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

By

Gerard Hoberg

University of Marylandand

Gordon Phillips

University of Maryland and NBER

Presented at:

VGSF, February 2010

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 - 2Endogenous 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

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R1R2

R3

R4

R5

R6

R9

R7

R8

R10

T

R1

R2

R3

R4

R5

R6

R9

R7

R8

R10

T

Very Close

Competition?

Incentives to change

competition?

R10 in same industry?

Somewhat Close

More Synergies?

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), Seim (2006). 5

Real Data: Merger of Symantec (anti-virus) and Veritas (internet security)

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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

General Dynamics (372) – Antheon (737)

Real Data: Merger of Disney and Pixar

8Conclude: SIC codes miss the point, example of similar but different.

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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i

j

j

i

q

p

p

q

j

k

k

j

q

p

p

q

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 SimilarityDoc 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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ii

ii

PP

PV

.

|||| ||||

.)(

ji

ji

PP

PPCos

Geometric interpretation

Suppose θ is the angle between a and b as shown in the image below with 0<= θ <=:

Then: If orthogonal, Cos(θ) = 0, and firms are unrelated.

|||| |||)(. baba Cos

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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.

Similarity Distrib.

Range (0,100)

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:

Similarity Concentration Index:

Total Summed Similarity:

3. Average Similarity index:

4. Sales 10K based Herfindahl:

5. Sales 10K based C4

6. High Potential Entry Indicator

7. Firm level: Similarity with respect to “10 nearest” neighbors.

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jtj,i ))selection rivalobsc

1

(Pr1(

)1(1

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jijjj,t ))Sales/(Sales*(Saleslarityrival simi

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T5: Reality Check: Document Similarity“The Profitability of Differentiated Products”

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Conclude: Most basic I/O theoretical prediction: product differentiation is profitable. Huge significance, equal in importance to value/growth variables.

Future Product Differentiation andAdvertising/R&D

Dependent variable: change in differentiation

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Conclude: Firms invest and advertise to generate ex-post product differentiation and hence ex-post profitability.

T2: New Industry Classifications

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Industry ClassificationsAdjusted RSQ of variable on industry “dummies”

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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.

Dependent Variable SIC3 NAICS410-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%

T3: New Industry Classifications

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Regress Firm characteristic on Industry Dummies/Averages

T7: 10K Based Competition and Profitability

28Conclude: New Industry Definitions work well in explaining profitability.

T8: Reality Check: Normal SIC codes

29Conclude: SIC codes and NAICs codes don’t perform very well.

T9: Sutton: Endogenous Competition

30Conclude: Our new competition measures pick up incentives to

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

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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i

j

j

i

q

p

p

q

j

k

k

j

q

p

p

q

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 Competition1. 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 products.

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Nested Logitwith 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.37

Table 10: Long-term Real Outcomes

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Conclude: acquirers in competitive product markets experience higher profitability and sales growth when similar and gain in differentiation. Results stronger as horizon is lengthened.

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 (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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