Transcript Modeling Growth Businesses UNM Lecture 9-17

Modeling Income Statements
Alexander Motola, CFA
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Modeling Growth Businesses
How the markets works; expectations
based investing
 What is “Net Revenue”
 Overview of Modeling Approaches
 How does the CEO get paid? (Proxy)
 What does a revenue model look like?
 Modeling Tips
 Specific Examples
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How the market works
Efficient Market Hypothesis
The weak form (prices on traded assets
(e.g., stocks and bonds) already reflect all
past publicly available information) is true
for most of the market, and is most true
for the “best known” stocks
 The strong form of EMH (all info is
instantly priced in) is definitely NOT true;
if you thought so, you wouldn’t be in this
class
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How the market works
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The market is expectations based
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“If we buy the stock today, then each day we move forward,
the headlights of the market move forward one day (let's call
it). The return that I earn over the next twelve months is the
difference between the market's expectations for the first
twelve months relative to the expectations it will have twelve
months hence.You are looking at an expectation set change
one year forward.” – Bill Miller
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Past data is priced in; how the market thinks about future
data is not
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How the market works
What does this mean for modeling
growth rates of individual companies?
 If a company grow revenues 20% a year
for the next 5 years, and you correctly
predicted that, will you outperform the
market?
 Why is modeling growth rates (revenue)
important?
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◦ Revenue is the lifeblood of growth stocks; it is
also the “headliner” for the financial
statements
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What is “Net Revenue”
According to InvestingAnswers.com, “Net
revenue typically refers to a company's revenue
net of discounts and returns”
 Therefore, what you see at the top of the income
statement is a number already adjusted by
management
 Every other statement flows from the income
statement
 Only via astute analysis can you determine
“discounts and returns”; sometimes it is not even
possible to derive this number
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Overview of Modeling Approaches
What is “the fade”?
Top Down
 Company Guidance
 Linear Extrapolation
 Past Performance
 Unit Level/Product
Level
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Sequential (QoQ)
Cyclicality
Deferred Revenue,
Waterfalls, etc.
 The impact of
Acquisitions
 One Special Case:
Retailers
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Modeling: Fade (Growth Rates)
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In theory, all growth rates will become
asymptotic to GDP
◦ Rate of fade
◦ Time Matters – is the fade gradual, or is there
something which causes a step function
(patent expiry, etc.)?
◦ What does the market think, and why?
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Re-Acceleration is a “holy grail” for
investors, because even if the market has
the direction correct, it usually is overly
conservative on the magnitude
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Modeling: Top Down
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Relevant GDP growth rates
◦ How fast is the company growing relative to national or global GDP?
◦ Can you use GDP growth rates by country along with revenue mix by geography?
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Industry Growth Rates
◦ How many players in the industry?
◦ Can you look at all of them?
◦ Read multiple companies’ 10-Ks, etc or industry reports to get industry growth rates
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Taking or Losing Share?
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Modeling: Company Guidance
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Companies often provide short or long term forecasts
◦ Earnings Calls, Analyst Days (often webcast); never in their SEC filings
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Forecasts can be meant for many different constituencies
◦ Competitors
◦ Investors
◦ Other Stakeholders (suppliers, employees, etc.)
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No Accountability, poor accuracy
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Can be useful as a basis for a high end of range boundary (companies will
almost never exceed, but will often fail to achieve their forecasts)
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Modeling: Linear Extrapolation
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Newton’s First Law of Motion – “An object that is in motion will not change its velocity
unless an external force acts upon it”
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Analysts often do this
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It’s easy
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It’s “intellectually dishonest”
Continues past growth into the future, blindly
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Ignores “the fade”
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Some projects succeed, some fail
Sometimes, in the absence of other data, this can (but not usually) be the “best” approach;
however, this can easily lead to an overestimation of future revenues.
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Modeling: Past Performance
A close cousin to “linear extrapolation”
Uses history as the sole guide
Can be useful for understanding the
revenue cycles of deeply cyclical
industries, but even those usually have
some secular growth rate (higher highs,
higher lows)
 If this is used, 15+ years of history should
be used, and the margin impact (gross
margins) should also be studied (Who
covers INTC?)
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Modeling: Unit Level
Different business units (“segments”) have
differing growth rates
 Understanding the impact of the growth
rates of the various segments can provide
a huge advantage in determining the
future direction of the total revenue
growth rate
 Companies usually provide a lot of
disclosure around segments (and
geographies)
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Modeling: Product Level
Revenue = Price * Volume (mostly true,
there are reserves)
 Register data, company reported data,
Nielsen data, government data – all
sources of units sold
 Sell in ≠Sell through
 Best opportunity to reach an “out of
consensus” perspective on a stock
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Modeling: QoQ Growth Rates
QoQ is only useful if you are modeling
time periods less than a year (Quarters,
Halves)
 Linearity refers to how the company
collects revenue within a given period
(front or rear end loaded)
 QoQ is very misleading for “seasonal”
companies, such as retailers
 QoQ is very appropriate for highly
predictable, recurring style models
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Modeling: Cyclicality
Cyclical business experience dramatic changes in
price and demand, with huge margin impacts
 A long history of revenue (20+ years) is useful,
along with an understanding of where you might
be in the cycle (you must be WELL AHEAD of
the cycle to make money)
 You typically want to buy these when they look
the worst
 “This time” is NOT different 99% of the time
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Modeling: D/R, Waterfalls, etc.
