Download Choice of comparable firms for multiple valuation

Choice of comparable firms for multiple valuation
A presentation by Prof. Thomas Plenborg, Jens Overgaard
Knudsen and Simon Vesterby Kold
1
Agenda
1
Comparable firm selection for multiple valuation
2
Our idea
3
How did we test it and how does it work?
4
Questions
2
Who are we and what is the background of our
project?
Thomas Plenborg
 Professor at
Department of
Accounting and
Auditing, CBS
Jens Overgaard Knudsen
 Global Finance
Graduate at Novo
Nordisk A/S
 Cand.merc.fir from
CBS in 2015
Simon Kold
 Financial Analyst at
Novo A/S
 Cand.merc.fir from
CBS in 2015
Desired outcome:
₊
Help analysts and
investors in
improving
valuation accuracy
3
Derivation of multiples from the present value
relation
EVt =
For simplicity, consider a firm which has a
constant growth rate and discount factor
forever.
P/E
ROE − g D
1
×
re − g D
ROE
P/B
ROE − g D
re − g D
EV/EBIT
EV/EBITDA
EV/SALES
ROIC − g FCFF
rWACC − g FCFF
ROIC − g FCFF
rWACC − g FCFF
ROIC − g FCFF
rWACC − g FCFF
FCFF
rWACC − g FCFF
MVE =
D
re − g D
Ideal peer-group companies
for multiple valuation
purposes should be truly
identical on the basis of
these properties
×
1
× (1 − TR )
ROIC
×
1
× (1 − TR ) × (1 − R% DEPR )
ROIC
×
1
× (1 − TR ) × (1 − R% DEPR ) × EBITDAm arg in
ROIC
- But how can we identify
such firms?
4
Drawbacks
Rationale for the
approach
Two obvious approaches for comparable firm
selection: Industry affiliation and fundamentals
Industry
Affiliation
Fundamentals
 Companies within the same industry
are subject to similar market
characteristics
 Current fundamentals can be used
to approximate future profitability,
growth and risk
 Companies within the same industry
tend to produce similar products,
leading to similarities of their supply
and demand curves
 Independent of subjectively defined
industry classes
 Easy to use and widely applied in
practice
 Used for comparable firm selection
in a range of empirical studies ,
including combinations with industry
 Theoretical difficulties in defining “an
industry”
 Industry classes are subjectively
defined and subjectively chosen
 Companies within the same industry
class can be very different from
each other
 Can incorporate, in principle, an
infinite amount of peers
 Which fundamentals are best at
projecting future levels of
profitability, growth and risk?
 Gathering fundamental data for a lot
of companies can be a cumbersome
process
 Most fundamental approaches are
only capable of using 2-3 proxies
5
Previous attempts at the fundamental approach

We know that the levels of multiples are determined by profitability,
growth and risk

Previous studies show that identifying comparable firms based on
fundamentals may be a useful alternative to industry classification

However, in these studies, peers are selected in the intersection of
the most similar firms in terms of proxies for profitability, growth and
risk

This creates an issue
Consequence
Illustration of the issue
All firms in the sample

