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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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S. Abraham Ravid, Rutgers University and Cornell University
Ron Sverdlove, Rutgers University
Vicente Pons-Sanz, Renaissance Capital
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.
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.
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.
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.
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.
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: