Did some research to generate a list of companies that have:



did some research to generate a list of companies that have:

•         Positive earnings growth for 5 years (i.e. Requirement #2)

•         An ROE of 15% or greater (i.e. Requirement #3)

•         And included data on Industry type, Gross Margins, Operating Margins, 5 year avg. Operating Margin, Return on Investments, Return on Assets

•         And included a column for current total market cap; as per one of Mohnish Pabrai's extraordinary essays (and just common sense) I'd be happier with a company at a $100 Million market cap. vs. $100 Billion to allow for the full benefits of long-term compounding.

•         The companies were sorted in ascending order of Total Debt/Equity -- -the low debt should suggest a strong balance sheet and avoid concerns that the above-average ROE is the artificially-inflated with dangerous amount of leverage.

•         Additionally, I wanted to add a data on insider-ownership levels (I’d like to see ~10% - 30% or so), but wasn’t able to find an easy way to get that info into the spreadsheet.  So I’ll just consult Value Line, which is always a good idea, once I find a business that is otherwise of interest.

 

Joel Greenblatt speaking at NYSSA

[pic]

Joel Greenblatt (left) and Brian Zen

[pic][pic]

Related gurus' buys/sells:

|Ticker |

|Date* |

|Price* |

|buy/sell |

|Picked By |

| |

|ARO |

|2006-09-30 |

|$27.3 |

|Buy |

|Joel Greenblatt |

| |

|LEA |

|2006-09-30 |

|$20.9 |

|Sell |

|Joel Greenblatt |

| |

|LYV |

|2006-09-30 |

|$20.7 |

|Sell |

|Joel Greenblatt |

| |

|LEA |

|2006-06-30 |

|$22.5 |

|Buy |

|Joel Greenblatt |

| |

|AMP |

|2006-03-31 |

|$43.6 |

|Sell |

|Joel Greenblatt |

| |

*The price and date might not be the actual time and price at which the transactions were made. In the case of institutional owners, the date is stated as the last day of their fiscal quarter. The prices are estimates if no accurate information available.

by Brian Zen and Garret Hamai

In the freezing cold evening of December 7th, 2005, Joel Greenblatt of Gotham Capital, armed with delightful jokes and a magic formula, warmed the hearts and souls of about 200 security analysts in a seminar organized by New York Society of Security Analysts (). We are pleased to bring you the financial enlightenments captured from that event:

Crunching Data, Searching Magic Formula

• During his years at Wharton, Joel Greenblatt manually entered stock data based on 9 years worth of S&P Stock Guides and created their own database for research.

• Cleaned up the data by eliminating certain things. Now they use Compustat database.

• Richard Pzena figured out how to enter those data into the mainframe computers at Wharton Business School. At that time, the computers were about the size of two conference rooms.

• Sometimes, the market throws off bargains because it is unreasonable about the prospects of certain companies.

Buying Cheap Stocks Works Over Time

• In the 70's, they tested Benjamin Graham's Net-net Formula and found that picking stocks below liquidation value worked well. (Liquidation value = Current Assets - All Liabilities.)

• Not every cheap stock performed well individually, but as a basket they did well.

• However, these types of opportunities are practically extinct in today's market.

• Many other studies have shown the strategy of buying cheap companies works over time.

Buffettized: Buying "Cheap" and "Good"

• Starting out as a die-hard value investor, Greenblatt became "Buffettized" in early 90's. Why not look for the good ones amongst the cheap companies?!

• Greenblatt says that he didn't realize that trying to find cheap and good companies, rather than just the cheap ones, was so important until the 1990s. While Graham was looking for starkly cheap companies, Buffett wants only the good ones.

• Greenblatt's friend, Richard Pzena, remains committed to buying troubled companies at dirt cheap prices, the cigar butt approach. You see this saggy cigar butt on a dirty corner of Wall Street. You pick it up and get one last puff out of it. The puff not very tasty. The act is not very elegant. But it's free (Laugh).

The Magic Formula Was Born

• By combining Graham and Buffett, Joel Greenblatt's magic formula is a computerized system to invest in good companies whose stocks are cheap.

• Good companies = High return on capital (ROC defined as operating profit divided by net working capital plus net fixed assets.)

Cheap stocks = High earnings yield (Earnings yield defined as pre-tax operating earnings divided by enterprise value.)

• ROC = EBIT/(Net working capital + Net fixed assets)

• Earnings yield = EBIT/(Enterprise Value)

• If net working capital is negative, use zero.

• Here, EBIT is last 12-month's earnings before interests and taxes (EBIT).

• Two key issues addressed by his magic formulas: (1) What is the return on the price you paid? (2) What is the return on the capital the company is investing?

Ranking the "Cheapness" and "Goodness"

• Greenblatt turned to his computers to rank companies by two factors, good (high ROC) and cheap (high earnings yield). Ranking Method: If company XYZ ranked 10 out of 3,500 companies in terms return on capital, and it ranked 20 in terms of earnings yield, the combined ranking of XYZ would be 10 + 20 = 30.

• Greenblatt's "Not-Trying-Very-Hard" Model Buy the top 30 of the highest ranked companies. Hold them for one year. Turnover the portfolio at year end to buy a new list of 30 highest ranked stocks based on one-year's worth of new financial data.

Sell losers right before a year is up, and sell winners right after 12 months for tax benefits.

Why the One-Year Holding Period?

• It is interesting to note that more stocks worked out over one-year rolling periods, rather than two-year periods. Maybe the time window that a value stock stays undiscovered is being shortened towards one year as more and more people start searching for values.

