Skip to main content
SpringerLink
Log in

Modeling Movie Success When ‘Nobody Knows Anything’: Conditional Stable-Distribution Analysis Of Film Returns

  • Published:
Journal of Cultural Economics Aims and scope Submit manuscript

Abstract

In this paper we apply a recently-developed statistical model that explicitly accounts for the extreme uncertainty surrounding film returns. The conditional distribution of box-office returns is analyzed using the stable distribution regression model. The regression coefficients in this model represent what is known about the correlates of film success while at the same time permitting the variance of film success at the box office to be infinite. The empirical analysis shows that the conditional distribution of film returns has infinite variance, and this invalidates statistical inferences from the often-applied least-squares regression model. The estimates of the stable regression confirm some earlier results on the statistics of the movie business and the analysis demonstrates how to model box-office success in the movie business where “nobody knows anything”.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Albert, S. (1998) “Movie Stars and the Distribution of Financially Successful Films in the Motion Picture Industry.” Journal of Cultural Economics 22: 249–270.

    Article  Google Scholar 

  • Albert, S. (1999) “Reply: Movie Stars and the Distribution of Financially Successful Films in the Motion Picture Industry.” Journal of Cultural Economics 23: 325–329.

    Article  Google Scholar 

  • Bergstrom, H. (1952) “On Some Expansions of Stable Distribution Functions.” Arkiv fur Mathematik 2: 375–378.

    Google Scholar 

  • Blattberg, R. and Sargent, T. (1971) “Regression with non-Gaussian Stable Disturbances Some Sampling Results.” Econometrica 39(3): 501–510.

    Google Scholar 

  • Box, G. and Cox, D. (1964) “An Analysis of Transformations.” Journal of the Royal Statistical Society B 26: 211–243.

    Google Scholar 

  • Brosen, B.W. and Prekel, P.V. (1993) “Linear Regression with Stably Distributed Residuals.” Communications in Statisics Theory and Methods 22: 659–667.

    Google Scholar 

  • Caves, R.E. (2000) Creative Industries: Contracts between Art and Commerce. Harvard University Press, Cambridge.

    Google Scholar 

  • De Vany, A.S. and Walls, W.D. (1996) “Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry.” The Economic Journal 439(106): 1493–1514.

    Google Scholar 

  • De Vany, A.S. and Walls, W.D. (1999) “Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office?” Journal of Cultural Economics 23(4): 285–318.

    Article  Google Scholar 

  • De Vany, A.S. and Walls, W.D. (2002a) “Big Budgets, Movie Stars, and Wide Releases Empirical Analysis of the Blockbuster Strategy.” In Proceedings of the XIX Latin American Meeting of the Econometric Society, São Paulo, Brazil.

  • De Vany, A.S. and Walls, W.D. (2002b) “Does Hollywood Make too Many R-rated Movies? Risk, Stochastic Dominance, and the Illusion of Expectation.” Journal of Business 75(3): 425– 451.

    Article  Google Scholar 

  • De Vany, A.S. and Walls, W.D. (2004) “Motion Picture profit, the Stable Paretian Hypothesis, and the Curse of the Superstar.” Journal of Economic Dynamics and Control 28(6): 1035–1057.

    Article  Google Scholar 

  • Fama, E. (1963) “Mandelbrot and the Stable Paretian Hypothesis.” Journal of Business 36(4): 420–429.

    Article  Google Scholar 

  • Fama, E. (1965) “The Behavior of Stock Market Prices.” Journal of Business 38(1): 34–105.

    Article  Google Scholar 

  • Goldman, W. (1983) Adventures in the Screen Trade. Warner Books, New York.

    Google Scholar 

  • Halvorsen, R. and Palmquist, R. (1980) “The Interpretation of Dummy Variables in Semilogarithmic Equations.” American Economic Review 70(3): 474–475.

    Google Scholar 

  • Hand, C. (2002) “The Distribution and Predictability of Cinema Admissions.” Journal of Cultural Economics 26: 53–64.

    Article  Google Scholar 

  • Huber, P.J. (1964) “Robust Estimation of a Location Parameter.” Annals of Mathematical Statistics 35: 73–101.

    Google Scholar 

  • Judge, G. Griffiths, W. Hill, R. Lutkepohl, H. and Lee, T. (1985) The Theory and Practice of Econometrics. Wiley, New York, second edition.

  • Litman, B.R. (1983) “Predicting the Success of Theatrical Movies: An Empirical Study.” Journal of Popular Culture 16: 159–175.

    Article  Google Scholar 

  • Litman, B.R. and Ahn, H. (1998) “Predicting Financial Success of Motion Pictures: The Early ‘90s Experience”, in The Motion Picture Mega-Industry. Allyn and Bacon, Needham Heights, Massachusetts, chapter 10, pages 172–197.

