Report earned 105% and is a complete valuation of the company based upon CAPM and the Dividend Discount Models. Includes regression analysis of macro variables, figures from conference calls and 10Ks, and a fair market stock price. (Not to be used as investment advice)

1. Equity Analysis
Report
Written By: Brett Bernstein
Date: March 10th 2008
Business 431 Equity Analysis
Professor: Gorman

2. 1 EXECUTIVE SUMMARY 4
2 INDUSTRY OVERVIEW 5
2.1 WFMI SPECIFIC 5
2.1.1 WFMI CONFERENCE CALL 5
2.2 COMPETITOR ANALYSIS 6
2.2.1 KROGER 7
2.2.2 SAFEWAY 7
3 RATIO ANALYSIS 8
3.1 CURRENT RATIO 8
3.2 ACCOUNTS RECEIVABLE TURNOVER 8
3.3 DEBT TO ASSETS 8
3.4 NET PROFIT MARGIN 9
3.5 PRICE TO EARNINGS (P/E) 9
4 E[RWFMI] 9
4.1 CAPM 9
4.1.1 WHOLE FOODS MARKET 9
4.1.2 INDUSTRY PORTFOLIO 10
4.1.3 FORMING THE DISCOUNT RATE 11
4.2 FAMA AND FRENCH 3 FACTOR MODEL 11
4.2.1 WHOLE FOODS MARKET 11
4.2.2 INDUSTRY PORTFOLIO 12
4.2.3 FORMING THE DISCOUNT RATE 12
5 SALES GROWTH 13
5.1 HISTORICAL 13
5.2 EXPECTED 13
5.2.1 FACTORS 13
5.2.2 FORECASTING SALES 16
6 VALUATION MODELS 16
6.1 DIVIDEND DISCOUNT MODEL 16
6.1.1 DRIVERS 17
6.1.2 THE PROCESS 17
6.1.3 THOUGHTS 17
6.2 COMPARABLES MODEL 18
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3. 6.2.1 PRICE BASED ON P/E 18
6.2.2 PRICE BASED ON PEG 18
6.2.3 PRICE BASED ON P/S 19
6.2.4 PRICE BASED ON P/B 19
6.2.5 PRICE BASED ON P/EBITDA 19
6.2.6 WEIGHTING 19
6.3 THREE STATEMENT DISCOUNTED CASH FLOW 20
6.3.1 INCOME STATEMENT 20
6.3.2 BALANCE SHEET 22
6.3.3 STATEMENT OF CASH FLOWS 22
6.3.4 EQUITY EVALUATION 23
7 CONCLUSION 24
8 WORKS CITED 25
3

4. 1 Executive Summary
This report values Whole Foods Market (WFMI). I approached the company with a
holistic viewpoint and centered on their established position as the nation’s leading
retailer of organic foods.
As a preliminary measure, I organized historic ratios and compared them to an
industry average derived from Robert Morris Associates and other online databases.
The differences provide both positive and negative feedback on Whole Foods
Market, relative to its industry. The most outstanding divergence came with a higher
debt to asset ratio than industry standard. I praised this for its high leveraging effects
and the resulting benefits.
In order to determine an appropriate rate of return, I utilized both the CAPM and
Fama-French 3 Factor models. I found a beta near 1 for CAPM but the FF3 betas
varied significantly. In addition, I rejected the null hypothesis that alpha equaled 0.
This suggested a high probability of abnormal returns associated with idiosyncratic
risk. Then to lower standard error, I found beta’s associated with a portfolio of
WFMI and two of its closest competitors. However the beta’s used to forecast
expected return came from the company specific regressions. The appropriate
returns for CAPM and FF3 were 8.60% and 18.35% respectively.
The next process forecasted growth. The best fitting regression of macro economic
data came from the commingling of irrigated cropland value, disposable income,
percentage of corn genetically engineered and the number of certified organic acres
within the United States. I forecasted each factor and determined sales growth out to
2015. Sales were predicted to grow more than 13% in 2008, but only 3% and 4% for
the following two years.
I then used these tools and assumptions to value WFMI with 3 distinct models:
dividend discount, comparables and 3 statement discounted cash flow. I compared
the CAPM and FF3 discount rates when relevant.
The dividend discount model valued the dividend stream from now until infinity. It
concluded a theoretical market value of $12.82 per share and $5.19 per share based
on CAPM and FF3 respectively.
For the comparables model, I related Whole Foods Market with 8 of its competitors.
The resulting fair market value was $14.89 per share with a significant portion
coming from the comparison of P/E and PEG ratios.
The 3 statement DCF emphasized a faulty depreciation schedule that was built on an
foundation of assumptions. The resulting theoretical prices reflect the discrepancy.
The WACC, when operating with CAPM measurements, found a stock price of
$485. The FF3 influenced WACC concluded a fair price of roughly $150 per share.
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5. As a final step, I merged all models based on a subjective weighting system to
conclude a final theoretical stock price of $37.30. I compared this to the actual price
of $37.04 and decided that Whole Foods Market is insignificantly undervalued and
therefore fairly priced as it currently trades.