• Besides, after one year, you get a complete set of new earnings data. It would be a good time to run the rankings again.

• Selling has always been difficult. The other day, Greenblatt and his partners went on and on talking about the stocks that made a huge move after they sold at intrinsic value. To remove the uncertainties and difficulties of selling, a one-year holding period was picked. "I call it the Not-Trying-Very-Hard Model (Laugh). My mantra is to keep things simple," said Greenblatt.

• Besides, trading cost is cheap now.

Sell Rules

• Sell close to intrinsic value.

• Sell if something even cheaper is found.

• "We never mastered the art of selling. We are semi-bubbling idiots at it," confessed Joel.

The Magic 30.8% Per Year for 17 Years

• Magic formula works! Using stocks of all sizes, it produced a 17 year annual return of 30.8%. Using only the largest 1,000 stocks, the annual return was 22.9%.

• When ranked by 10 deciles (250 stocks in each, higher deciles consistently outperformed those below them from top to bottom.

• The cheap portfolio tends to have less volatility also.

• The magic formula beats the market 96% of the time.

Magic Formula Investing:

The Operating Steps

In his new bestseller: "The little Book that Beats the Market", Joel Greenblatt also discussed in detail the operating steps of the magic formula investing:

1. Go to (Nice plug!)

2. Specify your criteria for minimum company size.

3. Get a list top-ranked companies based on high return on capital and high earnings yield.

4. Invest 1/3 or 1/5 of your money into 5 to 7 top-ranked companies every 2 to 3 months. (Dollar-cost-average into the "good and cheap".)

5. After 9 to 10 months, construct a portfolio of 20 to 30 stocks.

6. Sell each stock after holding it for one year. For tax purposes, sell winners a few days after the one-year holding period. Sell losers a few days before the one-year holding period.

7. Reinvest the proceeds into new top-ranked companies. Stick to this simple and mechanical system for at lease 3 to 5 years to give the magic formula a chance to work.

Greenblatt's Personal Investment Process

• Looks for value with a catalyst, so nice things happen sooner. Special situations are just value investing with a catalyst. They are simply different places to find cheap stocks.

• In his own hedge fund, Greenblatt uses the basic principals in the magic formula: Look for high ROC and high earnings yield.

Tries to figure out what "normalized earnings" will be 3-4 years into the future.

• Makes sure the stock is very cheap based on normalized earnings.

• 5 to 8 securities can make up 80% of his portfolio. One position could be up to 30%.

• Having a concentrated portfolio works well for lazy people. Not that many stocks to track.

• Thinks about how much he could lose if he's disastrously wrong.

• No formal process or time frame for purchase decisions. Usually spend one month or so to do research. In difficult situations for which he and his partners have time, research could take months.

• If there is a great opportunity which, in their opinion, won't last, and if they feel they understand it, they sometimes use the approach of "Ready, Fire, Aim!"

• Has financials and utilities in the portfolio.

EBITDA Minus Maintenance CapEx

• For his own investment practice, Greenblatt uses a different input for earnings.

• He thinks that "EBITDA - Maintenance Capital Expenditure" would be a better measure of earnings power, but it can be difficult to calculate.

No Edge in Foreign Markets

• Greenblatt prefers to invest domestically because it's within his circle of competence and he hasn't run out of opportunities.

• He does acknowledge that you could probably find cheaper companies internationally and it is a good idea if it is within your area of expertise.

• If he was younger, he may do more with international investing.

On Long-Short Strategies

• Q: "How about long the top deciles of cheap stocks and short the bottom deciles of expensive stocks?"

• A: "I am not a fan of shorting. The long-short guys blow up every eight years. I call it the 'I got it! I got it! I ain't got it!' Strategy."

Fair Bet Yet Unfair Investing

• Look for a big mess that seems too complicated, not well understood, not well followed, and requires too much work. People don't want to do the work, but once you do the research, you will be at an advantage.

• Look for semi-complicated situations. The key is to identify what cuts to the core.

Numbers Are More Important than People

• First look at the numbers as they don't lie. You can learn a lot about the management by looking at what they've done though the numbers.

• Meeting the management in person and determining their abilities is not easy. "I used to meet a CEO and say to myself: 'This guy is smart.' Next day I meet another CEO and say to myself: 'This guy is smart, too.' (Laugh) In the end, I feel meeting CEO is not very important because I am not good at reading people," said Greenblatt.

• What is more important is: (1) What the management has done with the cash? (2) What are the incentives? (3) Is the salary too high? (4) Is there heavy insider selling? (5) What is their trackrecord?

• Compare "what they do" with "what they say".

• Bad signs: high salaries and insider sellings.

The Macro Picture Is a Distraction

• In 1999 and 2000, there were plenty of non-internet values out there. If you were worried that the burst of the internet bubble would have dragged those values further down, you would have made a big mistake. "Fortunately, we ignore the macro picture," said Greenblatt.

• In 2002 and 2003, there were plenty opportunities in small caps. If you were concerned that the bear market could go on further, you would make another mistake.

• "Now the value is in big caps. But if you look at the macro picture, the consumers could drop dead, the housing bubble could drag everybody down?- We ignore those. We have no macro view," said Greenblatt.

• Everything is cyclical. Values can always be found somewhere.

Ignore Volatility and Stock Prices

• Taking your clue from the stock prices is crazy. If you could value companies, you should ignore the noises from volatility and stock prices.