  • Litman, B.R. and Kohl, L.S. (1989) “Predicting Financial Success of Motion Pictures: The ‘80s experience.” Journal of Media Economics 2: 35–50.

    Google Scholar 

  • Mandelbrot, B. (1963a) “New Methods in Statistical Economics”. Journal of Political Economy 71: 421–440.

    Article  Google Scholar 

  • Mandelbrot, B. (1963b) “The Variation of Certain Speculative Prices.” Journal of Business 36: 394–419.

    Article  Google Scholar 

  • McCulloch, J.H. (1996) “Financial Applications of Stable Distributions”, in Maddala G.S. and Rao C.R. (eds.), Statistical Methods in Finance, volume 14 of Handbook of Statistics. North-Holland, New York, pp. 393–425.

  • McCulloch, J.H. (1997) “Symmetric Stable Linear Regression Computer Code.” Gauss Source Code Archive, The American University, Washington DC.

  • McCulloch, J.H. (1998a) “Linear Regression with Stable Disturbances,” in Adler, R.J. Feldman, R.E. and Taqqu, M.S. (eds.), A Practical Guide to Heavy Tails Statistical Techniques and Applications. Birkhäuser, Berlin, pp. 359–378.

  • McCulloch, J.H. (1998b) “Numerical Approximation of the Symmetric Stable Distribution and Density,” in Adler R.J. Feldman R.E. and Taqqu M.S. (eds.), A Practical Guide to Heavy Tails Statistical Techniques and Applications. Birkhäuser, Berlin, pp. 489–499.

  • Mosteller, F. and Tukey, J.W. (1977) Data Analysis and Regression. Addison-Wesley, Reading, MA.

    Google Scholar 

  • Nolan, J.P. (1998) “Univariate Stable Distributions Parameterizations and Software,” in Adler, R.J., Feldman, R.E. and Taqqu, M.S. (eds.), A Practical Guide to Heavy Tails Statistical Techniques and Applications. Birkhäuser, Berlin, pp. 527–533.

  • Prag, J. and Cassavant, J. (1994) “An Empirical Study of Determinants of Revenues and Marketing Expenditures in the Motion Picture Industry.” Journal of Cultural Economics 18(3): 217–35.

    Article  Google Scholar 

  • Rachev, S. and Mittnik, S. (2000) Stable Paretian Models in Finance. John Wiley & Sons, New York.

    Google Scholar 

  • Ravid, S.A. (1999) “Information, Blockbusters and stars: A study of the film industry.” Journal of Business 72: 463–486.

    Article  Google Scholar 

  • Ravid, S.A. (2003) “Are They All Crazy or Just Risk Averse? Some Movie Puzzles and Possible Solutions”, in Ginsburgh V. ed., Economics of the Arts and Culture. Contributions to Economic Analysis No. 260. Elsevier Science, Amsterdam.

  • Ruppert, D. and Carroll, J. (1980) “Trimmed Least Squares Estimation in the Linear Model.” Journal of the American Statistical Association 75: 828–838.

    Google Scholar 

  • Samorodnitsky, G. and Taqqu, M.S. (1994) Stable Non-Gaussian Random Processes. Chapman and Hall, New York.

    Google Scholar 

  • Sedgwick, J. and Pokorny, M. (1999) “Comment Movie Stars and the Distribution of Financially Successful Films in the Motion Picture Industry.” Journal of Cultural Economics 23: 319–323.

    Article  Google Scholar 

  • Smith, S.P. and Smith, V.K. (1986) “Successful Movies: A Preliminary Empirical Analysis.” Applied Economics, 18(5): 501–507.

    Google Scholar 

  • Stata Corporation (2001) Stata Reference Manual, release 7, volume 3. Stata Press, College Station, TX.

  • Wallace, W.T., Seigerman, A. and Holbrook, M.B. (1993) “The Role of Actors and Actresses in the Success of Films: How Much is a Movie Star Worth?” Journal of Cultural Economics 17(1): 1–24.

    Article  Google Scholar 

  • Zellner, A. (1976) “Bayesian and non-Bayesian Analysis of the Regression Model with Multivariate Student-t error terms.” Journal of the American Statistical Association 71: 400–405.

    Google Scholar 

  • Zolotarev, V. M. (1986) One-Dimensional Stable Distributions, volume 65 of American Mathematical Society Translations of Mathematics Monographs. American Mathematical Society, Providence. (Translation of the original 1983 Russian edition.).

Download references

Author information

Authors and Affiliations

  1. Department of Economics and The Van Horne Institute, University of Calgary, Canada T2N 1N4

    W. David Walls

Authors
  1. W. David Walls
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to W. David Walls.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Walls, W.D. Modeling Movie Success When ‘Nobody Knows Anything’: Conditional Stable-Distribution Analysis Of Film Returns. J Cult Econ 29, 177–190 (2005). https://doi.org/10.1007/s10824-005-1156-5

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10824-005-1156-5

Keywords

Search

Navigation