weeks ago
• Only 851 (22%) still stand where they stood 13 weeks ago
• In other words, there’s a 78% probability that the life span of a near-term

earnings estimate, the group that get the most attention, will have a shelf life of less than three months

• There’s a reason why concepts such as earnings surprise and estimate revision

have become standard fare in the investment community

• Companies are more like ocean liners than rowboats
• They can turn in the opposite direction, but this tends to happen in gradually rather

than in an instant

• Evolutionary, rather than revolutionary change
• So rather than pretend the past is irrelevant, we may as well use it as constructively

as we can to help us make rational assumptions about the future

Deconstructing rules critiques: qualitative factors

• Are we REALLY as good at evaluating qualitative factors as we like to think we

are?

• Can we REALLY evaluate management (or do we find ourselves saying good

things about management teams that produce good numbers and vice versa)?

• Can we REALLY get good handles on patents, technologies, competition, etc.
• What’s the difference between an Intel chip and an AMC chip?
• Which oil company has better exploration prospects: Exxon Mobile or Chevron?
• What EXACTLY does Halliburton do?
• Who are GE’s competitors and how is GE better or worse?
• Tell me about Cisco’s patents
• What the heck is an economic moat? REALLY???
• Did you know that at the time his fame was spreading most rapidly, Warren Buffett,

an often-cited proponent of economic moats, had made an investment in McDonalds, which at the time was getting hammered by fast food rivals in a manner consistent with what one might expect from a zero-moat company?

Deconstructing rules critiques: biases

• Thu shalt not covet thy neighbor’s stocks!!!!!!!!!!!!!!!!!!!!
• If your system finds enough good stocks for you to enjoy strong investment

returns, don’t worry that somebody else with a different investment approach finds different winners

• If you’re winning in the market, why feel bad because you aren’t winning with, say,

biotech?

• There are a lot of great stocks out there, so it’s no sin to miss one
• You’re more likely to succeed if you define an area of proficiency, stick with

that, execute to the best of your ability, and let others do their thing with their stocks

Deconstructing rules critiques: being wrong

• If an idea goes bad, your worst-case scenario is -100%
• Realistically, even the most stubborn among us will cap losses in the -50% to -75%

range

• If an idea goes good, your best-case scenario is way more than +100%;

actually, there is no ceiling

• If, over the long term, you’re patient and extreme with your best and worst

ideas, and your probability of success equals what you’d get from a coin flip (50-50) . . .

• You can wind up with half your portfolio experiencing returns of -50% to -75% in a

bad scenario while the other half has unlimited upside

• We can’t say for sure, but it seems quite easy to envision many scenarios in which

the percentage gains on your good decisions will substantially exceed the losses from your bad decisions

• Under these circumstances, being right half the time could produce very positive results.
• Being right 55%-60% of the time might turn you into a major superstar
• So in actuality, in the right circumstances, being wrong can be a lot more

tolerable than is widely realized

A Designer Stock Market

• Rather than trying to get it perfect, we acknowledge that we still have to

make selections from a large stock universe and try to determine which

• nes are most likely to perform well
• What’s different is that in the designer market, you tilt the probabilities

in your favor even before you look at a single company or chart

• “Suppose a magician came along and offered to help you invest in stocks.

He refuses to do anything to improve your stock selection skills. So if you’re a good stock picker in the real world, you’ll stay good after he waves his magic wand. And if you’re a mediocre analyst, you’ll stay mediocre. But he does offer to let you choose whether the stock market goes up 20 percent

• r down 5 percent. I’ll be you’d accept the offer and choose a +20 percent
• market. You might still pick some duds. But wouldn’t you prefer to do your

thing in a bull market that rises 20 percent?”