• Things such as volatility have nothing to do with buying a good, cheap company for the long run. Greenblatt said, "It is kind of ironic that, the older I get, the longer time horizon I look at." (Laugh)

• The Efficient Market Theory is a crazy way to look at the market. "Pick and choose. You can beat the market. It is worth the work."

The Vogue of Return on Capital

• There seems to be a movement towards high return on capital.

• The low P/B stocks haven't work very well in the past 10 years.

• Don't know if or when this trend will reverse.

One More Trick

• Greenblatt disclosed: "We have one more trick. When we have gains, we look at before-tax numbers. When we have losses, we look at after-tax numbers. (Blank stare?-pause.) That was trying to be funny. (Laugh)"

Finding the next gold mining gem is a daunting task. I often have to use different screening criteria to come up with interesting stock picks just to begin my analysis. Today I will be using Joel Greenblatt’s investing formula for uncovering potential gold mining gems. For those of you unfamiliar with Joel’s formula, he uses only two main criteria for determining the attractiveness of a stock investment. The two criteria are 1) Return on Capital and 2) Earnings Yield.

Return on Capital ('ROC') is measured by taking a company’s pre-tax operating earnings ('EBIT') and dividing it by tangible capital employed; the higher the ratio the better. Joel uses ROC instead of the more commonly used Return on Equity (ROE = earnings/equity) or Return on Assets (ROA = earnings/assets) because ROC uses earnings before interest and taxes. Joel’s reasoning is that different companies operate with different levels of debt and differing tax rates.

Earnings Yield is measured by taking a company’s pre-tax operating earnings ("EBIT") and dividing it by Enterprise Value (Market Value of equity + Net Interest Bearing Debt). Joel uses Earnings Yield instead of the more commonly used Price/Earnings (P/E) ratio or Earnings/Price (E/P) ratio because P/E and E/P are greatly influenced by debt levels and tax rates, while Earnings Yield is not.

In layman’s terms ROC helps you measure how much income a business is earning in relationship to how much it costs. A business with a high ROC means it can invest its own money into the business with a high rate of return. Earnings Yield helps you find a company that earns more compared to price you are paying for it.

Using Joel’s criteria, I screened for companies with a minimum market capitalization of $500 million, a high ROC and a high Earnings Yield. Looking at the top 100 companies that met this criteria, I found one lone gold mining stock, Northgate Minerals Corp. (NXG). Northgate’s ROC is in the 25-50% range, and the Earnings Yield is 17%. Northgate appears to be worth serious consideration, and may be the next gold mining gem using Joel’s criteria.

Joel Greenblatt is the author of The Little Book that Beats the Market. In the book he discusses in detail, the advantages and strategies of using ROC and Earnings Yield to evaluate a stock investment. I highly recommended reading the book as it offers a simple and well reasoned approach to finding potentially undervalued investments.

An analysis of the screening method described in Joel Greenblatt's Little Book That Beats The Market

Joel Greenblatt's pop-hit, The Little Book That Beats The Market (2006, John Wiley and Sons, ISBN 0-471-73306-7), describes a simple screening method to identify stocks that offer good value and low risk. Many web sites, newspaper articles and blogs have commented on this book by now (February 2006). Those commentaries uniformly fail to provide any new insight into Greenblatt's ideas. I found one fairly negative, rather academic-flavored site that was quite critical, but after reading carefully, I concluded that the author of that article didn't pay close attention to what Greenblatt actually says in his book.

The Little Book describes some detailed, retrospective studies of simulated, mechanical investing in stocks selected by his method. The simulations used data from a Comstock "point in time" database containing the information that was actually available on a large universe of stocks at each date in the study. Each screening and simulated trade was based on exactly the information that was available historically at that moment in time. This allowed Greenblatt to avoid some forms of selection bias that would have invalidated his research.

I don't have access to the Comstock historical data, but current rankings served up by Greenblatt's free web site can be pretty well replicated using current data from Value Line. Doing so requires reading of the book's appendix carefully, and taking into account some other comments scattered through the book. Here's what I learned from that exercise.

I have no connection with Joel Greenblatt; I simply read his book and worked out what I think it means. Greenblatt has neither reviewed nor commented on this article.

I do subscribe to the Value Line data service, and I commend it to interested readers. It's a convenient, high-quality service with superior customer support.

Comments, critiques, observations and suggestions welcome!

In a nutshell, what Greenblatt says

Restated briefly, here are some key points from The Little Book.

• Stock prices vary much more, and much more rapidly, than the realistic valuations of companies can possibly account for. Therefore, a stock may sometimes be overpriced, and other times underpriced. Obviously we'd like to buy underpriced stocks and sell overpriced ones. But can we tell when when a stock is underpriced?

 

• It's very hard to determine what a stock is "really" worth. What we can more easily do is rank a population of stocks into an order that reveals which ones are relatively cheap today, compared to the others. A portfolio of relatively cheap stocks presumably has a reduced risk of loss compared to the "average" stock, and some of them will swing back up to much higher prices, giving investors substantial profits.

 

• Greenblatt's screening is based on two fundamental numbers: Earnings Yield (EY) and Return on Capital (ROC). These are conventional numbers, calculated in a slightly unconventional fashion that eliminates certain bookkeeping fictions, particularly "goodwill", and eliminates the effect of taxes.

 

• His screening method is to compute EY and ROC for each stock in the universe; sort the stocks by EY and assign each one an earnings yield rank; sort the stocks by ROC and assign them return-on-capital ranks; add each stock's ROC and EY ranks to get a combined ranking number, and sort by this total rank. The "best" stocks rank simultaneously well in EY and ROC.

