soft by wuyunyi

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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




                                                                    1
                          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.

                                                               2
             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.
                                                    3
     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.
                                                                       4
    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).

                                                    5
 Soft and Hard Information and the Film
                Industry
The film 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 finally reviewed by industry
participants for awards.
This process uses soft as well as hard information.

                                                                  6
 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.


                                                                      7
                       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”.
                                                         8
         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???)
                                 9
            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.
                                                10
           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.

                                                       11
         Forward looking prices
   “Nobody knows anything” William
    Goldman (1983)- or efficient markets?




                                            12
                           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.
                                                                13
                           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)).


                                                                  14
    Variables: Soft Information Proxies.
      The idea- to approximate complexity,
    difficulty of transmission and “fuzziness”.
    Soft Information – Script Complexity Variables
    Words Logline counts 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.
                                                                                              15
      “Hard Information” variables
   Number Movies measures the number of scripts previously sold by
    the script’s screenwriter.
   Reputation Movies takes 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.

                                                                        16
         Prices and soft information

                              N            Price                 Cont
                                    Mean           Median        Mean
SoftWords                 0   296     684              468        0.6014
                          1   214     645              468        0.6028
                          2   139     609              520        0.6331
                          3   102     624              298        0.7157
                    p-value                         0.0441 **     0.1963
InfoDummy                 0   508      623             468        0.6299
                          1   269      697             484        0.5911
                    p-value         0.3368          0.0001 ***    0.2900
TransparentScript         0   651      622             460        0.6252
                          1   100      836             497        0.6100
                    p-value         0.0548 *        0.0267 **     0.7707



                                                                        17
             Screenwriter’s experience

                                N            Price                 Cont
                                      Mean           Median        Mean
ReputationMovies            0   460      475             303        0.6239
                            1   219      718             520        0.6073
                            2    77     1114             565        0.6623
                            3    21     2019            1100        0.3810
                      p-value                         0.0001 ***    0.1209
FirstMovie                  1   460      475             303        0.6239
                            0   317      900             550        0.6057
                      p-value         0.0001 ***      0.0001 ***    0.6080
NomOscar                    0   764      628             468        0.6191
                            1    13     1890             878        0.4615
                      p-value         0.0001 ***      0.0403 **     0.2456




                                                                             18
       Small vs. Large Organizations


                Panel H: Hard and Soft Information: Large and Small Buyer Values



                                        N            Mean Price         Large-Small        Large/Small
                                    Large Small    Large      Small      Difference           Ratio
High Hard, Low Soft                    60   106     1214       771          443       **      1.574

Low Hard, High Soft                    69   168      542       471           71               1.151




                                                                                                      19
                    Price (table 2)

NumberMovies                    53.200 ***
                              (6.7400)
ReputationMovies                             341.494 ***
                                             (7.1400)
NomOscar                                     815.258 ***
                                             (2.8900)
AnyNom                        683.390 ***
                              (4.2100)
TransparentScript             143.939        223.033 **
                              (1.3500)       (2.1000)



                                                      20
Price- contingent contract, movie not
            made (table 4)
         NumberMovies                47.508 ***
                                   (9.1600)
         ReputationMovies

         FirstMovie * LogWords

         SoftWords

         InfoDummy

         FirstMovie * SoftGenres

         SoftLogMovies                33.431
                                    (0.5400)
         HighWords                   -73.684 *
                                   (-1.6700)
         FirstMovie * HighWords

         LargeStudio                 78.526 **
                                   (2.4300)
         Action


                                                  21
The Probability of a contingent
      contract (table 5)
 NumberMovies            -0.020 *
                       (0.0662)
 FirstMovie

 TransparentScript        0.036
                       (0.7941)
 SoftLogMovies

 SoftGenres              -0.053
                       (0.5856)
 HighWords                0.312 **
                       (0.0303)
 LargeStudio              0.073
                       (0.4398)
 Action
                                     22
            Movies by screenwriter (not
                    reported)

                                 FirstMovie = 0 (N = 76)      FirstMovie =1 (N = 66)

                                   Average      St. Dev.   Average    St. Dev.    T-test
Negative Costs                        36300      25100       29400      23400      0.0942
Dom. Print & Advertising Costs        25200      10600       23800      13100      0.4769
Domestic Gross                        41500      44700       35100      39300      0.3645
Domestic Rentals                      22100      23800       18500      20800      0.3423
Foreign Gross                         30100      47600       24200      36600      0.4277
Foreign Rentals                       14300      22500       11600      17700      0.4431
Domestic Video Gross                  22400      16200       19200      19100      0.2968
Domestic DVD Gross                    14000      27500        8380      13400      0.1462
Total Revenues                        80500     124000       65300      83700      0.2588
Rate1                                   3.69       3.44        3.28       3.42     0.5051
Rate2                                   1.70       1.21        1.46       1.10     0.2590



                                                                                           23
Total Revenues




                 24
        Rate of return regressions

Price                         0.0003 ***     0.0004 ***
                            (3.0600)       (3.6100)
Ln Budget 1                  -0.0760
                           (-0.3900)
Ln Budget 2                                  -0.1150
                                           (-0.4800)
PG                           0.4613         (0.8123)
                           (0.6200)         (1.1400)
PG-13                        0.6794 **        0.5302 *
                           (2.4200)         (1.8800)
Positive Review Fraction     1.6092 **
                           (2.0400)

                                                     25
             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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