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Author: GrandpaRalph Big red star, 1000 posts Old School Fool Add to my Favorite Fools Ignore this person (you won't see their posts anymore) Number: of 18386  
Subject: Fun with Numbers: Part 1 Date: 4/4/2001 9:36 PM
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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 "x-parameters" (say net margins for the 160
companies) and a set of "y-parameters" (say Price to Free-Cash-Flow
ratio for the same 160 companies) and perform a regression analysis to
identify the "best fit" line that passes through these 160 x-y 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 non-linear 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 un-rulemakerness 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 un-Foolish
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 x-values 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 y-values, 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 x-y 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
y-intercept 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 non-challenged, 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 x-values
consisting of "change in analyst recommendation" and y-values of
"% change in price". Depending on which perspective is appropriate,
2 equations can be derived from the best fit line for these x-y 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 non-challenged,

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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