Product Market Synergies and Competition in
Mergers and Acquisitions: A Text Based Analysis
By
Gerard Hoberg
University of Maryland
and
Gordon Phillips
University of Maryland and NBER
Motivation - 1
Economies of Scope and the Boundaries of the
firm (Panzar and Willig – 1981)




Which firms can combine successfully?
Firms with close potential rivals, price more competitively.
What areas are related to each other in product market space?
Why do profits increase for some mergers?
Increased cost efficiency? economies of scale?
Market power? Or are asset complementarities
important especially for new product introduction?
Competition can affect merger success and
motivation, profitability, and successful product
introduction.
 We develop new industry groupings & new measures of
industry competition. Old measures based on fixed
industry classifications do not have much explanatory
power. “Network” groupings.
2
Motivation - 2
Endogenous Barriers to Entry:
(Shaked and Sutton (1987), Sutton (1991), Siem (2006),
Nevo (2000, 2006))
 Firms advertise/conduct R&D/introduce new products in
order to create future barriers to entry through product
differentiation
Industry Classifications are used everywhere.
 Asset pricing/ corporate finance benchmarks.
 Existing classifications in many cases do not “perform”
that well. Existing SIC classifications have “Zero-One”
fixed measures of groupings that rarely change.
 What we need is a new measure of “relatedness” that
captures both within and across industry classifications.
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Motivation: Who to merge with? Relatedness
and Competition: How Close and to Whom
R2
R9
R9
R2
R1
R6
T
R7
R5
R8
R6
R7
R3
R4
R1
T
R5
Very Close
R8
R10
Competition?
Somewhat Close
Incentives to change
competition?
More Synergies?
R10 in same industry?
R3
R4
R10
4
Our contributions: Part of a 2 paper series
 Paper 1: Develop new measures of firm relatedness and
industry competitiveness. Jointly test importance of
competition and endogenous product differentiation.
 Paper 2: Examine merger likelihood and outcomes. Test the
importance of merger synergies and new product introduction.
 New automated methodology to read 47,609+ firm 10-Ks, and
extract product descriptions.
Web crawling based in PERL, SEC Edgar website.
APL based text parsing similarity matrix algorithms
extract and process product descriptions for each 10-K.
 Compute degree of similarity of every firm pair – both within and
across industries: (5,000*5,000/2) X 9 years.
 Build measures of asset complementarities and
relatedness/similarity to other firms. Test theories of the
endogeneous product market competition/ product differentiation
(Shaked and Sutton (1987), Sutton (1991), Nevo (2000, 2001),
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Seim (2006).
Real Data: Merger of Symantec (anti-virus) and Veritas (internet security)
Conclude: Example of similar but different. Merger permits new products
(different enough), but similar enough to permit integration. Very different
WITHIN the same industry. Variable Industry groupings do not impose
transitivity across firms – similar to Networks
6
Real Data: Merger of Disney and Pixar
Conclude: SIC codes miss the point, example of similar but different.
7
Related literature - 1
Why are we interested in relatedness? For example in the
context of mergers:
 (1.) Market power (Eckbo, Baker and Breshnahan(1985),
Nevo (2000 RJE, Econometrica) (2.) Vertical Mergers (Fan
and Goyal (2006), (3.) Economies of scale, Cost cutting.
Or (4.) Synergies from Asset Complementarities (Berry
and Waldfogel (2001, QJE), Rhodes-Kropf and Robinson
(2008)).
 Relatedness: Merger literature empirically use SIC codes
with 0-1 measures.
[Kaplan and Weisbach (1992), Healy, Palepu and Ruback (1992),
Andrade, Mitchell and Stafford (2001), Maksimovic, Phillips, and
Prabhala (2008).]
Open question: How related are firms within industries and across
industries???
8
Related literature - 2
 Endogeneous product market competition (Shaked and
Sutton (1987), Sutton (1991)), economies of scale Panzar
and Willig (1981).
 Changes in competition and merger pair similarity
should be examined jointly. Feasible with continuous
similarity measure.
9
Hypotheses about Merger Likelihood
Key Industrial Organization Prescription: Prediction of Baker and Breshahan (1985),
Nevo (2005) and others:
Optimal merger partner for firm i is firm j (with rival k) when:
High Own Cross Price Elasticity of Demand
qi p j
p j qi
and Low Cross price elasticity of demand with Rivals:
q j pk
pk q j
 H1: Asset Complementarity: Firms are more likely to merge
with other firms whose assets have high complementarity
with their assets.
 H2: Competition and Differentiation from Rivals: Acquirers in
competitive product markets should be more likely to choose
targets that help them to increase product differentiation
relative to their nearest ex-ante rivals.