2 Industry Overview
Whole Foods Market resides in SIC #5411 Retailers – Grocers & Meats and within
that, Primary NAICS #445110 Supermarkets and Other Grocery (except
Convenience) Stores.
2.1 WFMI Specific
Whole Foods Market is the largest retailer of natural and organic foods in the United
States. Opening it’s first location in Austin, Texas during mid 1980, it currently
operates 207 stores throughout the U.S., District of Columbia, Canada and the U.K..
Company expansion is primarily attributed to the acquisition of other natural food
supermarkets, most recent being Wild Oats Markets. Whole Foods Market bought
their biggest rival for an estimated $565 million dollars. The only reason the FTC
ruled against a breach of antitrust laws was the growing competition from traditional
grocers entering the organic business.
Long-term prospects remain upbeat. This is based on WFMI’s socially responsible
reputation, stable position as the largest health food retailer and as a niche supplier
for the current wellbeing trend. Market analysts connect the current and forecasted
concerns of obesity with future sales growth. In addition, the aging baby boomers,
with their significant control of the wealth, remain eager to look young and healthy.
The company focus and product makeup quickly aligned with this trend and Whole
Foods Market expects to capture this large target population going forward.
Whole Foods Market has a current market cap of $6.7 billion and common stock on
the order of 138 billion shares. Their current dividend yield measures 1.5% and they
hold an A rating for financial strength. Whole Foods Market currently trades under
the ticker WFMI at a price of $37.04 per share.
2.1.1 WFMI Conference Call
Whole Foods Market reported first quarter results on February 19th 2008. A
recording of the conference call allowed access to very relevant internal and external
information. Walter Rob and AC Gallo, Co-Presidents and Chief Operating Officers
begin by representing positive reports. Total sales increased 31% to $2.5 billion and
even more specifically, identical store sales, which excluded relocations and
expansion were up 7.1% on top of 6.2% last year. The average transactions on a
weekly basis increased 5% to $3.5 million and the average “basket size” reached
$36, an increase of 4%.
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6. They continued by discussing the expansion of the private label brand with SKU
counts up 15% year over year and representing 19% of sales.
After these reports, the executives decided to switch their methods of previous
reporting. They admitted they usually report gross margin and store contribution for
all stores, but this time they decided to only report results for identical stores. The
figures were obviously impressive and signaled a red flag to look at overall reports.
Luckily the question and answer section later addressed this.
When asked about the drop in contribution margin from 6.5% to 3.5%, Mr. Gallo
referenced the recent acquisition of Wild Oats Markets. This had been mentioned
earlier in the scripted portion of the conference call as a negative influence on a
short-term basis with expected returns growing appreciable in the future.
For contribution and gross margin specifically, Whole Foods Market responded by
focusing on the continuing unification of the two entities. Mr. Gallo expects “gross
margins to improve as the conversion of Wild Oats to Whole Foods completes.” He
described Wild Oats Markets as having higher prices and lower labor than compared
to Whole Foods Market. Those prices are now being cut and the contribution margin
needs time to correct.
Store size efficiency also came as a response. Whole Foods Market plans to see a
significant increase in return on capital by adjusting store size to meet the local
market demands.
Questions were also posed on a macro level, specifically inflation. Inflation on
conventional food items, like milk, has increased significantly, driving up prices.
However, this price hike has yet to influence the organic market. A question arose
concerning the sustainability of this trend.
Whole Foods Market responded by citing ethanol as a large contributor to the higher
demand and increased prices, particularly corn, grain and wheat. Walter Rob
deemed the era of cheap generic food to be over. He figures this will reduce the
price gap between generic and organic items, resulting in a positive influence on
sales.
2.2 Competitor Analysis
The supermarket industry thrives on volume. Typical net profit margins of 3% arise
from a large portion of sales from perishable goods. Combine that with the
overbearing threat from Wal-Mart, who’s notorious for driving down prices and
taking over any territory it enters. The resulting industry leaves little room for error
in the pursuit of success.
In fact, Wal-Mart has forced the union of many smaller grocers who together hope to
hold their ground from this revolutionizing entity. Kroger, Albertsons and Safeway
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7. led the scramble to scoop up as many small chains as possible. Albertson’s however
was the first casualty of the three when in 2006 it was forced to sell to Supervalu,
which currently sits as the number five grocer, behind Safeway, Costco, Kroger and
Wal-Mart.
The start-up of Internet grocers failed horrible, but they continue to hang in there.
Current growth is seen in oversees markets, particularly the U.K. and hopefuls like
Amazon.com are entering the industry in hopes of uplifting the market.
Whole Foods Market fortunately has a unique selling proposition, which ranks them
atop their class as the number one retailer of natural and organic grocery. Trader
Joe’s could be considered their closest competitor, however; they are significantly
smaller and remain private. Because of the massive range in products offered by
Wal-Mart, I looked at Kroger and Safeway for a competitor analysis.
2.2.1 Kroger
Kroger is the nation’s leading pure grocery chain. However, Wal-Mart used their
low price strategy to surpass Kroger as the leading seller of groceries. In an attempt
to overcome this, Kroger diversified with expansion into jewelry and general
merchandise, but this has little influence on total sales.
