Text-Based Product Characteristics, Competition and Dividends

& Dynamic Text-Based Industries and Endogenous

Product Differentiation

By

Gerard Hoberg,

Gordon Phillips

and

Nagpurnanand Prabhala

University of Maryland

Motivation - 1Dividends:

(Famous puzzles: Why pay if tax disadvantaged versus share repurchases? Lintner (1967) describe dividend behavior. Bhattacharya (1981): Costly Signaling. To whom?

Sustainability and stability of future earnings are the most important determinants of payout policy. (Figure 1 of Brav, Graham, Harvey, and Michaely (2005)).

Open questions: What causes firm’s future earnings to be stable? What are we signaling about the firm?

Stability: Product life cycle explanation as in Abernathy and Utterback's (1975) and Klepper (1996).

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Motivation - 2Importance of Industry Environment and Product

Characteristics: (Shaked and Sutton (1987), Sutton (1991), Siem (2006),

Nevo (2000, 2006)) Firms advertise/conduct R&D/introduce new products in

order to create endogenous barriers to entry and product differentiation

Relatedness and 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 - 3

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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Part of a 3 paper series Paper 1: “Dynamic Text-Based Industries and Endogenous

Product Differentiation” Develop new measures of firm relatedness and develop new industry classifications. Industry groupings can change over time. “Network” Variable Classifications. Test theories of the endogenous product market

competition/ product differentiation (Shaked and Sutton (1987), Sutton (1991), Nevo (2000, 2001), Seim (2006).

Paper 2: “Product Market Synergies and Competition in Mergers and Acquisitions: A Text Based Analysis” forthcoming RFS. We examine merger likelihood and outcomes. Test the

importance of asset complementarities to merger synergies and new product introduction.

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This Paper

Paper 3: “How do Product Characteristics and Competition Impact Dividends and Share Repurchases?. Examine propensity to pay dividends, repurchase shares,

change or initiate dividends based on fundament product characteristics and product market competition.

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Our contributionsPapers rely on the following central ideas and methods:

Economic Idea: Relatedness and characteristics of products are fundamental to industries and notion of competition (Hotelling, Lancaster)

Firms pay dividends when they believe they have a “stable” product. May signal their stability to potential business partners through dividends.

We compute degree of asset complementarities and similarity of every firm with each other -all pairs – both within and across industries: (5,000*5,000/2) X 9 years.

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. 7

Related literature - 1

Dividends and Share Repurchases: Very large literature Grullon-Michaely-Swaminathan-2002 and DeAngelo-DeAngelo-

Stulz-2006 show that firm maturity is associated with higher dividend payout.

Dividends vs. Repurchases: Fama-French-2001 and Grullon-Michaely-2002.

Text Based Analysis: large growing literature Li-2006b, Tetlock-2007, Tetlock-Tsechanksy-Macskassy-2008, and

Loughran-McDonald-2010 find word content to be informative in predicting stock price movements.

Hanley-Hoberg -2010 for IPOs, Rauh-Sufi-2010 for competitors.

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How to measure product fluidity and product environment?

Apple Computer

Apple Product description 1999 10KPower Macintosh, Powerbook, iMac, Power Macintosh, Powerbook, iMac,

iBookiBook

Apple product description 2009 10KMacbook, MacBook Pro, iMac, Macbook, MacBook Pro, iMac, iPhone, iPod, iPod Classic, iPodTouch, iPhone, iPod, iPod Classic, iPodTouch,

iTunes, Apple TViTunes, Apple TV

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Motivation: How to measure product environment?

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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?

General Dynamics (372) – Antheon (737)

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

Hypotheses about Dividends and Payout Policy

H1: Product Fluidity and Stability: Firms whose products change more over time - both relative to aggregate product changes or relative to their own past products - will pay lower dividends.

H2: Competition and Differentiation from Rivals: Firms which produce products that are less similar to competitors products and are in markets that are more protected from competitors should pay higher

dividends.

H3: Product Customer Type: Firms who sell more products to business customers should pay higher dividends and should favor dividends over share repurchases.

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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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ji similarityrivalss

1tj, ) (

NsimilarityrivalasN

ji /) (

1tj,

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jK emarketsharHHI

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2tj,10 )(

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: FIC: 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%

Variable Industry ClassificationsVIC Industries

Allow each firm to have its own industry peers. In SIC2, 4.45% of all possible pairs are industry pairs, A

5.14 similarity level also generates 4.45% of pairs in the same industry.

At the 3 digit level these numbers are 2.05% of all possible pairs are in the same industry, with a corresponding 7.06 similarity measure generating the same number of pairs.

This gives the following implied cutoffs based on similarities: VIC “SIC2” similarity score >= 5.24 VIC “SIC3” similarity score >= 7.06

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 Payout Policy

H1: Product Fluidity and Stability: Firms whose products change more over time - both relative to aggregate product changes or relative to their own past products - will pay lower dividends.

H2: Competition and Differentiation from Rivals: Firms which produce products that are less similar to competitors products and are in markets that are more protected from competitors should pay higher

dividends.

H3: Product Customer Type: Firms who sell more products to business customers should pay higher dividends and should favor dividends over share repurchases.

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Text Measures of Product Fluidity and Product ClienteleProduct Fluidity:

1. Overall Product Fluidity: cosine similarity between own word vector and aggregate fluidity vector, FLit

FLit =ABS((Ft-Ft-1)/(Ft+Ft+1)) ; Ft is fraction of firms

using the non-common word

2. Self Product Fluidity: 1- Cos(it,it-1) (similarity of 10K to last years 10K)

Product Clientele: Cosine similarity of own words to words in input-output matrix of industries that sell over 90% of their products to non-retail customers.