Adding a third or fourth variable
would further reduce the number
of peers

Of these firms, the 14%
closest firms in terms of size
In effect, this restrains the
approach to only being able to
use two proxies for
profitability, growth and risk
The 14% closest firms
in terms of ROE
The intersection of
the two (leaving only
2% of the sample)
6
Agenda
1
Comparable firm selection for multiple valuation
2
Our idea
3
How did we test it and how does it work?
4
Questions
7
The Idea:
-Sum of Rank Differences (SARD)
SARD
proposition
Our method selects comparable firms based on the least sum of
absolute rank differences across a range of variables which are
expected to affect multiples
We propose a method where peers are selected on the basis of fundamentals
and which allow for more than just one or two fundamental value drivers
Characteris
tics of the
SARD
model
If a potential peer has a low SARD value, the approach suggests that the potential
peer and the target firm share similarities with respect to the chosen variables.
If these variables are useful proxies of the drivers of the multiple, then the
identified peers and the target firm should also be traded at similar prices.
𝑆𝑆𝑆𝑆𝑆𝑆𝐷𝐷𝑖𝑖,𝑗𝑗 = |𝑟𝑟𝑋𝑋,𝑖𝑖 − 𝑟𝑟𝑋𝑋,𝑗𝑗 | + |𝑟𝑟𝑌𝑌,𝑖𝑖 − 𝑟𝑟𝑌𝑌,𝑗𝑗 | + ⋯ + |𝑟𝑟𝑍𝑍,𝑖𝑖 − 𝑟𝑟𝑍𝑍,𝑗𝑗 |
where SARD is the sum of absolute rank difference between firm i and firm j, rx,i is the rank
of firm i in terms of variable x, rx,j is the rank of firm j in terms of variable x … and so forth.
8
A 5-step implementation guide to the SARD approach
1
Select base sample
2
Estimate relevant value drivers
3
Convert into ranks
4
Calculate rank difference (“Penalty
points”)
5
Find the firms with the lowest SARD
9
A 5-step implementation guide to the SARD
approach
1
•
Construct a sample from which to select peergroup from. Could be:
Select base sample
- A large sample (like our empirical study where
we use S&P 1500)
- A qualitative selected sample (e.g. subjective
perception of industry affiliation)
2
Estimate relevant
value drivers
•
Select and calculate relevant value drivers. Our
tests include:
- Return on equity (profitability)
- Net debt/EBIT (risk)
- Size (risk and different levels of multiples)
- Forecasted EPS growth t+2 (growth)
- EBIT-margin (relevant for EV/sales
multiples)
•
Practitioner implementation: Consider manuel
adjustments, NTM ratios, industry-specific
ratios etc ?
10
A 5-step implementation guide
to the SARD approach
•
•
•
3
Convert into ranks
Transform all value drivers into ranks
Enables you to combine value drivers regardless of scale
Possibility of assigning weights to value drivers
Abbott Labs
Coca-Cola
Estee Lauder
10%
22%
34%
Size
(bn. $)
67
185
31
General Mills
24%
35
23%
280
20%
7%
31%
24
69
146
17%
204
30%
66
ROE
Johnson &
Johnson
Kellogg Co
Mondelez
Pepsico
Procter &
Gamble
Reynolds
America
rROE
rsize
9
6
1
6
3
9
4
8
5
7
10
2
1
10
5
4
8
2
3
7
Implementation tip
11
A 5-step implementation guide
to the SARD approach
•
•
•
Calculate the
difference in ranks
between each firm
in the base sample
and the firm
subject to the
valuation
Repeat for each
value driver
The rank
differences in the
table is based on
ROE
Abbott Labs
Coca-Cola
Estee Lauder
General Mills
Johnson & Johnson
Kellogg Co
Mondelez
Pepsico
Procter & Gamble
Reynolds America
4
Calculate rank
difference
(“Penalty points”)
Abbott
Coca-Cola