• Marc Gerstein, Screening The Market (John Wiley & Sons, 2002) page 34

Deconstructing rules critiques: not sexy

• OK. I’ll concede this one

Categories Of Rules

• Growth
• Rules seeking favorable EPS growth rate comparisons
• Rules seeking favorable Sales growth rate comparisons
• Rules seeking Sales growth rates that are generally keeping pace with EPS growth
• If sales aren’t growing, EPS growth will probably stall; you can’t cut costs forever
• Quality
• Rules seeking favorable Margin comparisons
• Try to use Operating (or Gross) margin, rather than net margin
• Provides better visibility of day-to-day business activities, instead of a broad combination of

business activities and other non-core corporate endeavors

• Corporate endeavors are important, but I’d rather examine these when I look at a specific

company; if the basic business is bad, I don’t even want to bother looking so I don’t want it on my list

• Rules seeking favorable Return on Capital comparisons
• Try to use Return on Assets or Return on Investment, rather than Return on Equity
• Provides better visibility of day-to-day business activities, instead of a broad combination of

business activities and corporate finance strategies

• Finance strategies are important, but I’d rather examine these when I look at a specific company; if

the company is financially strained, I don’t even want to bother looking so I don’t want it on my list

• Continued on next slide 

Categories Of Rules - continued

• Value
• P/E reasonable compared to peer group
• P/E reasonable compared to growth rate
• The notion that PEG (P/E-to-Growth) ratio shouldn’t exceed 1.00 owes much to folkore and

nothing to mathematics

• PEG ratios as high as 2.00 are usually acceptable
• Other ratios
• Price/Sales, Price/Book Value, Price/Cash Flow
• Sentiment
• Analysts are more positive (ratings and/or estimates) than they were before
• Even if you don’t like or trust analysts, note what they say
• Many still follows them, so their pronouncements are still trend-makers and trend-busters
• Institutional buying/selling
• They’re big, so their decisions, sound or not, move stocks
• Insider buying
• Insider selling is less useful
• Short selling/covering
• Relative share price performance

Strategic System Design

• Elements of a system
• Primary theme
• This is the category of rules that is most important to you
• Secondary theme
• This is a category of rules that differs from, but is generally supportive of you primary

theme

• If your primary theme is growth, sentiment could be a secondary theme
• Alternative theme
• This is a category of rules that is completely unrelated to, and preferably antagonistic to,

your primary theme

• If your primary theme is growth, value could be an alternative theme
• If your primary theme is value, sentiment (i.e. analyst estimate revision) could be an alternative

theme

• The strategic blueprint
• Articulate a primary theme
• Add at least one additional theme (secondary or alternative)
• Add more themes if you like but don’t go overboard lest you drive your result total to

zero

“Behavioral” tests

• A common thread
• Sometimes, the numbers per se are interesting, but often, we are interested

in the trend (and strength of the trend as evidence that sentiment is changing for better or worse)

• Some useful tests
• Estimate Revision
• Changes in analyst recommendations
• Short interest data
• Institutional buying and selling
• Insider buying and selling

My favorite behavioral test

• The test
• Company share price change over the past 4 weeks > Industry Average

share change over the past 4 weeks

• The benefits
• Assume a stock’s 4 week change is -3%
• This alone gives little information
• Assume the S&P 500’s 4 week change is +5%
• Now, it looks like investors see something negative about this specific company
• Assume the industry average 4 week change is -8%
• Now, it looks like investors see something negative about this specific company
• Although the stock has been weak, investors who examined it and, presumably,

considered a variety of factors (past and future, objective and subjective, etc.) and as a result, saw reason to separate this stock from its peers and treat it more favorably

• This alone isn’t determinative, but it is consistent with the sort of situation we

search for when we screen

Real-World Implementation

• The challenges
• Transaction costs
• We need a way to narrow down from a final list that may be small

relative to the entire universe, but still larger than most investors can plausibly accept as is.

• Alternative solutions
• A narrowing-down routine
• A platform choice that makes it feasible to invest in an entire list

Platform Investing in an entire list

• Use a brokerage firm like FOLIOfn, which makes it cost effective to

invest in as many as 100 stocks at a time and re-balance the list frequently

• Use an application like Portfolio123 or Zacks Research Wizard that

contains back-testing capabilities that allow you to have greater confidence in the merits of the full list

• I prefer Portfolio123 because . . .
• in addition to back-testing (a feature both applications have in common),