 

• EY and ROC are ratios whose numerator is EBIT, earnings before interest and taxes. Using EBIT compensates for differing levels of debt and tax rates that companies may experience. Greenblatt approximates EBIT in a way that I couldn't replicate exactly [see his footnote on page 139], but could approximate reasonably well.

 

• Greenblatt's ROC = EBIT / TangibleCapitalEmployed. Tangible capital employed is Net Working Capital plus Net Fixed Assets. "Net Working Capital" is not exactly what is ordinarily called "working capital" (current assets minus current liabilities). Short term obligations to suppliers are effectively interest free loans, and the text indicates that Greenblatt adjusts for that.

 

• Greenblatt's EY = EBIT / EnterpriseValue. Enterprise value is what you'd have to pay to purchase the entire company: the capital value of its outstanding common and preferred stock, plus any long term debt you'd have to pay off.

How I approximated Greenblatt's numbers

I approximated Greenblatt's numbers using Value Line data:

1. myEBIT = IncomeBeforeTaxes + Depreciation.  (IncomeBeforeTaxes has depreciation subtracted out; I added it back.)

2. ReturnOnCapitalDenominator = TotalCurrentAssets - Cash + NetPlant. (NetPlant is the depreciated value of long-term assets.)

3. ROC = myEBIT / ReturnOnCapitalDenominator.

4. EarningsYieldDenominator = MarketCap + PreferredEquity + LongTermDebt.

5. EY = myEBIT / EarningsYieldDenominator.

In addition, I eliminated stocks with Value Line industry codes "financl", "brokers", "thrift", "water", "reit", and all the codes designating banks, utilities, and insurance companies. I also eliminated stocks with ROC > 300 or EY > 50; extreme values suggest some condition in the company's history or accounting that might make its numbers not properly comparable with the rest of the population.

A company can have "good" ROC and EY by these definitions, yet still have negative shareholder equity, a poor net margin conventionally defined, or "negative" earnings by the book. Therefore I added a picky scan criterion which flags stocks that have negative shareholder equity, no earning per share, or a net profit margin less than 2%.

Expanding slightly on the features, I can specify a minimum market cap for the stocks selected, or a range of allowable market caps (e.g. companies with market caps between 25 - 1500 million dollars). I can also select logical combinations of industry codes, although doing so obviously does not find the cheapest stocks in the entire Value Line universe. This helps me notice relatively cheap stocks within an industry or market segment.

Finally, I don't compute a stock's final rank from the sum of its EY rank and ROC rank. Instead, I compute each stock's "distance" from the origin of an (EY rank,ROC rank) scatter plot according to the Pythagorean theorem (see second illustration below). The ideal "best" stock would have EY rank and ROC rank of #1, closest to the origin. The resulting sorted order is not significantly different from Greenblatt's trick.

While my definitions don't exactly replicate the numbers from , they do sort stocks into pretty much the same order. The result is certainly close enough for most purposes. Greenblatt himself makes the point that the exact definition isn't critical.

The (EY,ROC) scatter plot

It's informative to plot a scatter diagram with EY on the X-axis and ROC on the Y-axis (the raw values as opposed to ranked position in the list). The Value Line universe is big, even after eliminating non-industrial companies (financial etc. as noted above), so I only plot the top 300. Among those, the ticker symbol is placed on the plot for the best 65, and dots for the rest. Green points pass my "picky" test, red points fail it.

Here is a scatter plot of companies with >= 1500 million market cap:

[pic]

The top companies from this screen exhibit a sharp ROC/EY boundary. Companies whose EY or ROC is too small just don't make the cut; a firm can't operate with a really good ROC but a disastrously bad EY, for example. Good companies in the above plot, such as AEOS, UST, INTC, and DWA, fall near the 45-degree line, and farther out from the (0,0) origin.

MVL (top of the plot) is a company that was bankrupt. Such companies usually mark some of their capital assets down and shed long-term debt, so they might have abnormally large ROC values. You might think, well, that's OK - the company is operating with advantages after its bankruptcy. But consider that for "normal" companies, there is an implicit presumption that as the business grows, the firm can reinvest some profits to enjoy even more of that great ROC. Unfortunately, as a formerly bankrupt company that wrote off a lot of assets or debt begins to reinvest, its ROC can be expected to fall back toward a value that is more typical for its industry. Its ROC advantage won't last indefinitely. Screening by itself does not reveal such issues.

MT (Mittal Steel) is a good example of a somewhat different ROC issue. MT also scores well on a Greenblatt-type scan of large companies because its ROC is very high. Why? The company bought a lot of former communist-block steel factories at steal prices. Now, Mittal can produce steel very inexpensively, but those purchases were a one-time opportunity. Some day when demand slows, those same factories will saturate the market and drive steel prices into a very deep hole. Mittal might close some of them, or it might use its capacity to put less advantaged competitors out of business. One way or another, eventually there will be an ugly scene. Right now, with Asia using all the structural steel it can get, MT seems attractive. Will China keep importing, or will it develop its own capacity? Seeds of a future "capacity catastrophe" might be hidden in MT's present, unnaturally superior ROC.

The (EY rank,ROC rank) scatter plot

Let's have a look at a plot whose coordinates are (EY rank,ROC rank), instead of the raw EY and ROC values. Here are the best members of the Value Line universe with market caps at least 25 million.

[pic]

This picture is clearer because ranked points by definition can't fall on top of each other. Now the top-ranked companies, such as XJT and VTS, are near the origin. Again, red items fail the "picky" test for one reason or another. Some companies that were nearer the origin in December and January, such as EGY and AEOS, have migrated outward as the share prices went up.