Kroger has focused on mirroring the Wal-Mart strategy and made notable cuts in
prices. In addition, they added a private organic label called “Organics for
Everyone.” Almost 60 products now fall into this category with many priced lower
than Kroger’s Naturally Preferred brand.
Kroger has expanded through competitive acquisitions and price decreases. They
currently trade for $25.18 per share under the symbol KR. They have a market cap
nearing 17 billion and a dividend yield of 30%.
2.2.2 Safeway
Safeway is one of North America’s largest grocery retailers. Known mostly as Vons
throughout California, it holds many other titles across North America and Mexico.
In addition, Safeway operates 30 food-processing plants, which account for nearly
20% of its private label production. A major portion of success is attributed to their
1,300 in store pharmacies; they lead this category among U.S. grocery retailers.
In addition, Safeway provides third party gift cards to consumers resulting in
significantly boosted sales. These factors could single handedly be responsible for
their successful combat against retail giant, Wal-Mart.
Continuing with growth, Safeway has made major strides to compete in the organic
industry. They expanded their “O Organics” line and are currently growing stores to
fit an increased market share of natural foods.
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8. Safeway has a market cap of $12.7 billion, and currently trades for $28.98 under the
ticker SWY.
3 Ratio Analysis
The most notable implications from this analysis came in the comparison of 2007
WFMI values with that of the industry for the same year. The industry compared
was SIC #5411 Retailers – Grocers & Meats. I considered using SIC #5499, which
was Retailers – Health Foods & Vitamin Stores however every online analyst report
online used #5411. Unfortunately, I was unable to find industry ratios for all that I
would liked, but enough are present to generate a theory between Whole Foods
Market and the industry. (Notable ratios are discussed below, the complete sheet
available in the appendix)
3.1 Current Ratio
The current ratio measures short-term liquidity. The WFMI ratio in 2007 of 0.851
means they had $0.85 in current assets for every $1.00 in current liabilities. The
industry average was 0.980. A high current ratio is typically better as it indicates
liquidity, but it conversely could reflect an inefficient use of cash. With that said, I
would have preferred a higher ratio, but with the entire industry under 1, I am less
concerned than I would have been. I presume this industry works better with more
leverage on their money.
3.2 Accounts Receivable Turnover
This value measures how fast a company collects on their owed payments. Whole
Foods Market had a A/R turnover of 24.39 which means they collected their
outstanding credit and could therefore reloan the money 24.39 times that year. An
easier number to conceptualize comes in the derivation of this, the average collection
period.
For 2007, Whole Foods Market had an average collection period of 14.97 days,
while the industry average was 6.44. I found this disturbing because the longer it
takes to receive payment, the more likely the borrower will default. In addition, the
time value of money indicates they are losing money by collecting it later. The
earlier they receive the money, the earlier they could finance with it and begin
earning a return.
3.3 Debt to Assets
Whole Foods Market had a debt to asset ratio of 0.546 while the industry stayed
around 0.206. This means that WFMI takes on more debt compared to the amount of
assets they control. At first debt sounds bad, but personally I am pleased to see it.
Debt means leverage. Whole Foods Market used other people’s money to grow, and
it suggests they will continue to grow at a faster rate than they would otherwise be
able to.
8

9. The obvious downside to this would be the interest trap and eventually defaulting.
Luckily for WFMI, the interest rates are currently falling, so it’s even cheaper to
acquire borrowed money. While this may cause investors to lend less, WFMI
currently holds an “A” rating which should provide them with a significant pool of
money going forward.
3.4 Net Profit Margin
This ratio demonstrates how much profit is generated from every dollar in sales.
Whole Foods Market formulated roughly $.0277 cents in profit for every sales
dollar, while the industry average neared $.016 cents. Although both seem relatively
small, Whole Foods Market is almost twice as efficient.
3.5 Price to Earnings (P/E)
This measure shows how much an investor is willing to pay for every dollar in
earnings. For perspective, a typical P/E ratio sits in the 20 range, but during the
technology boom in the early half of this decade, some companies had multiples
around 200 to 300.
WFMI in 2007 had a multiple 31.628 while the industry average was significantly
smaller at 15.69. I attributed this to a trend factor. Investors were willing to pay a
higher price for WFMI earnings because the healthy lifestyle had been gaining
momentum. It was rational to assume this would continue and therefore WFMI sits
in a prime position to pick up future earnings well above what they were then.
Whether this comes about or not, time will tell.
4 E[Rwfmi]
4.1 CAPM
This capital asset pricing model for expected return takes into account the risk free
rate, expected return on the market and the corresponding beta found from a
regression analysis (shown below.)
4.1.1 Whole Foods Market
The beta estimate is slightly above 1. This indicates that WFMI moves quite
similarly to the S&P 500 index, but if anything it is slightly more volatile. The alpha
estimate should be 0 so it’s not included in forecasting, but interesting to look back
on. This positive alpha estimate shows the abnormal return associated with Whole
Foods Market based on idiosyncratic risk.