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Hypotheses about Ex Post Outcomes
Profitability of new products:
Think of profit function for new products: prob(success) *(pn –
cn)*qn
 H3: Differentiation from rivals: Acquirers outcomes better
with targets that differentiate products from rivals, higher
price cost margin, (pn – cn).
 H4: Synergy/Asset Complementarity: Outcomes better when
T closer to A: (1.) higher prob(n) above, and (2.) more cost
synergies from managerial skill: [(Csa – Cst)<0], where Csi for
acquirer, target.
 H5: H3, H4 stronger when – Unique products (patents) protect
target technology and give potential for new product introduction.
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Hypotheses about Industry Competition
Key Industrial Organization Predictions:
H1: More concentration, more profitability
(Lack of strong link in many previous studies).
H2: Limit pricing: Firms with “close” potential rivals
price more competitively and thus have lower profits.
H3: Endogenous Barriers to Entry: Firms actively
engage in mechanisms to increase their product
differentiation and reduce future product market
competition.
 Need accurate measures of “closeness” and
product market differentiation
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Sample: 10-K population of firms
 All 10-Ks on SEC Edgar that have a valid link to COMPUSTAT tax
number. Hand correct when tax numbers change.
 Must have a valid CRSP permno.
 Prior to matching with COMPUSTAT/CRSP, 49,000+ 10-Ks.
 After cleaning, 47,607 10-Ks from 1997 to 2005 (almost 5,000 /year).
 We use 10-Ks from 1996 only to compute starting values of lagged
variables.
 Overall, we get 95% of the eligible COMPUSTAT/CRSP sample.
 Firms are excluded if they do not have a valid tax ID link.
 Coverage from 1997 to 2005 nearly uniform at 95%.
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Document Similarity
 Take all words used in universe of 10-Ks in product
description each year (87,385 in 1997). Exclude words
(3027 of them in 1997) appearing in more than 5% of all
10-Ks.
 Form boolean vectors for each firm in each year (1=word
used, 0=not used). Normalize to unit length. Dot
products => pairwise product similarity.
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Document Similarity
Doc 1: “They sell cabinet products.”
Doc 2: “They operate in the cabinet industry.”
 Step 1) Drop words "they", "the", "and", "in" (common words).
 Step 2) 5 elements: "sell" "operates", "cabinet", "products", "industry"
P1 = (1,0,1,1,0)
P2 = (0,1,1,0,1)
Vi 
Pi
Pi .Pi
 Step 3) Normalize vector to have unit length of 1:

V1 = (.577,0,.577,.577,0)
V2 = (0,.577,.577,0,.577)
 Step 4) Compute document similarity V1 • V2 = .33333
 This dot product has a natural geometric interpretation:
Cos( ) 
 Document similarity is bounded between (0,1)
Pi .Pj
|| Pi || || Pj ||
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Geometric interpretation

Suppose θ is the angle between a and b as shown in the
image below with 0<= θ <=p:
 Then: a.b  Cos( ) || a | || b ||
 If orthogonal, Cos(θ) = 0, and firms are unrelated.
Similarity
Distrib.
Range
(0,100)
Conclude: Mergers are (1) far more similar than random firms, (2)
heterogeneous in degree of similarity, and (3) still very highly
similar even when in different SIC-2.
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Why not just use SIC codes?
Mergers in 2005 in different SIC-2
Conclude: SIC codes are informative but do not fully
describe similarity nor product market competition.
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Examples: T+A shared words
Conclude: common words indeed related to product offerings.
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Text Product Based
Industry Measures of Competition
First fix industry groups. Industry groups defined by maximizing
within group similarity. From groups compute:
N
 Similarity Concentration Index: sci   (1  Pr ob(rival selection j,t ))
j 1
N
  (1  rival similarity j,t*(Sales j /(Sales j  Salesi )))
j 1
 Total Summed Similarity:
N
ssi   (rival similarity j,t )
j 1
N
3.
4.
5.
6.
7.
asi   (rival similarity j,t ) / N
Average Similarity index:
j 1
N
Sales 10K based Herfindahl: HHI 10K   (marketsharej,t ) 2
j 1
Sales 10K based C4
High Potential Entry Indicator
Firm level: Similarity with respect to “10 nearest” neighbors.
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T5: Reality Check: Document Similarity
“The Profitability of Differentiated Products”
Conclude: Most basic I/O theoretical prediction: product
differentiation is profitable. Huge significance, equal in
importance to value/growth variables.
