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The Role of Soft and Hard Information in the Pricing of Assets and contract Design -- Evidence from Screenplays Sales

William N. Goetzmann Yale School of Management S. Abraham Ravid, Rutgers University and Cornell University Ron Sverdlove, Rutgers University Vicente Pons-Sanz, Renaissance Capital

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The Role of Soft and Hard Information in the Pricing of Assets and contract Design -- Evidence from Screenplays Sales

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  1. William N. Goetzmann Yale School of Management S. Abraham Ravid, Rutgers University and Cornell University Ron Sverdlove, Rutgers University Vicente Pons-Sanz, Renaissance Capital The Role of Soft and Hard Information in the Pricing of Assets and contract Design -- Evidence from Screenplays Sales

  2. Objective This one of very few papers outside the financial intermediation industry, which shows how soft information affects asset pricing. Second, we look at empirical contract design in a setting of pure risk sharing and information asymmetries, with no effort component. Third, we can compare ex-ante pricing of a screenplay to ex-post performance of resulting movies, an experiment which is difficult to perform in other industries.

  3. Research Design • We look at sales of screenplays. We consider prices as well as contract design (cash upfront vs. a contingent contract). We test for “large” vs. “small” buyers. • The independent variables we use include the complexity and nature of the “pitch” which proxies for soft information, as well as “hard information” variables on the screenwriter’s experience. • We include control variables • We also consider the performance of the resulting films.

  4. Literature Review- Soft Information. There is a growing literature on the role of soft information in organizations: The main theoretical focus is on how soft information affects organizational structure: See Laffont and Tirole (1997), Stein (2002) Faure Grimaud et al. (2003) Baker et al. (1994); Important recent applications of the concept of soft information focus on the financial intermediation industry, where soft information is combined with hard information, inclduing Petersen and Rajan (2002), Petersen (2004), Berger et al. (2005) Liberti (2004) shows how soft information proxies in the banking sector affect the price of working capital loans. Butler (2004) considers the pricing of municipal bond issues. Petersen (2004) provides a conceptual survey. Management studies include Uzzi (1999) and Uzzi and Gilespie (2002) who introduce related concepts, such as “embeddedness” and duration and “multiplexity” of banking relationship. Cohen and Carruthers (2001) present an interesting historical study.

  5. Literature Review- Contract Design • A huge theoretical literature. • Empirical Contract Design papers include for example, Lerner and Merges, (1998) - Bio-medical Industries; Gompers and Lerner(1996) Kaplan and Stromberg (2003), Bengtsson et al.,(2005)- Venture capital; Banerjee and Duflo (2000) – Indian Software industries. • In the movie industry- Chisholm (1997) and Eliashberg et al. (2007).

  6. Soft and Hard Information and the Film Industry Thefilm industry is a mechanism for turning ideas into profit. A major portion of the industry is devoted to the solicitation, evaluation, screening and business assessment of artistic projects. Many of these projects begin as script concepts that are read by agents, pitched to studio professionals, reviewed within studio companies, discussed and approved or rejected at meetings, optioned or purchased by studios through simple or contingent contracts, revised and re-written as part of the production process and finallyreviewed by industry participants for awards. This process uses soft as well as hard information.

  7. A Conceptual Model of Soft Information There is no universally accepted definition of soft information. Some authors implicitly suggest that soft information is information that is difficult (costly) to communicate to outsiders (See Stein (2002) and others). In this case, we can differentiate between soft and hard information by the cost of transmission. Also, if you “work harder” you can make soft information “harder”. Soft information can also be defined as a non-numeric input into a decision-making process, or information that is “communicated in text”(Petersen,2004). Soft Information can also be regarded as data for which human cognition is required and can be interpreted differently by different people. Our variables attempt to proxy for the existence of information that is hard to transmit and open to different interpretations by different people. We use the number of words in the pitch and whether or not other films are mentioned, and the number of genres specified.

  8. Examples: • Short and sweet: • Greatest Escapes: Several 12 year old kids escape from a camp from hell. • On any given Saturday Remembering the Titans Gives me the Varsity Blues: Spoof of football movies [Note that the title is longer than the logline.] • Long and complex: • Joe Somebody: “Corporate guy who is divorced and at the end of his rope is beaten up and humiliated by a co-worker over a parking space. He confronts his fears and in the process comes to terms with what he wants out of life and ultimately falls in love again”.

  9. Examples (continued): • Short with another movie mentioned: • Act of treason: “In the line of Fire” meets the “body guard”. • Several Genres: • Spoils of war; genre: action adventure comedy ; A newly found treasure map leads three soldiers to look for rewards just days before the Kuwait desert storm invasion. (a comedy???)