9

10. 4.1.1.1 Idiosyncratic Risk
Idiosyncratic risk is any risk above the fully diversified market risk. On average, it
should not be rewarded. To test if it was priced in WFMI, I tested the following
hypothesis:
Ho : α = 0
Ha : α does not equal 0
The output above is a more detailed version of the CAPM regression output
mentioned earlier. The confidence level suggested I could only be 19.589% sure that
alpha = 0. Stated otherwise, I am very confident that idiosyncratic risk was rewarded
for WFMI based on excess returns above the risk free rate and excess returns of the
market.
4.1.2 Industry Portfolio
In order to lower total risk and standard error, I grouped three related companies
together and analyzed those results to form an industry version of the CAPM. The
three companies correlated were Whole Foods Market, Kroger, and Safeway. The
regression output was:
Note the lower alpha and lower standard error in comparison to the output
specifically from WFMI.
4.1.2.1 Idiosyncratic Risk
Then I tested for the return associated with idiosyncratic risk in the diversified
portfolio.
Ho : α = 0
Ha : α does not equal 0
This more diversified model did increase the confidence that idiosyncratic returns
were equal to zero. However, the 27% was hardly offered a significant reason to fail
to reject the null. I was roughly 73% sure that idiosyncratic risk was rewarded.
10

11. 4.1.3 Forming the Discount Rate
In order to finally create the expected return on equity through the CAPM model, I
had to develop 2 more assumptions. I first
assumed the return on the market going
forward would be 8% annually. I felt the
days of 10.3% returns were over. I
referenced inflation, earnings growth, dividend yield and added a personal opinion.
The other assumption was the risk free rate, which I took simply as the current 1year
t-bill. Notice I chose the beta associated with WFMI alone, and not the industry. I
chose this because I felt the difference between WFMI and its competitors were
insignificant on a statistical level but rather significant on a tangible level. Using a
beta that included relatively different companies would offer more assumptions than
it would clear up.
The equation for the r based on risk was: E[Rwfmi] = rf + β*(E[Rmkt] – rf)
4.2 Fama and French 3 Factor Model
The FF3 model is the CAPM plus two factors other than market risk: size and value.
This model accounts for the historical fact that small cap tends to outperform large
cap and value stocks typically outperform growth stocks.
4.2.1 Whole Foods Market
I obtained data from Ken French’s website and used the returns of small minus big
(SMB) and value minus growth (high minus low b/m) in the following regression:
I could tell right away this was a poor model for WFMI. The R2 value of .122
indicated these variables together can only predict 12% of what WFMI will do.
However I did use these numbers to find the systematic and idiosyncratic risk, along
with a discount rate.
4.2.1.1 Idiosyncratic Risk
As an alternate method of finding idiosyncratic risk, I utilized the R2 value. This
value is the proportion of systematic risk to total risk. I found total risk of WFMI by
using an excel function for variance and discovered it to be 0.00842. Therefore the
systematic risk was found by multiplying that value by the R2.
11

12. Then the idiosyncratic risk could be deduced by subtracting systematic risk from
total risk.
The most important thing I noted was that idiosyncratic risk made up 87% of the
total risk. This could be severely reduced by diversifying a portfolio.
4.2.2 Industry Portfolio
I grouped together WFMI, KR and SYY again this time with a regression of the FF3
factors and found the following:
These factors had a much greater connection with the WFMI returns. The R2
indicates these 3 factors working together explain 94% of the IRR associated with
WFMI.
4.2.2.1 Idiosyncratic Risk
I then deduced the idiosyncratic and found it to be
significantly smaller in both size and proportion of
total risk than WFMI alone.
4.2.3 Forming the Discount Rate
The formula for expected return on WFMI equity
based on FF3 was:
E[Rwfmi]= rf + βmkt, wfmi*(E[Rmkt]-rf) + β2,wfmi *
(E[Rsmb]) + β3,wfmi * (E[Rhml])
To better conceptualize, it’s the same process as the CAPM but for 2 more factors.
Note the forecasts above appear particularly big, especially the E[Rmkt]. These
were in percent and monthly terms because of the format of the data derived from
Ken French’s website. So the forecasted E[Rmkt] was actually 0.66% monthly, or
8% annually. The E[Rsmb] and E[Rhml] were simply the averages of the previous
60 months. The risk free rate is .0226% which is different than the rf used in
previous models because it’s the latest rate Ken French published. These forecasts,
when combined with their beta’s and adjusted risk free rate give the following
discount rate, or r based on risk:
12

13. Because of the drastic difference between WFMI and the 3 stock industry I found an
r value via FF3 for the industry as well:
There was a drastic difference between WFMI and the industry based on FF3
analysis, but also between FF3 and CAPM.
Moving forward I chose to use only the WFMI specific FF3 even though it offered a
much lower R2 because I wanted to stay consistent with the CAPM.
5 Sales Growth
5.1 Historical
This model allowed for analysis, but offered very little in results. I found the
geometric and arithmetic average in the growth of revenue from 1998 to 2007.
The most important aspect I took
away from this model compared
the 19% average for the previous
10 years with the expected growth
moving forward.
