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Introduction This is intended to be the first in a series of posts which will quantitatively examine RuleMaker (and other) metrics using statistics and other mathematical tools and relationships. The primary goal of this analysis is to answer questions such as: o Do the RM metrics embody "quality" that the market recognizes? (I already have statistical data from the RM ranking spreadsheets that suggests yes) o If so, how is that quality recognized... Valuation? Which valuation method? o What is the "relative quality" of each metric? For example, is 1 point of Net Margin equivalent to 1 point of Gross Margin? Or is it closer to 1.856 points? Can 3 points of CashKing Margin be traded for .05 points of Flow Ratio, and still have equal quality? o Assuming the questions above are answered, can a mathematical model of the "valuation vs. quality" relationship be constructed? And can this model then be used to identify companies which may be currently undervalued or overvalued based on the metrics. o Can the model constructed above be backtested, and if so, how well does it perform in a strategy that tries to buy RuleMakers at attractive prices, keeping them until they either fall from RuleMaker grace or become too overvalued to have a reasonable chance of continuing to achieving the 2x5y objective. Some Important Assumptions! (My assumptions... yours may differ) o There are MANY MANY forces at work in the market. o Different forces affect different companies and sectors. "Future hope" is probably more relevant than "net margin" for many biotechs. I haven't found the "future hope" line item in anyone's financial statement yet; trying to quantify how some biotech companies behave as a function of RM metrics is probably futile. This will influence the types of companies included in this study. o SOME of the forces at work are a function of the fundamentals of the business, and will have a long term impact on the stock price. Examples include margins, free cash flow, and management integrity. o SOME of the forces at work have little to NOTHING to do with the business, and will have only a short term impact on the stock price. Examples might include reaction to a product recall (Tylenol!!... Firestone???), a poor call made by an analyst, or a false merger rumor. o A mathematical analysis can not totally replace other Due Diligence work. (The folks on the Mechanical Investing Board, as well as some Technical Analysts may disagree, and they may in fact be correct under certain circumstances. That's OK with me. There is a LOT of good brainpower at work in those camps that I will not pretend to fully understand. My assertion here is that using ONLY math and TA will be inferior in the LONG TERM to a strategy that ALSO uses some intelligent subjective analysis, like looking at management integrity, pending litigation, and accounting procedures when you suddenly find that "too good to be true" undervalued company.) o The "answer" (a.k.a. holy grail, epiphany, motherlode...) is unlikely to pop out quickly and obviously. At best, I am hoping to find relationships that will help highlight potential fertile starting points for further investment research. It is also hoped that as appropriate, the data can be used to dampen generalizations that are not universally true concerning certain metrics, so I can stop wasting my time computing them. o The "answer" will not only be hard to find, but pieces of it will also change over time. I wish I had started this a little over a year ago, just before we learned the truth about bubbles. Methodology To try and answer some of the questions posed above, I began studying a group of 160 companies (See Below) with respect to their RuleMaker (and other) metrics approximately 3 months ago. For each company, I maintain a spreadsheet onto which I enter data from each quarterly or annual report. The spreadsheet then calculates all of the RM metrics, plus a few others that are frequently encountered in business analysis, such as inventory turns, cash conversion cycle, ROA, ROI, and ROEIEIO. All "fundamental" data is calculated on a Trailing Twelve Month (TTM) basis, since many companies have business models that exhibit an annual cyclicality, and also because I am more interested in how the market reacts to longer term trends. Speaking of "trends", the data also includes some data on the prior TTM, so that statistical analysis of both current position and direction can be done for certain metrics. The RM Data from each individual spreadsheet is then transferred to a larger spreadsheet, which also includes "transient" items such as price, forward earnings estimates, forward growth estimates, and analyst recommendations. On a monthly basis, this data is "frozen" in time and saved as a separate data file for future analysis. This allows me to go back in time for instance to March 2, 2001 and see all of the RM data that was currently known on that date, as well as the price, forward projections, and valuations that were current on that date. This will be used in the future to backtest various hypotheses. Statistical analysis to uncover underlying relationships between two different parameters is performed by using a "statistical calculator" which can accept a set of "xparameters" (say