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Business Clientele Words PLASTICS RUBBER PULP PAPER PAPERBOARD TRANSPORTATION

SUPPORT AGRICULTURE CONSTRUCTION MINING MACHINERY ACCOUNTING BOOKKEEPING SERVICES ADMINISTRATIVE SUPPORT SERVICES MOTOR VEHICLE BODIES TRAILERS PARTS HVAC COMMERCIAL REFRIGERATION EQUIPMENT CHEMICAL PRODUCTS INDUSTRIAL MACHINERY NONMETALLIC MINERAL GENERAL PURPOSE MACHINERY AGRICULTURAL CHEMICALS YARN FABRICS TEXTILE MILL PAINTS COATINGS ADHESIVES MAGNETIC MEDIA PRINTED ANIMAL AGRICULTURE FORESTRY SUPPORT SERVICES PIPELINE TRANSPORTATION TURBINE POWER TRANSMISSION EQUIPMENT AEROSPACE PARTS FABRICATED METAL WOOD WAREHOUSING STORAGE MANAGEMENT TECHNICAL CONSULTING SERVICES FORGINGS STAMPINGS EMPLOYMENT SERVICES PRIMARY FERROUS METAL ELECTRICAL EQUIPMENT BOILERS TANKS SHIPPING CONTAINERS METALWORKING MACHINERY BASIC CHEMICALS ADVERTISING RELATED SERVICES SEMICONDUCTORS ELECTRONIC COMPONENTS COAL NONMETALLIC MINERALS MACHINERY EQUIPMENT RENTAL LEASING ARCHITECTURAL STRUCTURAL METAL PRIMARY NONFERROUS METAL FOUNDRY

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Product Fluidity

Firms with high product fluidity in 2008: MEDTRONIC, STILLWATER MINING, ALLERGAN,

EV3, CLINICAL DATA, AMGEN, CEPHALON, VIACOM, NEWS Corp, VICAL, CISCO SYSTEMS, ALNYLAM PHARMACEUTICALS, FORTUNE BRANDS, STRYKER, VARIAN MEDICAL SYSTEMS, TIME WARNER NEW, ENZO BIOCHEM, EMERGENT BIOSOLUTIONS, FOUNDATION COAL HOLDINGS, WASHINGTON POST, CUBIST PHARMACEUTICALS, HLTH, OBAGI MEDICAL PRODUCTS, MANNKIND, GTX

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Product Fluidity

Firms with low product fluidity (stable products) in 2008:

UNION PACIFIC, HUDSON HIGHLAND GROUP, AZZ, COMPUTER TASK GROUP, COMPUWARE, CONSULIER ENGINEERING, CASS INFORMATION SYSTEMS, FOOT LOCKER, CHICAGO RIVET & MACH, WEIS MARKETS, W S I INDUSTRIES, AMERICAN WOODMARK, OFFICE DEPOT, UNITED STATIONERS, ALANCO TECHNOLOGIES, MCGRAW HILL COS, CABELAS, HARBINGER GROUP, E D A C TECHNOLOGIES, MDI, PITNEY BOWES, AMPCO PITTSBURGH, TWIN DISC, NORTECH SYSTEMS, NAVIGANT CONSULTING

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Dividends and Repurchases Data

Summary statistics: Table 1 shows: 26% of the firms are dividend payers 43%, are repurchases. S eparation: Only 17% of firms repurchase shares

and pay dividends in the same year. Dividends are sticky downwards: While many firms

increase dividends (9.7%), less than 1% of firms decrease dividends in any year.

Low self product fluidity scores - Maximum of .9, median of .114 Wide variation in changes in products each year. Many firms change products each year given that no change in product description would be indicated by a score of 0.

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T4: Dividend Payer Likelihood

Conclude: Product characteristic differences have sharp bivariate differences across groups

Table V: Dividend Payer Likelihood

Regressions include controls for firm risk, firm age, M/B, Asset Growth, Profitability, Firm Size, and R&D.

Conclude: Product characteristics strongly impact dividends.39

Table VI: Dividend Payer & Competition

Conclude: Product Market Concentration impacts dividend likelihood40

Table VII: Repurchaser Likelihood

41Conclude: Product characteristics impact repurchase likelihood.

Opposite effect of Business Clientele Similarity.

Table VIII: Economic Magnitude

Panel A: Propensity to pay dividends, Panel B: RepurchaseConclude: Economic impact large for dividend propensity than

repurchase propensity.42

Figure 1: Economic Magnitude

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Table IX: Dividends vs. Repurchases

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Panel A: Propensity to pay dividends

Panel B: Propensity to Repurchase

Tables XI to XIII: Initiations/Omissions

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Dividend Initiation Policy

Conclude: Product characteristics impact initiations. Historically hard to explain in the data.

Tables XII Dividend Omissions

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Dividend paper conclusions

“Product Characteristics and competition matter”(1) Firms with fluid, less stable products less likely to pay dividends – consistent with product life cycle explanation as in Abernathy and Utterback's (1975) and Klepper (1996).

(2) Firms with differentiated products that operate in local concentrated markets are more likely to pay dividends and repurchase shares

(3) Firms that sell more to business clientèles are more likely to pay dividends but this clientèle effect does not similarly affect firm repurchase decisions consistent with signaling to business clientèles.

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Hoberg-Phillips Industries

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