Still a need for analysis

This screen produces some gems, and some stocks with real issues. Veritas (VTS) looks promising. It's essentially a software company that provides subterranean mapping of oil fields worldwide. It has a growing database of the most promising fields. The company managed itself through lean years without debt. It has great EY and ROC. Now, with the advent of what seems to be a new, persistently higher level of oil prices, Veritas is taking on a line of revolving debt and hiring more geophysical scientists. Even though energy-related stocks have run up a long way, VTS might be a really good purchase.

On the other hand, consider XJT. ExpressJet provides commuter services for a larger airline, under contract. I believe there was a recent report that big brother has decided to diversify its outsourcing. The commercial airline game is brutal; XJT may be cheap for good reason - it sells its services to one or a few customers who have every reason to squeeze hard.

These are just some of the issues that can arise if one blindly follows a numerical value screen. Still other problems may arise if a company's books can't be trusted, due to honest or dishonest mistakes.

Why does Greenblatt's method produce such good results?

Despite these limitations, Greenblatt's screen produces an excellent list of candidates to consider. His premise that these companies are inherently less risky than the "average" stock in the universe is probably true, because their raw numbers demonstrate that they have the requisites to succeed, if they are managed properly. And they're cheap buys in a tangible, concrete sense that is hard to dispute.

Greenblatt believes we can earn really remarkable rates of return by buying these companies, yet the widely accepted risk/return principle of investing holds that greater risk is required to get higher rates of return. Is there some hidden risk here? Or is that canonical belief simply wrong?

His explanation is the simplest one: in the long run stock prices do reflect risk in an average way, but they are very inaccurate in the short term. The risk/return principle is intimately related to another axiom of financial theory, which holds that at any moment, the probability of a small price up-move is about equal to the probability of a small down-move. This is equivalent to claiming that stock price returns have a log-normal probability distribution.

That is roughly true for the market as a whole, but it is probably not true for the sub-population of stocks selected by Greenblatt's screen. These stocks are already relatively cheap, and since their EY and ROC indicate that the essential requirements for a successful business are satisfied, the probability of a down-move is not about the same as the probability of an up-move for these stocks. The rate-of-return distribution for this population of stocks is probably quite asymmetric.

Which raises another question ...

We just saw that without additional analysis, depending on screening alone may lead you into mistakes. Yet Greenblatt's mechanical simulations, which demonstrated extraordinary returns, obviously didn't incorporate such analysis - although he might have applied additional, "picky" criteria that are not fully explained in The Little Book.

So ask yourself this. Suppose you accept the risk/return principle. By applying additional analysis that was not done in Greenblatt's simulation, you are selecting even less risky stocks from the screened population. Do you believe that applying such analysis will actually reduce your rate of return?

- Roger Ison, 27 Feb. 2006

 Three-Factor Screen

Phil Graham sought the cheapest stocks, and Warren Buffett sought stocks in great companies at bargain prices. A three-factor screen is being touted to pick so-called good cheap stocks, but it is old wine in a new bottle.

An article entitled "Magic Formula Of Little Book Just May Work" by Jesse Eisinger on pages C1 and C5 in the 9 November 2005 Wall Street Journal discusses a method of picking stocks that is essentially a screen. The source of the magic formula is a 2005 book entitled The Little Book That Beats the Market by Joel Greenblatt, who is also the author of a 1997 book entitled You Can Be a Stock Market Genius (Even If You're Not Too Smart). Eisinger reports that the "Little Book is one of the best, clearest guides to [so-called] value investing. ... in a world where individual-investor advice is dominated by jargon-filled short-termism on the one hand and oversimplified [slice and dice] indexing on the other. ... He [Mr. Greenblatt] writes ... with the fervor of a true believer." The following is a paraphrase of selected excerpts from the article, followed by a cautionary comment.

The magic formula is to invest in good companies when they are cheap. Good companies earn high returns on their investments. Cheap companies have share prices that are low based on past earnings. The proxies for these two criteria are accounting return on capital and market earnings yield. Accounting return on capital is here defined as operating profit as a percentage of net working capital and net fixed assets. Market earnings yield is here defined as pretax operating earnings compared with enterprise value, which is the market capitalization of the stock plus the net debt.

Mr. Greenblatt advises individual investors to buy a basket of top stocks and turn them over on a strict schedule, depending on how they perform. For maximum tax advantage, sell losers just before a year is up, and winners just after a year.

Looked at in hindsight, the returns of the magic-formula method allegedly beat the market. From 1988 through 2004 (7 years), according to Mr. Greenblatt's book, the (1) high-book-return and (2) low-price stocks of (3) the largest 1,000 companies had (4) stock market returns of 22.9% annually, compared to 12.4% for the S&P 500. When (1) 2,500 companies [one-half of U.S. industrial stocks; probably the largest] are ranked for (2) price and (3) book returns (based on the formula), then in terms of (4) stock market returns, the top 10% outperformed the second 10%, which outperformed the third 10%, and so on. It works in order.

The approach is difficult not because it is hard to understand, but because it requires patience and trust that you are right when the market is indicating that you are wrong. Some limitations to the approach include the tendency to choose stocks whose high book returns and growth in size or market capitalization may be in the past. Some of the magic-formula stocks with more than $1 billion in stock-market capitalization include many fast-growing specialty retailers and niche pharmaceutical companies, some of which will burn out.