Labs
Estee Lauder
n/a
|9-6|+|6-3|=6
|9-1|+|6-9|=11
n/a
|6-1|+|3-9|=11
n/a
General
Mills
A 5-step implementation guide
to the SARD approach
5
Find the firms with
the lowest SARD
•
Calculate the sum of absolute rank differences across the value drivers
•
Identify the firms with the least sum of absolute rank differences
•
Decide on a cut-off (i.e. number of firms to include as peers). In the example
below we use six as cut-off (i.e. select six peers)
4
Coca-Cola
(6)
Abbott
Labs
(6)
Mondelez
(6)
5
6
General
Mills
(7)
Reynolds
America
(7)
Reynolds
America
(7)
General
Mills (5)
Reynolds
America
(4)
Estee
Lauder
(4)
Procter &
Gamble
(4)
Abbott
Labs (6)
Pepsico
(6)
Kellogg
Co (5)
Pepsico
(6)
Kellogg
Co (7)
Pepsico
(6)
Abbott
Labs
(11)
CocaCola (11)
Reynolds
America
Pepsico
(5)
Coca-Cola (3)
Procter &
Gamble
Kellogg
Co (6)
Reynolds
America
(2)
Pepsico
3
General
Mills
(4)
Mondelez
Johnson &
Johnson
(3)
Kellogg Co
2
Procter &
Gamble
(5)
1
Johnson &
Johnson
Procter &
Gamble
(3)
General
Mills
Mondelez
(2)
Estee
Lauder
Coca-Cola
Abbott
Labs
Pe
er
Abbott Labs
(2)
Reynolds
America (4)
CocaCola (3)
General Mills
(2)
Procter &
Gamble
(5)
Coca-Cola (5)
Johnson
&
Johnson
(4)
Pepsico
(4)
Reynolds
America
(7)
Coca-Cola (6)
General Mills
(6)
Mondelez
(5)
Estee Lauder
(4)
General Mills
(8)
Estee
Lauder (7)
Kellogg Co
(8)
Estee Lauder
(6)
Abbott
Labs
(5)
Coca-Cola (7)
Abbott
Labs (7)
Reynolds
America (8)
Mondelez
(8)
Reynolds
America (9)
Johnson &
Johnson (6)
Pepsico
(8)
Abbott Labs (7)
CocaCola
(7)
Abbott Labs
(9)
CocaCola
(8)
Pepsico
(9)
Procter &
Gamble
(8)
Kellogg
Co (9)
Kellogg Co (7)
Agenda
1
Comparable firm selection for multiple valuation
2
Our idea
3
How did we test it and how does it work?
4
Questions
14
Random example from S&P 1500:
Six peers selected for IFF on the basis of SARD
Firm being valued
International Flavors & Fragrances
(IFF)
Industry
EV/EBIT
ROE
Debt/EBIT
Size
Materials
12.9
23%
2.7
3,356
Peers
Industry
EV/EBIT
ROE
Debt/EBIT
Size
KB Home
Consumer Durables & Apparel
7.3
23%
2.3
3,175
Beckman Coulter Inc
Health Care Equip. & Services
13.2
23%
2.0
3,354
Pioneer Natural Resources Co
Energy
12.6
22%
3.6
3,878
McCormick & Co Inc
Food, Beverage & Tobacco
16.9
24%
2.0
4,593
TCF Financial Corp
Banks
12.4
23%
4.5
3,601
Knight-Ridder Inc.
Media
12.8
20%
2.6
5,777
Actual EV/EBIT of IIF
Prediction of the EV/EBIT
multiple (harmonic mean
of peers)
12.9
11.8
Valuation error │(11.8-12.9)/12.9│
8%
15
A distinct pattern: Combining fundamentals seems
to work
Absolute percentage errors and ranks (brackets) of valuations based on each selection method
EV/EBIT
EV/Sales
P/B
P/E
ROE
Debt/EBIT
Size
Growth
ROE
Debt/EBIT
Size
Growth
EBITmargin
Industry
ROE
ROE
Debt/EBIT
ROE
Debt/EBIT
Size
Median
0.255 (4)
0.292 (6)
0.260 (5)
0.250 (3)
0.228 (2)
0.222 (1)
Mean
0.341 (3)
0.390 (6)
0.351 (5)
0.343 (4)
0.309 (2)
0.307 (1)
IQ
0.330 (3)
0.364 (6)
0.335 (5)
0.330 (4)
0.297 (2)
0.291 (1)
Median
0.407 (2)
0.531 (6)
0.504 (5)
0.477 (4)
0.470 (3)
0.254 (1)
Mean
0.576 (2)
0.761 (6)
0.720 (5)
0.693 (4)
0.671 (3)
0.360 (1)
IQ
0.463 (2)
0.499 (6)
0.483 (5)
0.480 (3)
0.481 (4)
0.332 (1)
Median
0.349 (6)
0.298 (5)
0.283 (4)
0.275 (3)
0.241 (2)
0.240 (1)
Mean
0.444 (6)
0.377 (5)
0.360 (4)
0.348 (3)
0.316 (2)
0.313 (1)
IQ
0.416 (6)
0.352 (5)
0.347 (4)
0.335 (3)
0.312 (1)
0.313 (2)
Median
0.286 (5)
0.297 (6)
