Product Market Synergies and Competition in Mergers and Acquisitions: A Text Based Analysis

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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
• Presented at
• VGSF, February 2010

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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.

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Motivation - 2

• Endogenous Barriers to Entry
• (Shaked and Sutton (1987), Sutton (1991), Siem
  (2006), Nevo (2000, 2006))
• Firms advertise/conduct RD/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
Very Close Competition? Incentives to change
competition? R10 in same industry?
Somewhat Close More Synergies?
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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
  (5,0005,000/2) 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),
  Seim (2006).

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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
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General Dynamics (372) Antheon (737)
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Real Data Merger of Disney and Pixar
Conclude SIC codes miss the point, example of
similar but different.
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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???

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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.

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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
• and Low Cross price elasticity of demand with
  Rivals
• 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)lt0, 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
  (1word used, 0not used). Normalize to unit
  length. Dot products gt 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)
• 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
• Document similarity is bounded between (0,1)

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Geometric interpretation

• Suppose ? is the angle between a and b as
  shown in the image below with 0lt ? ltp
• Then
• If orthogonal, Cos(?) 0, and firms are
  unrelated.

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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 TA 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
• Similarity Concentration Index
• Total Summed Similarity
• Average Similarity index
• Sales 10K based Herfindahl
• 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 SimilarityThe
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 andAdvertising/RD
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 ClassificationsAdjusted RSQ of variable
on industry dummies
Dependent Variable SIC3 NAICS4 10-K based (constrain) 10-K based (generalize)
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 dont perform
very well.
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T9 Sutton Endogenous Competition
Conclude Our new competition measures pick up
incentives to differentiate yourself endogenous
competition.
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ConclusionsNew 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
• and Low Cross price elasticity of demand with
  Rivals
• 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

• Asset Complementarity (Own similarity) Pairwise
  similarity b/t target and acquirer using text
  similarity.
• Similarity between T and Ts closest rivals
  (ranked in terms of text similarity).
• Intensity of Target product market competition.
• Similarity between A and As closest rivals.
• Intensity of Acquirer product market competition.
• Similarity between T and As closest rivals.
• Comparing to above, permits computation of how
  much the acquirers product market competition.
• Number or of words in prod description having
  word root patent or Trademark
• A more direct measure of unique assets /
  potential for new products.

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Nested Logitwith spreading sorts all 5000 firms
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T8 Nested Logit

• Conclude Product similarity is most important
  determinant of pairings. In competitive
  industries, also dissimilarity to rivals

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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
   acquirers 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 SynergiesGrowth 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 (ReturnsProfitabili
ty)

• 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 As
  nearest rivals
• (3) that have unique, hard to replicate assets
  (patents) that make potential new products.
• Similar but Different.