net margins for the 160 companies) and a set of "yparameters" (say Price to FreeCashFlow ratio for the same 160 companies) and perform a regression analysis to identify the "best fit" line that passes through these 160 xy pairs. By examining the equations derived for these lines, as well as parameters like "correlation" and "covariance", you can begin to assign real live numbers to the "value" the market assigns to certain metrics. You can also begin to assess whether the parameters are strongly or weakly related. At this time, no plans are in place to explore nonlinear solutions to achieve higher accuracy. I'm just an 80/20 kinda guy that way. How the Companies were chosen As hinted at above when discussing market forces, it is my belief that if you want to study the affect RM metrics have, you should probably select companies for which RM metrics are at least partially relevant. Therefore, all the companies that have ever had an RM analysis performed on the RM company board served as the starting point. Then, since a full RM analysis requires at least a passing glance at competitors, a significant number of competitors to RuleMaker companies was added. I also included some of the "biggest" companies in the sectors where RM's were found, independent of whether or not they were viewed as a direct competitor to one of the RM companies. Since these last inclusions have now "polluted" my list with companies that may not be even close to looking like RM's, it will be necessary (eventually) to perform some of the statistical number crunching with two sets of data... sometimes with all companies, and sometimes with only those that have "RuleMakerness", to be defined later. Now since the data is already polluted, I added some companies that have decidedly unrulemakerness attributes, just to serve as a test bed for the hypothesis that different types of companies and sectors do indeed behave differently when looking at their metrics. Finally, I eliminated companies that require unique analysis. (All financial companies or others that have "financial looking" financial statements). The complete list of tickers for companies included in the study is shown at the end of this post. The subset that are deemed to have "RuleMakerness" will have to be identified at a later date, since at this time I am still waiting for or analyzing about 30 annual reports for the period ending 12/00. First Analytical Results: (I bet you can hardly wait, right?) My first results to share have to do with a decidedly unFoolish metric, the "Analyst Recommendation". On March 2, 2001, I took the first "data snapshot" for the 160 companies. On March 31, 2001, I took the second "data snapshot". Looking only at the analyst recommendation and price on these two dates, I set out to answer the following 3 questions: 1) How did the stock price for the period 3/2/01 to 3/31/01 perform as a function of the analyst recommendations on 3/2/01? Well, as most of you are probably aware, analyst recommendations can be found on the "Research" Stock quote page of Yahoo!, expressed as a numerical value between 1 (Strong Buy) and 5 (Strong Sell). The number listed is simply the average value of all the ratings by all the analysts. So if there were two analysts covering XYZ company, and one analyst rated the company a "Strong Buy" (Value=1) and the other had it rated a "Hold" (Value=3), then the average recommendation would be (1+3)/2=2 or "Buy". So to answer the above question, I began by simply feeding my statistical calculator 160 xvalues corresponding to the 160 average recommendations. Then, I computed the percentage price change for each company according to the formula % Price Change = 100 * (((Price on Mar31)/(Price on Mar2))1) Spreadsheets are marvelous, as I only had to type this formula in once, and was then able to replicate it for the other 159. Anyway, I was now armed with 160 yvalues, which I promptly fed to the calculator using the copy and paste utility between the spreadsheet and the web page where the calculator resides. I then hit the "submit" button, and almost immediately was looking at a scatterplot of the 160 xy pairs along with two "best fit" lines, one which assumed x was the independent variable, and the other which assumed y was the independent variable. And what did the equation of that line tell me? Well, hopefully everyone here remembers their basic algebra (or was it geometry?) in which a straight line can be expressed in the form y = mx + b where the dependent variable y can be calculated for any independent value of x, assuming you know m (the slope of the line) and b (the yintercept of the line). For the Analyst recommendations and price changes of our 160 companies, the best fit line came back as: % Price Change = (6.25 * Analyst Reccomendation) 21.06 (y) = (m) (x) (b) Those familiar with looking at these types of equations will undoubtedly notice that there is a positive slope to the line. In other words, the higher in the positive direction the analyst rating, the higher in the positive direction the price changed. But WAIT!! Isn't a higher rating a less attractive rating? (1 = Strong Buy, 5 = Strong Sell). Yup, as the data played out for this group of stocks in this time period, the better results (statistically speaking) were achieved by going exactly counter