Certain business have future revenue on
or off balance sheet which can increase
the accuracy of any forecasts (keep in
mind the delta to expectations drives the
stock price)
 This works best for quarterly forecasting
 Focus on key drivers to predict business
success
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Modeling: Acquisitions
Businesses can give a lot of information
about acquisitions; you can usually get
enough to impute the “organic” growth
rate
 PEP recent 10-K, page 53 has a section
called “Organic Revenue Growth” which
provides a nice table showing what
aspects of their growth are more
repeatable than others.
 Most smaller companies make you do the
math or hide acquisitions
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Modeling: Acquisitions (PEP)
Will Exchange Rates be the same in the
future or move as much as they did in
2012? ForEx helped in 2011.
 How integral to PEP’s strategy are
ongoing acquisitions?
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Modeling: Retailers
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Certain business/industry models are unique
enough to require another method of
analysis (retailers, banks, smaller E&P
companies)
WAG (2Q13 Results Press Release) gives us
the following data: Front-end comparable
sales, traffic, basket size, total sales.
“Pure” Retailers often disclose SSS, comps,
new stores, square footage, etc. which will
allow a fairly robust model
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What’s in a Revenue Model?
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Very simple model of AMG; includes 3
revenue segments
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What’s in a Revenue Model?
AMG’s model is pretty basic – AUM * fee
for all 3 segments, plus small adjustments
for performance fees, so you need to
forecast AUM (a function of market
performance, marketing, and product
performance) and the fee.
 The last slide had the AUM; here’s the fee
history
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Modeling: Tips
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Differentiate between Fact and Assumption
Reduce your key assumptions; simpler is
better and often more accurate
Forecasting is a flawed “science”; your goal is
more to understand what can happen, how it
can happen, and “What the market is
missing”; you will not forecast an EPS
number in the future
Track your performance to understand your
forecasting errors
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Price * Unit Model: HANS
Hansen’s (HANS) is now Monster
Beverage (MNST) – one of the biggest
misses of my career
 Relatively unique in that they disclose
gross revenues and detailed product level
information
 Typically a company discloses less and less
specific information as they get bigger or
face slowing growth; they also change
definitions, which impacts comparability
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Price * Unit Model: HANS
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I chose to model HANS based on Case
Units and Gross Price per case
◦ Instead of picking numbers, I used growth
rates in units and $ (my estimates shown in
green, blue italic represents forecasts)
◦ Forecasts supported by other data (see Excel
model)
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Organic Growth: MFE (INTC)
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A lot of investors owned MFE because mgmt claimed the organic growth
was fairly high; I didn’t agree at high prices because I thought the organic
(non-acquisition) growth was much lower. In fact, my best guess is it was
0%
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Revenue Model (R166- 357)
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R193-243 focuses on acquisition analysis; it shows revenue contribution
from various acquired companies or combinations of acquired companies
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For example, MFE bought SCUR+Reconnex+Solidcore (R208-209); look at
3Q09, if they had those companies in 2Q09, revenue growth would have
been 3.6% yoy, instead of the reported +18% (R325)
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Mix Shift: ZOLL
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Exciting new product is growing much faster than corporate average, becoming a bigger and bigger part of revenue each
quarter, and moving the corporate total growth rate higher (notice how fast LifeVest grew each Q)
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Zoll reported 1Q09 on 1/22/09; stock was $16.54; by 7/28/11 (3Q11 report) stock was at $69.66; if you caught this mix
shift, you made a lot of money; stock was finally acquired by Asahi Kasei on 4/23/12 for $93 as LifeVest continued to
grow rapidly
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Retail Model: CAKE
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Waterfall Model: RNOW
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Excel Model
R8-23; 2Q11 beat and guidance
R206-222; key metrics
R280+ Revenue model
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Segment & geography (R281-318)
OBS backlog tracking (R320-328)
Waterfall (R347-413)
Deal Metrics, Beat History, D/R
Start of tracking forex impact (R494-520)
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Summary
What’s priced in? (expectations)
Modeling helps you understand what
makes the business work
 Different Techniques for Different
Business Models
 Keep things simple, estimate the fewest
variables in your forecast
 Fit your projections to the data, not the
other way around
 Focus on what’s material
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