  10. Large vs. Small • Theoretical models (see Stein, 2002) suggest that large hierarcial organizations will shun soft information. • Empirical papers (Liberti, 2004, Berger et al. 2005) suggest that this is indeed the case in the banking sector. • We consider large studios vs. other buyers, and expect large studios to pay more for “harder” screenplays.

  11. Contingent contracts. • In equilibrium, a cash compensation should be offered to a risk averse writer by a multi-national conglomerate (no effort issues), approaching the “first best” a-la- Holmstrom(1979). • However, consider the following example- • Seller and buyer agree that if a screenplay is produced it is worth 10,000 and if not, 0. • Seller thinks the probability is 0.5, buyer 0.1. • A cash contract will not work. A contract contingent on production will.

  12. Forward looking prices • “Nobody knows anything” William Goldman (1983)- or efficient markets?

  13. Data The 2003 Spec Screenplay Sales Directory, compiled by Hollywoodsales.com, contains approximately six years of screenplays sales. The information provided on each sale includes: title, pitch, genre, agent, producer, date-of-sale, purchase price, and buyer and the type of contract; sometimes additional information,. We search IMDB for screenwriter information, in particular, how many of his screenplays had been produced; we also check IMDB and our data set for first time screenwriters. For each movie produced, we obtain its financial performance from Baseline services in California. Specifically, we have the budget of each film, domestic revenues, international revenues as well as video and DVD revenues.

  14. Data (2) We obtain several additional control variables. MPAA ratings (in particular, family friendly ratings) were significantly correlated with revenues and returns in a number of previous papers . Our sample is somewhat skewed with no G rated films and too many PG-13 rated films (see Ravid (1999), Ravid and Basuroy (2004), DeVany and Walls (2002) Fee (2001) and Simonoff and Sparrow (2000)). Stars can matter – we consider academy awards and nominations and starmeter rankings from IMDB.pro.. Reviews- in its Crix pix column, Variety classifies reviews as “pro”, “con”, and “mixed.” We use these classifications to come up with measures of the quality of critical reviews. Finally, we look up each film’s release date (see Einav (2003)). Variables we create:

  15. Variables: Soft Information Proxies.The idea- to approximate complexity, difficulty of transmission and “fuzziness”. • Soft Information – Script Complexity Variables • Words Loglinecounts the number of words in the script logline (pitch). • Soft_Words equals 0 if the script logline contains less than 20 words; 1 if the script logline contains between 21 and 30 words; 2 if the script logline contains between 31 and 40 words; and 3 if the script logline contains more than 40 words. (we tried several other cutoffs). • Highwords equals 1 if the number of words in the logline is greater than 40. • InfoDummy equals 1 if additional information about the script is available. • Transparent Script: We create a script complexity index, that equals 1 when the log line contains less than 20 words (i.e. Soft_Words equals 0), and additional information about the script is available (i.e. InfoDummy equals 1). • Soft_Genre equals 1 if the qualified number of genres is 2 or greater, and 0 otherwise. • Soft_Logmovies equals 1 if the scripts logline refers to any other movie, and 0 otherwise.

  16. “Hard Information” variables • Number Movies measures the number of scripts previously sold by the script’s screenwriter. • Reputation Moviestakes the value 0 if the screenwriter has not previously sold any script; 1 if the screenwriter has previously sold between 1 and 3 scripts; 2 if the screenwriter has previously sold between 4 and 10 scripts; and 3 if the screenwriter has previously sold more than 10 scripts. • First Movie takes the value one if the screenwriter has not previously sold any script, and 0 otherwise. • Nominated Oscar (Awarded Oscar)takes the value 1 if the screenwriter has been previously nominated to an Oscar. • Any Nomination (Any Award) takes the value 1 if the screenwriter has been previously nominated to an award in the following festivals: Oscars, Golden Globes, British Academy Awards, Emmy Award, European Film Award, Cannes, Sundance, Toronto, Berlin.

  17. Prices and soft information

  18. Screenwriter’s experience

  19. Small vs. Large Organizations

  20. Price (table 2)

  21. Price- contingent contract, movie not made (table 4)

  22. The Probability of a contingent contract (table 5)

  23. Movies by screenwriter (not reported)

  24. Total Revenues

  25. Rate of return regressions

  26. Results and conclusions: • Our analysis supports the notion that hard information as well as soft information are priced in screenplays sales. • Soft information lowers the price. In a market where distance and relationships cannot be applied differentially, soft information is viewed as a risk factor. Reputation increases prices paid. • Large studios pay more, but seem to shun soft information, as expected. • Even in the absence of effort incentives, we do not observe “first best” risk sharing. “softer” screenplays and less experienced writers are likely to sell as contingent contracts. • Prices paid for screenplays are correlated with the eventual success of the movies produced. Somebody knows something (contrary to William Goldman’s suggestion)

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