5.2 Expected
Regression analysis was the key to developing expected sales growth. I ran
regressions based on econometrics. I felt macro economic data was the only
influence in determining future sales growth. I collected a pool of data and then
allowed Excel to determine which combinations of factors explained historical
sales. After running nearly 40 regressions, I determined beta values that could then
be used to forecast.
5.2.1 Factors
The 14 factors I chose to test as a influencer of sales were as follows: 3 month t-bill
and 10 year t-note, both real and nominal, GDP real growth, CPI based inflation, real
and nominal prices of crude oil, irrigated cropland value, the amount of acres
certified organic in the United States, the pounds of vegetables available per capita,
13

14. the percentage of corn genetically engineered in the United States, disposable
income and a time trend.
My expectations before regressing were:
This meant that a factor with a positive relationship expected would need to have a
positive beta. If the regression provided a result incongruent with my theories above,
I ignored the outcomes and continued until I did find a model. A snapshot of the
first few regressions is available in the appendix. The most significant regression,
which described the factors that best explain WFMI growth, is:
5.2.1.1 Cropland Value
I forecasted cropland value to have a negative relationship with WFMI sales growth.
As the land used to grow crops gets more
expensive, the crops produced on the land
would also get more expensive. If Whole
Foods Market pays more for their inventory,
then either they take less profit or raise their
prices. If prices go up, sales should go down,
even if each item costs more to the consumer.
5.2.1.1.1 Projection Estimates
I forecasted this factor to increase 20% in
2008 per an online source.
(http://aglines.com/2008_02_14_archive.php)
In addition, as the dollar continues to weaken,
the price of commodities increases, and crops
will rejoice in this category. By 2011 my
assumptions began to overbuild, so I just kept
14

15. it at a constant growth of 5%.
5.2.1.2 Disposable Income
Disposable income in its nature has a positive relationship. Organic food is deemed
a luxury; the more money available for luxury purchases should influence sales
positively. The same direct relationship is true for the opposite, less disposable
income, less sales in organic food.
5.2.1.2.1 Projection Estimates
Based on a CNN article, unemployment is
projected to increase from 4.7% to 5.7%.
This will clearly hit disposable income,
and is just another indicator or result of the
impending U.S. recession.
(http://money.cnn.com/2008/02/20/markets/morningbuzz/index.htm?postversion=
2008022010) My forecasts reflect my opinion on the economic state of the U.S.
economy.
5.2.1.3 % of Corn Genetically Engineered
The percentage of corn that is genetically engineered should have a negative
relationship to sales. I used the economic theory of supply and demand to justify this
prediction. If more corn is produced opposite to the Whole Foods Market standards,
then the market is flooded with a cheaper grade corn, and subsequently an even
cheaper price. With the price of genetically engineered corn decreasing, it would
send those consumers that ranked the marginal benefits of organic slightly above the
marginal cost of organic to change sides when the prices of the competition goes
down.
5.2.1.3.1 Projection Estimates
This factor forced me to balance the costs of rising population with the benefits of
organic food and the trend associated with it. While historically, this corn
percentage has been growing at an average annual rate of 15%, I predicted
significantly slower growth moving forward. I felt the market reached a balance
with a 3:1 ratio of genetically engineered corn to organic. I projected 1% growth
from now until forever.
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16. 5.2.1.4 Organic Acreage
The last relevant factor, organic acreage, should have a positive correlation with
WFMI sales. More organic land means more organic crops. This translates to lower
prices and increased sales.
5.2.1.4.1 Projection Estimates
I projected a slower rate of growth than the
22% annual seen for the last 10 years. I
referenced once again the weakening U.S.
economy and increased price of farming land.
However I continue to suspect significant
growth as the percentage of land allotted to
organic farming remains far underweighted
when compared to other countries. It’s nearly
6% of land in America, but a full 23% in
Europe.
(http://www.maf.govt.nz/mafnet/rural-
nz/sustainable-resource-use/organic-
production/international-developments-in-
organic-agriculture/)
5.2.2 Forecasting Sales
Now that I found the betas for each factor and forecasted a value, the process of
developing sales growth was simple. I took the alpha associated with WFMI’s
historical sales values and added that to the multiplication of each beta and its
corresponding value for the year.
6 Valuation Models
6.1 Dividend Discount Model
This model presents an absolute based stock price. I felt strongest about these results
and my ending weights reflect such.
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17. 6.1.1 Drivers
The ROE driver reflects my personal opinion of the macro environment going
forward. I feel the U.S. economy sits on the brink of a recession and as a result,
organic purchasing will be hit with a decline. To emphasize this, I forecasted a
significant drop in ROE for 2008 through 2010. But then I figured the market would
correct itself and the ROE would spike to levels as high as 20%. The final ROE
equals the discount rate and remains constant for all time.
The payout ratio adheres to a similar pattern. I suspect WFMI will payout less when
times are rough, and more when times are good.
6.1.2 The Process
Since I could only use the equation: P = A/r-g when variables were constant, I started
the model in 2015. “A” totaled the dividend expected based on the book value of
equity, ROE and PO. “r” was set to the discount rate determined by either CAPM or
FF3, and “g” was ROE times the PB. All of which were built on assumptions.