0.286 (4)
0.279 (3)
0.244 (2)
0.240 (1)
Mean
0.382 (6)
0.375 (5)
0.363 (4)
0.354 (3)
0.325 (2)
0.325 (1)
IQ
0.380 (6)
0.352 (5)
0.347 (4)
0.340 (3)
0.321 (1)
0.321 (2)
16
The pattern remains intra-industry
Absolute percentage errors and ranks (brackets) of valuations based on each selection method
EV/EBIT
EV/Sales
P/B
P/E
ROE
Debt/EBIT
Size
Growth
EBIT-%
(s.industry)
Industry
ROE
(same
industry)
ROE
Debt/EBIT
(s.industry)
ROE
Debt/EBIT
Size
(s.industry)
ROE
Debt/EBIT
Size
Growth
(s.industry)
Median
0.255 (6)
0.244 (5)
0.219 (4)
0.215 (3)
0.206 (2)
0.203 (1)
Mean
0.341 (6)
0.321 (5)
0.295 (4)
0.290 (3)
0.279 (2)
0.275 (1)
IQ
0.330 (6)
0.311 (5)
0.296 (4)
0.292 (3)
0.280 (2)
0.279 (1)
Median
0.407 (6)
0.380 (5)
0.364 (4)
0.344 (3)
0.338 (2)
0.271 (1)
Mean
0.576 (6)
0.537 (5)
0.514 (4)
0.492 (3)
0.483 (2)
0.369 (1)
IQ
0.463 (6)
0.452 (5)
0.439 (4)
0.426 (3)
0.423 (2)
0.357 (1)
Median
0.349 (6)
0.247 (4)
0.249 (5)
0.243 (3)
0.240 (1)
0.241 (2)
Mean
0.444 (6)
0.318 (4)
0.322 (5)
0.312 (3)
0.310 (2)
0.307 (1)
IQ
0.416 (6)
0.317 (4)
0.319 (5)
0.311 (3)
0.310 (2)
0.304 (1)
Median
0.286 (6)
0.247 (5)
0.244 (4)
0.241 (3)
0.228 (2)
0.225 (1)
Mean
0.382 (6)
0.322 (5)
0.322 (4)
0.320 (3)
0.307 (2)
0.306 (1)
IQ
0.380 (6)
0.319 (3)
0.327 (4)
0.331 (5)
0.316 (2)
0.311 (1)
17
Robustness checks
Robustness checks performed in this study
Number of firms in peer group
Across time
Across size
18
Number of firms in peer group
• The ranking of selection methods remain stable across various numbers of peers
• Incrementally increasing accuracy when peers are added
• Similar results for all evaluated multiples
EV/Sales
19
Across time
• The ranking of selection methods remain stable over the sample time period
• Increase in valuation errors around the dot-com-bubble and the financial crisis
• Similar results for all evaluated multiples
P/B
20
Across size
• The ranking of selection methods remain stable over the sample time period
• Seems to be less estimation error in S&P 500 compared to the other two indices
• Similar results for all evaluated multiples
ROE
Debt/EBIT
Size
ROE
Debt/EBIT
Size
Growth
ROE
Debt/EBIT
Size
Growth
EBIT-margin
Industry
ROE
ROE
Debt/EBIT
S&P 500
0.326 (6)
0.256 (4)
0.273 (5)
0.222 (3)
0.221 (2)
0.210 (1)
S&P 400
0.367 (6)
0.256 (5)
0.234 (4)
0.204 (2)
0.217 (3)
0.199 (1)
S&P 600
0.343 (6)
0.278 (5)
0.260 (4)
0.259 (3)
0.213 (2)
0.200 (1)
P/B:
21
Conclusions
Current environment
Our solution
The SARD offers a promising alternative in that:
Most analysts and investors tend to use
industry classification as proxy for perfect
substitutes
However, firms within the same industry do
not necessarily have the same profitability,
risk or growth characteristics and they
should therefore not be traded at the same
multiple.
The SARD approach is significantly more accurate than
the industry approach
The SARD approach is able to cater for, in principle, an
infinite number of proxies for profitability, growth and
risk
The SARD approach is less sensitive to sample size than
the industry approach
The SARD approach is able to tailor the selection
variables to fit the need of any desired multiple
Less accurate valuation estimates
More accurate valuation estimates>
22
Agenda
1
Comparable firm selection for multiple valuation
2
Our idea
3
How did we test it and how does it work?
4
Questions
23