That is why Mr. Greenblatt argues that novice investors buy at least 20 to 30 of them. For himself, he buys a smaller number that he can know deeply. But that requires something not easily taught in a book: good instincts and judgment to distinguish true cheap gems from one-hit wonders.

COMMENT: There are two problems with this magic formula investing three-factor screen of (1) market size, (2) book return on capital, and (3) combination market-and-book earnings/size yield, to maximize (4) stock market return. The current values of the magic formula investing criteria are available from S&P Compustat that provides financial and other information on more than 5,000 U.S. industrial stocks and from other commercial databases, but their use as a stock picking method is dubious.

Neither Mr. Eisinger nor Mr. Greenblatt holds a Ph.D. degree in financial economics, and this might explain their silence about the first problem. Two of the three screens or factors are not independent of stock market return. The market capitalization of a company's total stock, a k a size, as here defined, is not independent of stock market return. Earnings/size yield, as here defined, is not independent of stock market return, because earnings/size yield entails size. Only return on capital, as here defined, is independent of stock market return.

Size is logically circular as an explanatory factor in any econometric model of stock market return, because both size and market return entail stock price. An econometric model with size as a factor, or with size as part of a factor, is not scientifically valid. Therefore, back-testing the magic formula investing three-factor screen is vicious circular reasoning, fatally fallacious, meaningless, and non-interpretable. The magic formula investing three-factor screening method for picking stocks is fatally flawed and disguised market timing.

When being back-tested for (4) stock market return, and compared to benchmarks such as the S&P 500 Index of common stocks, the magic formula investing three-factor screening method is essentially and effectively an asset pricing model of return; and as such, it is a variation of the pseudo-scientific Three-Factor Model of return for stock portfolio pricing. Both return models have three factors, each of which entails one of three variables: (1) size, defined as market capitalization; (2) a variable that does not entail price; and (3) a yield on price, defined in various ways, such as earnings/price, earnings/size, or earnings/enterprise value. For both return models, the factors related to the size and yield-on-price variables are logically circular.

Both Mr. Eisinger and Mr. Greenblatt have reason to know about the second problem with this magic formula investing three-factor screen. An investor can choose to ignore the first problem, which involves theory, and believe in magic. But an investor cannot ignore the second problem, which is practical implementation of the theory behind the magic formula investing three-factor screen.

The Greenblattt Magic Cube of three dimensions is a black box, and the details of sorting, ranking, and picking individual stocks are proprietary. And the devil is in the details. More transparency is needed for fuller accountability. The Greenblattt universe of investment opportunities is already screened for (1) publicly traded common stocks, (2) U.S. based companies, and (3) industrial firms. This universe of U.S. industrial common stocks is sorted by the three screens in some order, and the order is arbitrarily chosen. A different order results in a different selection of stocks if the net remaining universe is used instead of the gross beginning universe of investment opportunities to determine averages or cut-off points. The gross cheapest stocks that are the net best are not the same as the gross best stocks that are the ne cheapest.

In addition, each screen or dimension is a continuum that is partitioned into categories, and the number of categories is arbitrarily chosen. If each of the three dimensions is split into two parts, then 1/8 of the universe is selected; and if each dimension is split into ten parts, then 1/1000 of the universe is selected. For the universe of U.S. industrial stocks of about 5,000 companies, this ranges from 625 to 5 stocks selected.

Furthermore, there must be breakpoints between the adjacent categories, and the breakpoint values and/or the method of determining the breakpoint values is arbitrarily chosen. The categories might be deciles, for example, and the breakpoint values would follow this choice of relative comparative values. Or the categories might be simply pass or no pass, depending on the absolute minimum acceptable value of each criteria. For an example of such absolute minimums, "large" might be more than $1 billion in market capitalization; "good" might be higher than the risk-free rate on U.S. Treasury bonds, adjusted for price-level inflation, plus an equity risk premium; and "cheap" might be higher than the earnings/price ratio on the S&P 500 index of common stocks.

In summary, the magic formula investing three-factor screen will continue to work even after everyone "knows" it, because mathematics will continue to work even after everyone "knows" it. Logically circular back-testing is purely mathematical. Vicious circular reasoning can be subtle to detect, even for intellectuals with doctoral degrees and for practitioners who have made fortunes in the stock market. It is a familiar phenomenon for someone who has successfully invested in the stock market to believe he has a magic touch and then write a book about his alleged method, leaving readers to discover that there is no magic or science about his stock-picking success.

[pic]

Unit Pricing

Price is not value, and unit pricing is not valuation. Unit pricing in this context does refer to market price per share of stock but rather to what the investor gets by owning that share of stock. A unit price may be useful for comparison shopping, but how many people buy even their commodity groceries strictly on the basis of calculated unit prices? That is how many value and growth investors unwittingly buy their stocks and mutual funds. Are common stocks fungible?

Unit prices are expressed either in number of units per dollar or other currency or in dollars or other currency per unit. Unit prices for common stocks are expressed as price ratios such as the P/E ratio (the dollar market price per unit of earnings), the P/BV ratio (the dollar market price per unit of book value), and the dividend yield or D/P ratio (the number of dividend units per dollar market price).

The fact that unit prices are quantifiable, monetizable and easy to calculate explains their attraction. They are also superficial in the sense that no judgment or expertise or even experience is needed to calculate them. They have the advantage of convenience. Screens based on external financial accounting data bring to mind the Sufi story about the man searching at night under a lamp post for his lost house keys. When asked by a passerby where he was when he last had the keys, he said it was near his house. When then asked why he was searching so far away from his house, he said because this is where the light is.