After determining the 2015 price of owning that dividend stream with constant ROE
and PB, I stepped back to 2014. Now the “A” value equaled the price in 2015 plus
the dividend for 2014. This value is in 2015 dollars, so before moving on, I had to
discount it at the CAPM or FF3. This same process was completed for all earlier
years, discounting one year at a time, until finding a price in today’s dollars.
6.1.2.1 CAPM
The CAPM discount model resourced was:
The theoretical price today and forecasted for 2008 and 2009 are shown below:
6.1.2.2 Famma French 3 Factor
The FF3 discount model resourced was:
The theoretical price today and forecasted for 2008 and 2009 are shown below:
6.1.3 Thoughts
Both discount rates presented WFMI as currently overvalued. I felt this to be a
significant discovery especially since the discrepancy between the actual and
17

18. theoretical prices were different on an order of 3:1 and 6:1. I attribute the difference
between actual and theoretical once again to be based on incorrect assumptions.
However, it’s also important to note the lack of consideration for events
unforeseeable. This model simply assumes no incredible news will come about.
Whether that news derives internally with executive scandal, or externally with
scientific findings that revolutionize the industry, it accounts for neither.
6.2 Comparables Model
This model determined a relative value of Whole Foods Market in relationship to its
competitors. Stock prices alone tell nothing. Company A at $50 per share may be
cheaper than Company B at $5 per share. I employed a number of multiples: P/E,
PEG, P/S, P/B and P/ EBITDA to place the stocks on a comparable level. More
specifically, I took the mean for all competitors of whichever multiple I was
evaluating and then multiplied that by the specific value for WFMI. Example: to
find a price based on Price to EBITDA:
Price WFMI = Prices of Competitors * EBITDA WFMI
EBITDA Competitors
Then I weighted the resulting prices based on personal preference and determined an
overall stock price. My results deemed that WFMI was overvalued at $37 per share,
and should realistically sit at $14.59 per share.
I compared Whole Foods Market to 8 competitors. The closest competitors were
Kroger and Safeway. I also looked at United Natural Foods, which deals with the
same product line, but sells wholesale versus retail. Also note, I compared Whole
Foods Market with Wal-Mart. While the two companies currently only share a retail
grocery setup (only a portion of which makes up WMT), their company dynamics
are very different and I almost didn’t include the comparison. However, I suspect
the gap will lessen with time as Wal-Mart attempts to capitalize on the organic trend.
6.2.1 Price based on P/E
Known as The Multiple, the P/E ratio is the mostly commonly referenced valuation
ratio in the finance community. It presented me with a price closer to what WFMI
trades at today, compared to the other multiples studied: $20.50 theoretical versus
$37.04 actual.
6.2.2 Price based on PEG
This was my favorite of the multiples and I weighted it as such. The price of $16.20
that it developed considered all that the P/E considered, plus accounted for expected
annual growth. I stayed consistent and forecasted growth annually as the average of
my forecasted sales growth from 2008 to 2015. This offered an expected annual
growth of 10.19%. However, to determine the competitor’s growth factor, I trusted
analysts from Bank of America, Citigroup, UBS and many others.
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19. 6.2.3 Price based on P/S
This factor should give a similar outcome to P/E. However it determined a fair price
just below $3 dollars per share. I’ve determined the discrepancy resulted from the
range of companies studied. Whole Foods Market recorded sales only 1.5% that of
Wal-Mart while the stock prices sat at a 3:4 ratio.
6.2.4 Price based on P/B
This multiple compared the company’s market value (price) to the book value
(accountants). Most notable here, is WFMI recorded a book value per share of 3.92
while the average of the 8 competitors rounded to 2.46. A higher P/B ratio could
mean the company is overvalued, and the model predicted such. It felt WFMI’s fair
market value was $9.63.
6.2.5 Price based on P/EBITDA
This multiple is beneficial to observe because it’s calculated before financial and
accounting decisions have the opportunity to influence. On the other hand, it’s a
non-GAAP measure. Which is risky because it offers the company more freedom to
adjust news for deception. I took the two sides into consideration when weighting
the importance of this measure.
This factor determined the fair value of the stock to be $2.09 dollars per share. I
attributed this severe drop in value to be consistent with the reasons stated for Price
to Sales.
6.2.6 Weighting
I weighed the PEG ratio most significant because it accounted for growth. However
the subjectivism concealed within the estimates constrained me to keep its influence
at 50%. The next most important was P/E at 25%. So I felt 75% of my model
should take into account earnings per share. The remaining portion of weight was
split up through the less accurate multiplies. I gave P/EBITDA the smallest portion
because of the possible managerial trickery that could result from the lack of
regulation on this reporting.
The resulting price takes those weights into effect:
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20. 6.3 Three Statement Discounted Cash Flow
6.3.1 Income Statement
The historical section is significantly simpler than
the company disclosed version. However, Sales,
Operating Income and Net Income were controlled
for accuracy. (see appendix)
Most notable is the net income of 2007 compared
to the proforma 2008:
Net income in 2007 totaled $182 million while
2008 is predicted to increase roughly 70% to 308
million. This is significantly greater than previous
historical years and forces attention to the drivers.