Portfolio123 also offers multi-factor ranking (and testing capabilities),

• the ability to combine and test ranks and screens, and
• a simulation module that allows you to examine the impact of
• Transaction costs
• Price slippage
• Other buy-sell rules including stop losses
• Cost of Portfolio123
• $115/month for the highest (Gold) tier of service
• Most individuals would do fine with the Silver tier, at $49/month and some can

even use the Bronze tier at $29/month

A Portfolio123 demo

• We’ll create and test . . .
• A value-oriented screen
• A growth-oriented rank
• A combination that uses the value screen and all stocks with growth ranks of 80 or

above

• We’ll also do a simulation that . . .
• Uses the above-referenced screen-rank combination
• Rebalances every four weeks
• Assumes $10 per trade commission
• Assumes 0.5% price slippage
• Aims to identify about 25 stocks
• Prevents any single industry from being more than 15% of the3 portfolio
• Uses a stop loss to eliminate any stock that is more than 10% below the highest

close reached in the last 10 trading sessions

The Portfolio123 Value Screen

A one-year test of the screen

One-year test of screen -- continued

Two-year test of screen

Three-year test of screen

Testing the screen from 3/31/01

Testing the screen from 3/31/01 – continued

The Portfolio123 Growth Rank

Testing the rank – from 6/2/07

Testing the rank – from 6/3/06

Testing the rank – from 3/31/01

Simulation – Summary

Simulation – Performance Statistics

Simulation – risk data

Narrowing Down

• Somehow or other, get a listing preferable in Excel, of all the stocks and, for

each one, a series of data items.

• The data items need not be the same ones as those used in the screen
• In fact, consistent with the notion of theme mixing, it’s better if they are different
• This can be done through quality screeners like Portfolio123, AAII Stock Investor

Pro, or Zacks Research Wizard

• The next group of slides will demonstrate the process using the Stock Investor pro
• Narrow gradually through a series of data sorts
• Sort the entire list based on one data item
• Eliminate the lesser performers
• Sort the smaller list based on another data item
• Again eliminate lesser performers
• Repeat until the list is down to manageable size
• It should rarely be necessary to do more than three sorts
• Use “eyeballing” for the final elimination
• Drop names and/or industries you know you don’t want
• Drop stocks you see as having unfavorable results in other data items (the ones you

did not use for sorting)

Screen result spreadsheet – Buffett (Hagstrom Screen) 40 companies passed the screen

• 40 companies passed
• Use Earnings Estimates View

Making the sheet more meaningful

• Add some extra columns and create some useful formulas
• New Column C
• Estimate revision for Q1 - week
• New Column D
• Estimate revision for Q1 - month
• New Column E
• Estimate revision for Y2 - week
• New Column F
• Estimate revision for Y2 – month

Sort by weekly revision for Q1 and eliminate downward revisions

Next, sort by weekly revision for Y2 and eliminate everything without an upward revision; this gives us a very usable 14-stock

• group. We can buy all, or review in depth one at a time.

One-at-a-time review

• Two Good sources of data
• MoneyCentral.msn.com/investor
• A freee site with a good data presentation
• AAII Stock Investor Pro CD
• A subscription product ($198/yr for AAII members, $247/yr otherwise)

with a great presentation (that’s there in addition to the screener application)

Sample Money Central pages for Tractor Supply (TSCO)

Sample Stock Investor Pro reports for Tractor Supply (TSCO)

Exchange Traded Funds (ETFs)

Perspectives to get us past the jumble

• These are not the sort of neutral, passive, no-decision investments we once

thought they were

• An ETF passively tracks an index, but there are so many indexes out there, the

choice of an index is definitely an informed, skilled decision

• Don’t get hung up on traditional notions of the word “index”
• According to the dictionary, and index is something that reveals, indicates, or depicts
• Traditionally, a stock index depicted the state of the market
• Now, indexes depict the performance of strategies
• Market Cap, Style, or Geographic Strategies
• Asset class strategies
• Thematic strategies
• Strategies that use rules-based models
• Start by deciding what kind of index/strategy you want
• Once you choose a strategy, pick a fund that . . .
• Has as long a track record as you can find
• This is no small matter since so many ETFs have short trading histories
• Has a good risk-adjusted performance record relative to peers in the same strategic

group

Getting information on ETFs

• The usual suspects
• Morningstar
• Good for portfolio/returns data
• Yahoo Finance
• Good for portfolio/returns data
• Add these to your repertoire!
• IndexUniverse.com (free site)
• The best source of news regarding what’s happening in the ETF industry
• A terrific free ETF screener/report generator
• We’ll use this screener today
• Good guides/educational sections
• SeekingAlpha.com >> ETFs (free site)
• Re-transmits IndexUniverse articles
• Has commentary from other sources as well