to the analyst recommendations. March was yet another down month, so on an absolute basis, most stocks did poorly.... but making a table of various values from the above equation, it is easy to see that the statistics definitely favored the least favorite analyst picks: Analyst Recommendation I % Change in Price ___(AVERAGE)____________I____(AVERAGE)______ I 1.0 (Strong Buy) I 14.81 1.5 I 11.69 2.0 (Buy) I  8.56 2.5 I  5.44 3.0 (Hold) I  2.31 Once again, these are statistical averages only, which are very similar to the "average family". It doesn't actually exist. And just because we have discovered some sort of "relationship" between these parameters does not necessarily mean that the relationship is one of "Cause and Effect". It is higly unlikely that a lousier rating CAUSED improved performance. A more plausible explanation might be that the analysts were expecting a certain sector or group to do well (such as tech), and the opposite occurred. For the statistically nonchallenged, here are some detailed stats behind the data: Mean Analyst Rec = 1.95 Mean % Price Change = 8.87 Covariance = 1.05 Correlation = .18 Regression (Price on rec) = 6.25 Regression (Rec on Price) = .005 For the statistically challenged, (which includes myself), I think the most important number above is correlation = .18. Correlation can vary between 0 (No correlation) and 1 (Perfect correlation). The value of .18 would indicate a fairly weak relationship, in which other factors are clearly working. This should be no big surprise. As we proceed through analysis of the various RM metrics, I expect that no single parameter will have a large correlation factor to valuation, since so many parameters are simultaneously at work. OK, now on to the next Question... 2) Is there a relationship between "Change in Analyst Reccommendation" and "Change in Price"? We frequently see complaints on the boards about analyst downgrades causing a stock price to tumble. Clearly, there is an implied cause and effect relationship here. But we also see cases where a company will issue a profit warning, which simultaneously has two outcomes: The stock price drops, and a host of analysts issue a downgrade. Here, the analyst downgrade is the result of something happening to the company (and its stock price), not the cause. OK, whatever! To answer question 2, I fed the calculator with xvalues consisting of "change in analyst recommendation" and yvalues of "% change in price". Depending on which perspective is appropriate, 2 equations can be derived from the best fit line for these xy values. If % Price change results from an analyst upgrade/downgrade, the relationship was: % Price Change = (14.56 * Analyst Rec Change)  6.98 Solving the above equation for a couple of specifics, we find An analyst downgrade of 1 full point caused a price drop of 21.54% No analyst change resulted in a price drop of 6.98% An analyst upgrade of 1 full point caused a price rise of 7.58% If on the other hand an analyst upgrade/downgrade results from some company news and resulting price change, the relationship is: Analyst Rec change = (% Price Change + 6.98)/14.56 Solving for a couple of specifics again, we get A price change of +10% resulted in 1.16 points of analyst upgrade. A stable price resulted in 0.48 points of analyst upgrade. A price change of 10% resulted in 0.21 points of analyst downgrade. Once again, for the statistically nonchallenged, Mean analyst change = .12 Mean Price change = 8.87% Covariance = .80 Correlation = .24 Regr'ion d(Price) on d(rec)= 14.56 Regr'ion d(rec) on d(Price)= .004 The correlation of .24 is still fairly weak, but somewhat stronger than the correlation in the previous data. Finally, I looked at one last potential relationship: 3) Is there a relationship between the change in price from 3/2/01 to 3/31/01 and the absolute analyst rating on 3/31/01. This might be rephrased "Do analyst ratings correlate with short term relative strength?" Without belaboring the mechanics any further, this set of data had a covariance of only 0.24, and a correlation of only 0.04, indicating virtually no relationship. OK, enough fun with numbers for today. COMING SOON: What is the relationship between the RM metrics "Gross and Net Margins" and the valuation metrics "P/E ratio and P/FCF ratio"? (PEGs, YPEGS, Benjamin Graham Indices, and other valuation techniques can be built up from these by the "interested student".) I promise the next post will be shorter, since I won't have to explain so much background info. Ralph Company Tickers of included Companies (Disclosure: I own the bold ones) ABI ABT ABV ADBE ADI ADP AEOS ALTR AMAT AMD AMGN ANF AOL APCC AVNT BARZ BBBY BC BGEN BLCCY BMCS BMY BNG BRCD BRCM BUD CA CAH CCE CCL CHKP CHRT CIEN CMVT COH COST CPB CREE CSCO CSG DA DCX DELL DEO DG DIS DNA DRI EAT EBAY EDS ELN ELY EMC ERTS F FD FMX GE GENZ GIS GLW GM GPS GUC HAS HD HDI HINKY HNZ HON HWP IBI IDTI IFX IMNX INTC INTU ISCA IVGN JDSU JNJ JNY KM KO KSS LIZ LLTC LLY LOW LSCC LTD LUV LVMHY MACR MAT MAY MCD MCHP MDT MEDI MERQ MMM MSFT MSIM NOK NOVL NT NTAP NTT NYE OAT ORCL OSI PALM PAYX PEP PFE PHG PMCS POC PZZA QCOM RATL RCL RL RTRSY S SBC SBUX SEBL SGP SLE SNPS STM SUNW SYMC SYY T TGT TJX TLB TM TOM TOY TSCO TSM TXN TYC UN V VFC VZ WMT WWY XLNX YHOO YUM

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