[pic]

Grail

There is a perennial search for a screen that is a good proxy for investment value. Such a screen is a grail for some so-called value investors. Various screens have been proposed over the years. Some questions come to mind concerning such a grail screen.

First, how high is the correlation between any given screen and investment value, in terms of the stocks selected? If a particular screen results in selecting the same stocks as estimates of investment value, then the correlation between the screen and the investment value estimate is perfect and the coefficient of correlation between them is one hundred percent. What is the minimum acceptable correlation coefficient to justify use of a screen in lieu of estimates of investment value?

Second, since correlation is a statistical concept based on a group of stock selections as opposed to a single stock selection, would it be necessary to have a minimum number of stock selections in order to realize the validity of the correlation? If so, what is the minimum number of selected stocks in the portfolio?

Third, correlation is calculated between a sample of a sufficient number of pairs of points. Each pair consists of a point for the screen value for a particular stock and a point for investment value of that same stock. As a practical matter, the concept of investment value is operationalized as a range of values and not as a single point estimate, and sometimes to select or deselect a stock, this investment value range is unbounded on either the upper tail or the lower tail, respectively. In other circumstances, to select or deselect a stock, a margin of safety is calculated from the estimated investment value mean and the quoted market price, and the size of this margin can vary subjectively from stock to stock depending on the size and shape (moments) of the distribution of investment value and other considerations. Without losing information for informed rational decisions about selecting or deselecting a stock, how can a simple correlation coefficient be calculated from a distribution of investment value, an unbounded distribution of investment value, or a fuzzy distribution of the margin of safety of a stock?

Fourth, before calculating correlation coefficients, someone with an appropriate circle of competence would need to estimate investment value of the company and its common stock. Such expertise is idiosyncratic and constitutes a valuable person-specific monopoly of knowledge. Setting aside for the time being the practical difficulties of finding such qualified persons, how would you operationally define and grade the circle of competence of the expert appraisers?

Fifth, assuming that we have the results of such a correlation analysis with assessments of statistical reliability, is this correlation that is calculated using in part historical data and 20/20 hindsight expected to hold beyond the fiscal periods on which the screen data are based? For example, assume the screen is the Price to Book Value ratio (P/BV). If the correlation between P/BV and investment value turns out to have the highest coefficient in the empirical study, will P/BV remain the single best proxy of investment value both for all stocks in all markets and for all future stock market cycles?

Sixth, assuming that we have access to estimates of investment value of a sufficient number of stocks within the circles of competence of the associated expert appraisers, what would be the purpose of calculating the statistical correlation coefficients between the investment value selections and the selections by various screens? Would it be done for an academic paper? Would the authors of the paper estimate investment values?

It is clear that screening, regardless of the number or sophistication of the screens used, is not a reliable substitute for valuation. Screening is superficial, and valuation is deep. Screening is an efficient way to reduce a universe of stocks within an already circumscribed circle of competence to a short list of stocks for in-depth study. Valuation is an independent process that has no necessary relation to screening and can be done without any screening whatsoever. Screening is more useful in a top-down approach that begins with a universe of stocks than in a bottom-up approach that begins with a single stock idea.

 

|Global Value Investing |

|HOME   |   INVEST   |   VALUE   |   SAFETY   |   FADS   |   NEW   |   FAQ |

|GLOSSARY   |   BOOKS   |   LINKS   |   AUTHOR   |   SITE MAP   |   SEARCH |

|A multifaceted approach to value investing with stock valuation based on intrinsic value estimated from cash returns, |

|appraised value of assets, and other facets of value. |

 

 

Link List

 

The links listed below appear as active hyperlinks at Links A to M and Links N to Z where a fuller description of each appears. They are listed here for quick preview.

 

 

|NAME |DESCRIPTION |

|Links A to M |

|AAII Stock Investor |databases; screen, rank & graph 8,000 stocks; fee |

|American Partnership Board |limited partnerships online auction system |

|Ameritrade |low-cost, on-line transaction service |

|Annual Report Gallery |online financial reports and links |

|BEAR Valuations |business equity appraisal reports; fee |

|Big Charts |quick charts & interactive charts for stock data |

|Bloomberg |quotes, data, analysis, funds, world markets, energy |

|Bloomberg: International Yield Curves |G-7 and Dutch governments |

|Bonds Online |quotes/search for U.S. corporate & other bonds |

|Business Directory |100,000+ companies online and links |

|CBS Market Watch |global markets; foreign exchange |

|CERES |Coalition for Environmentally Responsible Economies |

|CEP |Council on Economic Priorities |

|CNNfn World Stock Markets |indexes for bourses in four global regions |

|Corporate Library |corporate governance issues and information |

|CPA Class |U.S. generally accepted accounting principles (GAAP) |

|CPCUG |historical graphs of markets and economies |

|Data Downlink Corporation |spreadsheet-formatted financial data |

|Datek |low-cost, on-line transaction service |

|DBC Online: Financial Markets: Industry Groups |38 industry groups |

|Decisioneering Crystal Ball |risk analysis and Monte Carlo simulation software |

|DecisionPro |management decision analytics |

|Dismal Scientist |economic information and analysis |

|eBondTrade |prices and online trading for investment-grade municipal bonds |

|EDGAR Online |real-time SEC filings; insider trading reports; fee and free |

|Escape Artist |offshore investments |

|eTrade |low-cost, on-line transaction service |

|FASB Exposure Drafts |proposed U.S. financial accounting standards |

|FinanCenter |finance calculators for stocks, bonds, and mutual funds |

|FinancialCAD |financial engineering software for option pricing and other modeling |