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Future Product Differentiation and
Advertising/R&D
Dependent variable: change in differentiation
Conclude: Firms invest and advertise to generate ex-post product
differentiation and hence ex-post profitability.
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T2: New Industry Classifications
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Industry Classifications
Adjusted RSQ of variable on industry “dummies”
NAICS4
10-K based
(constrain)
10-K based
(generalize)
Dependent Variable
SIC3
Operating Inc/Sales
28.3%
28.5%
33.1%
38.9%
Advertising/Sales
4.5%
6.6%
7.3%
9.4%
Market Beta
29.2%
30.2%
36.5%
45.5%
Conclude: Industry definitions constructed from 10Ks are better
and more flexible than SIC/NAICS (see companion paper).
For merger paper: We use 10-K based measures b/c they better
explain competitiveness and offer flexibility. Flexibility in firm
location measurement is pivotal in examining mergers.
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T3: New Industry Classifications
Regress Firm characteristic on Industry Dummies/Averages
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T7: 10K Based
Competition and Profitability
Conclude: New Industry Definitions work well in explaining profitability.
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T8: Reality Check: Normal SIC codes
Conclude: SIC codes and NAICs codes don’t perform very well.
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T9: Sutton: Endogenous Competition
Conclude: Our new competition measures pick up incentives to
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differentiate yourself – endogenous competition.
Conclusions:
New Product Based Industries
Text-based analysis of product descriptions
produces improved measures of:
(1) Industry competition
(2) Relatedness between firms both within and
across industries.
(3) These new measures allow tests of theories
of economies of scope and endogenous barriers
to entry, and tests of merger pair relatedness
Competition and product differentiation.
We can use these new industries to examine
many finance related questions as well.
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Hypotheses about Merger Likelihood
Key Industrial Organization Prescription: Prediction of Baker and Breshahan (1985),
Nevo (2005) and others:
Optimal merger partner for firm i is firm j (with rival k) when:
High Own Cross Price Elasticity of Demand
qi p j
p j qi
and Low Cross price elasticity of demand with Rivals:
q j pk
pk q j
 H1: Asset Complementarity: Firms are more likely to merge
with other firms whose assets have high complementarity
with their assets.
 H2: Competition and Differentiation from Rivals: Acquirers in
competitive product markets should be more likely to choose
targets that help them to increase product differentiation.
 H2b: Firms with complementary assets are more likely to
introduce new products post merger to increase diff.
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Database of Restructuring
Transactions
 SDC Platinum. We consider mergers and acquisition
of assets transactions.
 Target and acquirer must also both have a valid link
to the machine readable firms database.
 Final sample of 5,643 restructuring transactions from
1995 to 2005.
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Text Measures of
Complementarities and Competition
1. Asset Complementarity (Own similarity): Pairwise
similarity b/t target and acquirer using text similarity.
2. Similarity between T and T’s closest rivals (ranked in
terms of text similarity).
 Intensity of Target product market competition.
3. Similarity between A and A’s closest rivals.
 Intensity of Acquirer product market competition.
4. Similarity between T and A’s closest rivals.
 Comparing to above, permits computation of how much the
acquirer’s product market competition.
5. Number or % of words in prod description having word
root “patent” or “Trademark”
 A more direct measure of unique assets / potential for new
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products.
Nested Logit
with spreading sorts – all 5000 firms
T8: Nested Logit
Conclude: Product similarity is most important determinant of pairings.
In competitive industries, also dissimilarity to rivals
T9: Announcement Returns
(1) Combined firm returns larger when acquirer in comp. product
market and when target is more unique.
(2) Especially large when target is dissimilar to acquirer’s near rivals
and when pairwise similarity is larger.
(3) Results also larger when patent-proxy for unique assets is higher.
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Table 10: Long-term Real Outcomes
Conclude: acquirers in competitive product markets experience higher
profitability and sales growth when similar and gain in differentiation.
Results stronger as horizon is lengthened.
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Table 11: Synergies
Growth in Product Descriptions
Conclude: Acquirer product market competitiveness very
related to product desc. growth. Support for post-merger
real gains being related to synergies and unique assets.
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Table 12: Economic Magnitude
(Returns+Profitability)
Conclude: Economic impact on announcement returns modest,
stronger on fundamentals, especially sales growth and growth in
product descriptions.
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Merger paper conclusions
“Synergies and competition matter”
Merger pair similarity – while high - is quite heterogeneous
** Best mergers with higher ex post cash flows and new
product introductions are ones
(1) with similar acquirer and target
(2) with targets that are further away from A’s nearest
rivals
(3) that have unique, hard to replicate assets (patents)
that make potential new products.
“Similar but Different”.
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