Screening on IndexUniverse.com

• Register for the site – it’s free
• In the left side menu, under ‘SECTIONS,” click on “Data” (which is currently the

top selection

• IndexUniverse does not use the word Screener; they refer to it as Data or Data Tool
• r Data Query
• It’s updated monthly, but for our purposes, that’s ok
• In the Data section, ignore “Quick Search” and “Power Search.” Go right to

“Advanced/Power Search” and click the ETFs check box

The secret sauce: we are not really going to screen!

• We’re going to identify the types of ETFs we want to see, and then

create a report.

• Assuming we did a reasonable job identifying type, and assuming we

choose good columns to show in our report, eyeballing the report, whether on the web site or using a downloaded Excel spreadsheet, should serve us well

• Traditional ETF screening doesn’t really accomplish much
• Most screening tests are geared toward historical results
• Using that at a primary idea generator will simply turn us into performance

chasers and may push us into styles we don’t really want

• We need to bite the bullet and make the big style/strategy decisions on
• ur own
• Performance is then studied only to help us discern among ETFs with

similar approaches; here performance differences stand a better chance of representing something more substantive

Finding ETFs on IndexUniverse

• Start by choosing a style: we’ll aim at small/mid cap
• Notice we didn’t differentiate based on value/growth
• Don’t over-choose: be willing to eyeball

Next, choose fields for the report

Ignore the “Screen by:” section Go directly to “Submit” and click

Here’s what we get (after clicking on the “Alpha” column to produce a descending sort)

Reviewing the choices we made

• All mid- and small-cap ETFs
• It’s hard to develop much conviction on the issue of mid versus small
• I’m undecided for now on value vs. growth
• Assets
• Look at this to see if ETF is a minor player in the ETF arena
• Expense Ratio
• An obvious consideration
• 3-year returns
• This matches the 3-year horizon of most risk measures
• 1 and 3 month returns
• A quick view of what’s happening lately, to check if there’s been any sudden and

substantial change of trend that’s not shared among peers

• SD (Standard Deviation)
• Measures volatility of returns
• Higher numbers indicate more volatility
• Sharp
• Return (above risk-free rate) divided by standard deviation
• Higher numbers mean ETF is getting more payoff for the amount of volatility it incurs

Reviewing the choices we made – continued

• Beta
• Measures volatility relative to the market
• 1.00 means ETF’s volatility matches that of the market
• 0.90 means ETF has 90% of the market’s vilatility
• 1.15 means ETF is 15% more volatile than the market
• r2 (r-square)
• The percent of an ETF’s fluctuations that are in common with the market
• 100 means all of ETF’s movements are in common with the market
• 83 means 83% of ETF’s movements are in common with market
• Alpha
• A measure of the extent to which the ETF has generated returns above and

beyond the level that would be naturally expected to flow from the riskiness (volatility) of the portfolio

• The higher the better

Assessing our results

• Mid-cap growth has been hot, as evidenced by the preponderance of such
• fferings at the top
• iShares S&P is the big ETF family in this area, but its offerings are trumped by

two smaller ETFs, one from PowerShares (PWJ) and one from iShares Morningstar (JKH), which have excellent alphas and no apparent negatives

• Note that PWJ and JKH have much higher expense ratios than the vanguard
• ffering, but the excess returns they produce leads me to tolerate the expenses
• Unless we really have a conviction that leads us away from mid-cap growth, I’d

be inclined to split my investment between PWJ and JKH

Suppose we REALLY want something else, like small-cap value

• Scroll down until we see the category we want

Assessing the Value search

• There are three interesting choices, an iShares S&P (IJS), an iShares

Morningstar (JKL) and a Vanguard (VBR)

• The negative alphas show this area hasn’t been hot in the past three years
• But if we like small-cap value, these ETFs had alphas that were less negative

than those of peers

Going further down the list . . .

