Finding Investment Ideas By Screening Stocks and ETFs

1. Finding Investment Ideas By Screening Stocks and ETFs Marc H. Gerstein mgerstein@yahoo.com June 14, 2008

2. 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.

3. 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

4. Stocks

5. 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

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

7. 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)

8. 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

9. 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 on 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

10. Types of Rules – Screens • Stocks selected based on data-oriented tests • All stocks in universe are rated “yes” or “no” depending on 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.

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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?

20. 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

21. 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

22. 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 ones 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 or 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

23. Deconstructing rules critiques: not sexy • OK. I’ll concede this one

24. 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 

25. 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

26. 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

27. “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

28. 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

29. 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

30. 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

31. 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

32. The Portfolio123 Value Screen

33. A one-year test of the screen

34. One-year test of screen -- continued

35. Two-year test of screen

36. Three-year test of screen

37. Testing the screen from 3/31/01

38. Testing the screen from 3/31/01 – continued

39. The Portfolio123 Growth Rank

40. Testing the rank – from 6/2/07

41. Testing the rank – from 6/3/06

42. Testing the rank – from 3/31/01

43. Simulation – Summary

44. Simulation – Performance Statistics

45. Simulation – risk data

46. 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)

47. Screen result spreadsheet – Buffett (Hagstrom Screen) 40 companies passed the screen • 40 companies passed • Use Earnings Estimates View

48. 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

49. Sort by weekly revision for Q1 and eliminate downward revisions

50. 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.