Sales forecast drivers were put through the rigorous
regression method mentioned before and
determined based upon significant macro variables.
The ratio of cost of goods sold to sales was taken
by averaging that common size for the previous 10 years.
General and Administrative Expenses were determined on historical averages, but
only for the past 5 years. 2001 and older exhibited G&A Expenses nearing 30%
whereas ever since, they neared 3.5%. I felt Whole Foods Market made significant
adjustments to lower this common size value and future values should reflect the
change.
Non-recurring expenses are typically unpredictable, so I forecasted those drivers as
the average of the previous 10 years as well. When in doubt, the best indicator of the
future is the past.
Operating Income is set at the same rate as Sales Growth. I figured with the depth of
study that went into forecasting sales growth, it would be a stronger variable than
any I could subjectively construe. In addition, there is most certainly a strong
positive correlation between sales growth and operating income. However in order
to ensure that EBIT equaled the disclosed amount from WFMI in the historical, I
utilized goal seek and displaced the difference in the other account. Moving
forward, I had that other account grow at a 3% rate with the intention of just keeping
with inflation.
The short and long term interest rates were set as the average percentage for the
previous 12 months. I decided to use an average as opposed to most recent
percentage, because I am forecasting out until 2015 and I felt there to be a stronger
foundation when incorporating more data points.
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21. So with all those drivers explained and keeping mostly congruent with intuition, I
expected a closer correlation between the net incomes in 2007 and 2008. The only
other explanations would have to come from depreciation or interest expense.
6.3.1.1 Depreciation Schedule
Let me preface this section by stating that this is the most inaccurate depiction in the
entire report. Inaccessibility to information on top of using drivers, which are almost
certainly mistaken, offered the discrepancy.
However, it was imperative I constructed this model, so when presented with an
obvious imprecision, I took an unrealistic approach to “sweep it under the rug.” All
“easy fixes” will be presented within the report and explained in detail. My intention
was not to get away with a secret financial account, but rather learn from the holistic
approach to the concept.
The first step in forecasting the depreciation schedule incorporated the fixed asset
turnover driver, capacity utilization, and projected sales growth.
The fixed asset turnover measures a company’s effectiveness in generating income
from property, plant and equipment investments. The higher the turnover, the more
efficient the company is. I began forecasting this driver based upon a clever thought
process that took into account the national economy and its relation to expenditures.
However, I was forced to implement a simple fix later on. I changed the turnover to
a value that would simply offer a Net PPE EOY that was more in line with historical
values. Before implementing this method, I would discover values hundreds of
billions of dollars too big. This in turn, affected the depreciable amount and the
entire model was completely unrealistic, even more so than presently.
The Net PPE required at EOY for Max Sales Level was derived by multiplying the
average level by 2 and subtracting off last years Net PPE. The Average Net PPE was
found by taking the max sales possible at full capacity and dividing it by the Fixed
Asset Turnover. These all utilized expected revenue and max sales possible at full
production capacity, which were influenced by sales growth and capacity utilization.
To better explain, I found what level of Net PPE was required for WFMI at the end
of the year based upon sales assumptions. This value then allowed me to employ
goal seek and discover the Gross PPE level, or in other words, the amount of PPE
that WFMI would need to purchase to end with that final amount, net depreciation.
Now looking at the drivers specific to the depreciation schedule, I have everything
depreciating over 10 years to a value of 10% initial cost. However, I set all sales to
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22. occur in 5 years from the purchase date and sold with a recoup of 15%. Absolutely
none of these assumptions are correct, however the actual data eluded me. I figured
these assumptions somewhat accurately reflect a weighted average between assets
depreciated over 5 to 32.5 years.
6.3.2 Balance Sheet
The balance sheet uses drivers for accounts receivable, inventory, accounts payable
and cash levels.
6.3.2.1 Drivers
I set accounts receivable via Days A/R, which estimates the average number of days
it takes to collect. I had no reason to think this value would change, so I kept it
constant at the average of the previous 10 years, 24.39.
The same theory held true for inventory, accounts payable, and the cash to sales
ratio.
6.3.2.2 Balancing
In order to balance the balance sheet I forecasted Whole Foods Market increasing
their short term debt via notes payable. Yet I still must call your attention to this
account from 2008 on:
Notice the prevalence of negative numbers. This is due to the balancing efforts via
goal seek and it clearly reflects the lack of precision involved with this model. The
notes payable essentially also became notes receivable. Nonetheless, this is an error
I was forced to look past in the sake of overall education.
Later on, a second account was utilized to keep with the balancing efforts. Other
assets needed to be created to store a remainder that occurred when working with the
statement of cash flows.
6.3.3 Statement of Cash Flows
This statement is mostly a summary of previous models. It doesn’t incorporate
drivers directly, but rather links and manipulates earlier findings, which are based on
assumptions. The biggest issue with this statement involved the cash at end of year.
This number should be the same as the cash in the balance sheet for that year as well
as the cash at beginning of year for the following year. In other words, the amount
of zero was actually the historical ending cash for 2007, the $374 million was found
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23. on the proforma income statement via the cash to sales driver. Take note on how
that same ending monetary value is found in the subsequent column, 2009.
