Text-Based Product Characteristics, Competition and Dividends Presented at 2011 UBC Winter Conference Gerard Hoberg Gordon Phillips Nagpurnanand Prabhala Robert H. Smith School of Business University of Maryland, College Park

Research Question  How the structure and evolution of a firm’s product space shapes its payout policy.  Text to characterize product space Fluidity, competition, and product customer type  Several payout decisions Propensity to pay dividends + initiations, omissions, changes. Repurchases Dividend-repurchase substitution 2

Motivation: Fluidity Brav, Graham, Harvey, Michaely (2005) “ Sustainability and stability of future earnings are the most important determinants of payout” policy.” Sustainable earnings less likely in fluid product markets in flux due to rivals. 3

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Motivation: Fluidity We construct a new metric of fluidity from product text. Why should this matter? Fluidity measures ex-ante threats. This can be quite different from measured ex-post cash flow risk and both can matter. Power. Product text is (a) voluminous; (b) timely. Thus, it contains detailed, forward looking information about product markets as seen by senior managers., AAPL “Music” 024 “Phone” 035 # words SIC

Hypotheses H1: Fluidity -Firms facing fluid product markets are less likely to make payouts. Especially via dividends. H2: Competition and Differentiation -Firms with differentiated products and in more protected markets should be more likely to pay dividends. H3: Business (not investor) clientele -Business customers may value long-term stability to ensure stable supply chain. -If yes, then firms with more business (non-retail) clientele should prefer dividends over repurchases.. 7

Related Literature Product life cycle (Abernathy-Utterback, 1978) -Stable products and dominant designs late in life cycle favor payouts. Life cycle + agency (DeAngelo et al., 2009) -Mature firms pay out to avoid agency problems. -Competitive threat from product market fluidity makes disciplining dividends less necessary. Firm maturity: DeAngelo et al., 2006; Grullon et al, Maturity is gradual ageing over life cycle. -But old, mature firms can also face fresh threats in the product market. We can pick these up. -Both can matter for payout. 8

Related literature Payout Policy Reviews by Allen and Michaely (2005), DeAngelo, DeAngelo, and Skinner (2009). CEO/CFO surveys by Lintner (1956), and Brav et al. (2005). Investor-driven clientele hypothesis finds weak support. Brav et al. (2005); Grinstein and Michaely (2005), Jain (2007). Dividends vs. Repurchases: Fama-French-2001, Grullon-Michaely-2002, or Jagannathan, Stephens, and Weisbach Choice boils down to managers’ view about permanence and stability of cash flows. 9

Related literature Text-Based Analysis Asset pricing applications are in Tetlock-2007, Tetlock- Tsechanksy-Macskassy-2008, and Loughran-McDonald Studies relate word content to stock price movements. The roots of this paper are in Hoberg-Phillips (2010a,b) -HP introduce product text to the corporate literature -We build on their work by introducing new metrics of fluidity and dynamics of product space 10

Data The guts of our sample is from HP -49, Ks from 1997 to 2005 for product text Ks from 1996 only used for starting lagged variables. -95%+ of eligible COMPUSTAT/CRSP sample

Linguistics: Converting Text to Quantitative Mappings 1.Union of all words in product descriptions (87,385 in 1997) minus words in more than 5% of all 10-Ks (3027 in 1997) a.Optional: throw out words that are not part of “cliques.” b.Remaining “local” words are “industry vocabularies” 2.Form boolean vectors for all word vectors (1=word used, 0=not used). Normalize to unit length. 3.Compute “cosine similarities” or dot products of these 84,000 element vectors. 13

Cosine Similarity  Hoberg and Phillips (2010a, 2010b) introduce the notion of cosine “similarity.” -Similarity is the dot product between a firm’s word list and another vector of words. -HP analyze firm-to-firm similarity for each year and reconstruct industry pairs. 14

Similarity in HP Firm 1: “They sell cabinet products.” Firm 2: “They operate in the cabinet industry.”  Step 1) Drop words "they", "the", "and", "in" (common words).  Step 2) 5 elements: "sell" "operate", "cabinet", "products", "industry" P 1 = (1,0,1,1,0) P 2 = (0,1,1,0,1)  Step 3) Normalize vector to have unit length of 1:  V 1 = (.577,0,.577,.577,0) V 2 = (0,.577,.577,0,.577)  Step 4) Compute document similarity V 1 V 2 =  Document similarity is bounded between (0,1) 15

Similarity for “Fluidity” We develop HP ideas of similarity along new directions. 1.Product Fluidity Cosine similarity between own word vector and a vector of word changes. -Local fluidity – based on close competitors -Broad fluidity – aggregate word change list. Note that it excludes 5% common words. 2.Self Product Fluidity 1- Cos(  it,it-1 ) (similarity of 10K to last years 10K) 3.Business Clientele Similarity See next slide 16

“Similarity” for 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. 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 17

Fluidity Some Familiar Examples  Microsoft (started dividends)  Adobe Systems (stopped dividends)  Apple Computers (never paid and still does not) 18

Table V: Dividend Payer Likelihood Regressions include controls for firm risk, firm age, M/B, Asset Growth, Profitability, Firm Size, R&D, and Negative Earnings Dummy. Conclude: Fluidity (-), Clientele (+), Concentration (+) 25

Smorgasboard of Controls 26

Table VIII: Economic Magnitude 27

Table VII: Repurchaser Likelihood 29 Conclude: Product characteristics impact repurchase likelihood. Opposite effect of Business Clientele Similarity.

Dividends (upper panel) and Repurchases

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Tables XI to XIII: Initiations/Omissions 33 Dividend Initiation Policy Conclude: Fluidity significantly impacts initiations. Historically hard to explain in the data.

Tables XII Dividend Omissions 34

Conclusions “Product Characteristics and competition matter”