• We see some interesting items
• Wisdom Tree is there; it gets lots of interesting press
• PowerShares Dynamic Deep Value and Claymore Clear Spinoff sound interesting

But there’s no real reason to reach down to these low-on-the-list offerings

• The performance of Wisdom Tree’s offerings has a lot less appeal than the

publicity the firm gets for its unique approach (dividend weighting)

• Spinoffs tend to get very favorable publicity and they are beloved by the Street

and academia, but the Claymore offering has lackluster returns

• Deep-value sounds great, very Graham Dodd Buffett-ish, (How could a value

investor not love it?) but here, too, returns have not been good

• Meanwhile, none of these ETFs have three-year histories, meaning we don’t

have the full gamut of risk statistics

• Assets in the fund are small, suggesting they haven’t caught on with the

investing public and raising questions as to whether the ETFs may be liquidated in the event the market retrenches

Why split my investment among several similar ETFs rather than put it all into the best

• Remember, we’re relying a lot on past performance as opposed to underlying

fundamentals

• Although our leading choices are ostensibly similar, it’s still reasonable to

diversify against a major change the next time a portfolio is rebalanced

• Brokerage commissions today are low and ETFs tend to be traded less

frequently, so trading costs present no real obstacle to spreading one’s bets

Getting Information on individual ETFs

• The best source of information is the web site of the sponsor (iShares,

PowerShares, Claymore, etc.)

• This is easy to find via Google
• Once at the site, look for an opportunity to search by ticker
• You can get standard portfolio and performance data
• Most importantly, this is where you can get prospectuses
• Unfortunately, these are likely to be less helpful than you think in some area
• The more exotic the ETF (commodities, currencies, short), the more likely the

prospectus is to make for interesting reading

Be aware: When seeking a discussion of ETF investment strategy, you usually have to dig

• The typical investing strategy verbiage describes how the ETF attempts

to track its index as closely as possible

• This tends to be legal boilerplate and is unlikely to help you in any way
• Dig deeper to find a discussion of how the index is put together
• That’s where the real strategic issues are likely to be discussed
• You won’t always find this material satisfying
• “The index identifies value stocks by considering such factors as P/E,

Price/Sales, Price/Book . . . yada yada yada”

• Thanks for reiterating the obvious!
• But for better or worse, if there’s any interesting exposition to be found

(again, this is more likely in the exotic asset classes), you’ll find it here; in the discussion of the index

One more morsel: ProShares

• If you’re serious about ETFs, you owe it to yourself to Google your way to the

ProShares web site and study, study, study!

• Their specialties:
• Ultra ETFs
• ETFs that aim, for better or worse, to double the index’s performance
• Fro example, it the S&P 500 goes up 1 percent in a day, the Ultra S&P 500 ETF should go

up 2 percent

• Conversely, if the index drops 1 percent, the ETF should drop 2 percent
• Short ETFs
• These are designed to reverse the performance of the index
• If the S&P 500 drops 1 percent the Short S&P 500 ETF should rise 1 percent
• Ultra Short ETFs – the main attraction
• If the S&P 500 drops 1 percent, the Ultra Short S&P 500 ETF should rise 2 percent
• If you think oil is going down, you want to own the Ultra Short Oil ETF (ticker: DUG)!
• Tracking error among ProShares offerings tends to be above average (i.e. an

ETF that should go down 2 percent may drop only 1.25 percent), but the exposures are so unique, it’s worth tolerating the imprecise targeting

• The bottom line: thank to ProShares, there’s never a bad market, only bad

decisions!

Final note: Getting from IndexUniverse to Excel

• You can download your IndexUniverse.com screen results into Excel, but it’s

not easy to find the link.

• Here’s how you do it.
• Go to the Dropdown that says “Show 100 Results Per Page” as the default selection
• The last choice is “View All (CSV Download”). Choose that. Always save file to your

hard drive and open in Excel (you can then re-save as xls).

In conclusion . . .

Good Luck!