In order for that to work, I forecasted cash for years 2008 to 2015 on the balance
sheet via the driver. Then when discovering I was getting different values, I once
again used goal seek to set cash at end of year to the appropriate value by changing
other assets. I used a new account as opposed to changing short-term debt again
because I would have then been unable to balance the balance sheet otherwise.
6.3.4 Equity Evaluation
6.3.4.1 Cash Flows
6.3.4.1.1 Cash Flows from Operations
This was a simple formula that developed the cash involved with the regular day
workings of Whole Foods Market. It was found by multiplying operating income by
the tax shield and adding back depreciation. Depreciation isn’t included in the tax
shield because “nobody writes a check for depreciation.”
6.3.4.1.2 Cash Flows from Capital Spending
This portion of the valuation method focuses on the accountants and the structure
they adhere to. It’s important to remember that the purchase of PPE used in this
equation is derived from the goal seek of the net PPE which was found by drivers.
6.3.4.1.3 Cash Flows from Change in Working Capital
This category bridges together the accounting and financial worlds. Because
valuation is a financial act, I must keep that mindset, however the data explored is
written by accountants, so the two are forced to commingle. This category accounts
for the difference in timing between when one transaction is deemed an asset or
liability based on the two perspectives.
6.3.4.2 WACC
This stands for the weighted average cost of capital and is simply an effective
discount rate. For this report, it weights the expected return on equity (derived from
either CAPM or Famma French 3 Factor) and the expected return on debt, based on
YTM.
I labeled the yield to maturity 5% based on the WFMI 10k, and found that the weight
of debt to equity neared 31%. I determined this by taking total liabilities and
dividing by the market value of equity (market capitalization.) This offered a more
reasonable ratio than when compared with the heavily altered balance sheet.
The returns on equity were inserted into separate WACC equations and weighted
proportionate to debt. The subsequent discount factors were as follows:
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24. This means according to Fama-French, WFMI should expect to earn a greater rate of
return for the risk they assume when compared to CAPM. And even more
specifically, that the other two factors in Fama-French, size and b/m, are specifically
responsible for the increased rate, because that’s the only difference between the two
equity valuations. (All of this information could have been derived without
incorporating the WACC, but rather simply comparing CAPM and FF3.)
6.3.4.3 Present Value
The present value of the total cash flows from the three discussed (Operations, Cap
Spending, Change in WC) for all forecasted years required a discount and growth
rate. The discount rates were the two versions of the WACC, while the growth rate
is something new. It is only used in the final present value equation to price the
perpetuity with a constant r and g.
To find the present value of 2008’s cash flows I summed them up and divided by 1+
the WACC. 2009 is the same, but the (1+WACC) is squared, cubed for 2010, and so
forth. To find the present value of the cash flows from 2015 to infinity, grow the
previous PV at the driver growth rate and divide by (WACC-growth.)
I determined a small growth rate of 3.5% because I feel this industry will spike up
now and sit at a significantly high level, but then grow at a sustainable rate as it
enters the mature stage of the business cycle.
After summing all the present values and subtracting off debt, I found rather high
prices for the stock. Based on the CAPM WACC, it neared $485 dollars per share
and sat around $150 dollars per share based on FF3 WACC. These values are
clearly overpriced and reflect the amount of assumptions I was forced to make.
Building on assumptions has an exponential effect as one assumption leads to more
and suddenly all foundation has been limited to a minor detail.
While I learned the most from the 3 statement discount cash flow, I feel it was the
least accurate in depicting a fair value of the stock and my final weighting will
reference that belief.
7 Conclusion
My final task weighed the significance of each model and it’s corresponding stock
price. This allowed me to determine an overall fair market value based on personal
theory.
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25. To begin, I threw out all prices derived from FF3 measures. I felt the additional
factors in Fama-French 3 were not sufficient enough indicators of WFMI. I cited the
unrealistic expected returns of either 18% or 2% found via FF3. Next, I decided the
dividend discount model was the most significant of the models. However, it still
relied on assumptions of ROE and PB, which compelled me to restrict its influence
to 50%. Next significant was the comparables model. It offered a relative
perspective that contrasted the absolute approach taken by the dividend discount
model. However, I felt it was slightly weaker in nature, being relative, so I attributed
only 45% of the final value to it. I felt generous weighting the remaining 5% on the
3 stmt DCF. The model proved more the exponential effects that can result from
assumptions on assumptions than it did an equity valuation. None the less, after
weighting the theoretical prices as stated, I found an overall fair market value of
Whole Foods Market of $37.30. I compared this to the actual price of $37.04 and
decided that Whole Foods Market is insignificantly undervalued and therefore fairly
priced as it currently trades.
8 Works Cited
www.wholefoodsmarket.com
www.mergentonline.com
Robert Morris Associates
www.infinance.com
www.finance.yahoo.com
http://aglines.com/2008_02_14_archive.php
http://money.cnn.com/2008/02/20/markets/morningbuzz/index.htm?postversion=200
8022010
http://www.maf.govt.nz/mafnet/rural-nz/sustainable-resource-use/organic-
production/international-developments-in-organic-agriculture/
http://www.ers.usda.gov/
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