|Financial Data Finder |OSU Fisher College of Business search directory |

|Financial Players Center |finance education -- Time Value of Money |

|FindLaw |for lawyers, students, the public, and business |

|FreeEDGAR |free unlimited real-time SEC filings |

|FINWeb |financial economics journals & papers |

|Form 1040 |U.S. Federal & State income tax information, forms, and links |

|Gomez Advisors |Personal Finance: Internet-Broker Scorecard |

|Governing Corporate Objective |Shareholders vs Stakeholders |

|Hoover's Online |capsules on over 11,000 companies, U.S. and non-U.S. |

|Inflation Calculator |1800 to 1995; U.S. Consumer Price Index |

|Intrinsic Value Associates |institutional equity research and valuation |

|InvestingSites - Brokers |list of links to full-service and on-line brokers |

|Invest Offshore |offshore tax havens around the world; private banking, investments |

|Investor Protection Trust |your Investment Quotient-IQ; 50 state listings |

|InvestorGuide |investing and personal finance; advisor data |

|Jadco Stock Charts |actual and projected revenues and earnings |

|James' Calculator |financial calculator for NPV, FV and ROR |

|Kiplinger Financial Calculators |8 including stocks, bonds & mutual funds |

|LENS Investment Management |activist money manger criteria and methods |

|LivEDGAR |M&A database for comparable deals and data |

|Market Guide |provider of financial information; real-time |

|MatLab for Finance |computational and visualization analytical tools; fee only |

|MEAP's Currency Conversion Calculator |daily rates for 35 currencies |

|Media General Financial Services |database of NYSE, AmEx & Nasdaq financials |

|MergerStat |control-premium study for valuing a controlling interest |

|Moody's Investor Services |ratings and real-time rating actions |

|Moody's Manuals |screen, rank, spreadsheet & graph; US & non-US, fee only |

|Morningstar Net |market data; quick takes; portfolio radar; fund and company screens |

|Multex Investor |clearinghouse of institutional research; fee |

|Links N to Z |

|NAIC |education; investment clubs; DRIPs, S&P screening; fee only |

|Natural Investing |making decisions in alignment with personal values |

|Numa Option Calculator |adjusted Black-Scholes model to value European options |

|OnLine Investment Services |Discount Stock Brokers Ranked report |

|Perspective on Value |Why Share-Owner Value? |

|Public Register's Annual Report Service |free annual reports on 3,600+ public companies |

| |true random number service |

| |comprehensive data, SEC filings, stock evaluator |

| |stock, fund, index and money market quotes; research; SEC filings; free & fee |

|Research: Magazine: InvestorNet |market and company data; free & fee |

|Resources for Economists on the Internet |selective, narrowly-focused |

|Reuters Money Network |screen U.S. stocks, bonds, funds, CDs, etc.; fee only |

|Robert's Online Applications |pricers for options, commissions, etc. |

|Savings Bond Wizard |U.S. Treasury; redemption values; reports |

|SmartMoney |broker ratings; quotes & data; calculators & worksheets |

| |socially responsible investing funds |

|S&P Blue List |U.S. corporate & municipal bonds current offerings |

|S&P Compustat PC |financial data, global, ACE, I/B/E/S |

|S&P ComStock |worldwide real-time market data |

|S&P Equity Investor Services |Earnings Guide, Stock Guide, etc.; fee only |

|S&P Personal Wealth |U.S. & global stocks, bonds; holdings, analysis; fee & free |

|S&P Ratings Services |ratings criteria for 8 categories |

|Statistica |animated interactive probability calculator |

|Stockpoint |investing tools, screen and sort, export; free |

|StockSelector |four stock "value" calculators and Dow analysis |

|StockSense |discounted cash flow models for stock valuation; examples |

|Stock Smart |comprehensive worldwide company data; fee |

|StockTools |screens, quotes, industry groups, portfolios, and graph wizard |

|StockVal |model that returns a warranted price-earnings ratio |

|U.S. SEC Archive Search |historical filings |

|U.S. SEC EDGAR Search |internet database of corporate information; new filings |

|U.S. Tax Resources |Federal and States income tax forms and publications |

|University Angels |investor and entrepreneur equity capital network |

|Value Line Investment Survey |screen, rank & graph 5,000 stocks; fee only |

|Venture Capital Resource Library |database; list & browse investment opportunities |

|Wall Street City |collection of single-question personal finance "calculators" |

|Wall Street Research Net |economy, industry, company and fund data |

|Wiley & Sons |download spreadsheet programs for valuation |

|World Economic Forum |global competitiveness reports |

|World Wide Web Resources in Economics |mainly broad academic topics |

| |American Depositary Receipts; EDGAR Online; global indices; currencies |

|Xenon Laboratories |worldwide currency converter |

|Yield Curves |for terms of 3 months to 30 years |

|Zacks Investment Research |analysts earnings estimates and other data |

|ZDNet Search |personal stock quote & private portfolio applications |

 

|Criteria for Selected Links |

|1. |Provides useful product, service or information for the intended audience of this|

| |Web site. |

|2. |Provides at least part of this stuff for free without obligation of any kind |

| |(with notable exceptions). |

|3. |Provides this free stuff in greater quantity or higher quality than other content|

| |providers. |

 

Legal Tender

Can you recognize a counterfeit on the face of it?

[pic]

 

[pic]

Copyright © 1996-2003. . All rights reserved.

................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download