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Movie Advertising and the Stock Market Valuation of Studios: A Case of “Great Expectations?”

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  • Amit M. Joshi

    (College of Business Administration, University of Central Florida, Orlando, Florida 32816)

  • Dominique M. Hanssens

    (Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095)

Abstract

Product innovation is the key revenue driver in the motion picture industry. Because major studios typically launch fewer than 20 movies per year, the financial performance of a single release can have a major effect on the studio's profitability. In this paper we study how single movie releases impact the investor valuation of the studio. We analyze the change in postlaunch stock price and predict the direction and magnitude of excess returns based on the revenue expectation built up for a movie release. That expectation is set, in part, by media support; i.e., highly advertised movies are expected to draw larger audiences than others. By using an event-study methodology, we isolate the impact of a movie launch on studio stock price and track the determinants of that change. We examine a comprehensive data set comprising over 300 movies released by the largest studios. Our results indicate a clear between the marketing support received by a movie and the direction and magnitude of its excess stock return post launch. Movies with above average prelaunch advertising have lower postlaunch stock returns than films with below average advertising. Our findings also suggest that movies that are hits at the box office may result in a lowering of stock price if they had high media support because of high performance expectations built up prior to launch. Thus prelaunch advertising plays a dual role of informing consumers about a movie's arrival as well as helping investors form expectations about the studio's profit performance.

Suggested Citation

  • Amit M. Joshi & Dominique M. Hanssens, 2009. "Movie Advertising and the Stock Market Valuation of Studios: A Case of “Great Expectations?”," Marketing Science, INFORMS, vol. 28(2), pages 239-250, 03-04.
  • Handle: RePEc:inm:ormksc:v:28:y:2009:i:2:p:239-250
    DOI: 10.1287/mksc.1080.0392
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    References listed on IDEAS

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    1. Anita Elberse & Jehoshua Eliashberg, 2003. "Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures," Marketing Science, INFORMS, vol. 22(3), pages 329-354.
    2. Andrew Ainslie & Xavier Drèze & Fred Zufryden, 2005. "Modeling Movie Life Cycles and Market Share," Marketing Science, INFORMS, vol. 24(3), pages 508-517, November.
    3. Elberse, Anita & Anand, Bharat, 2007. "The effectiveness of pre-release advertising for motion pictures: An empirical investigation using a simulated market," Information Economics and Policy, Elsevier, vol. 19(3-4), pages 319-343, October.
    4. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    5. Robert E. Krider & Tieshan Li & Yong Liu & Charles B. Weinberg, 2005. "The Lead-Lag Puzzle of Demand and Distribution: A Graphical Method Applied to Movies," Marketing Science, INFORMS, vol. 24(4), pages 635-645, April.
    6. Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, vol. 18(2), pages 115-136.
    7. Ball, R & Brown, P, 1968. "Empirical Evaluation Of Accounting Income Numbers," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 159-178.
    8. Jehoshua Eliashberg & Jedid-Jah Jonker & Mohanbir S. Sawhney & Berend Wierenga, 2000. "MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures," Marketing Science, INFORMS, vol. 19(3), pages 226-243, January.
    9. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    10. Natalie Mizik & Robert Jacobson, 2007. "Myopic Marketing Management: Evidence of the Phenomenon and Its Long-Term Performance Consequences in the SEO Context," Marketing Science, INFORMS, vol. 26(3), pages 361-379, 05-06.
    11. Praveen K. Kopalle & Donald R. Lehmann, 2006. "Setting Quality Expectations When Entering a Market: What Should the Promise Be?," Marketing Science, INFORMS, vol. 25(1), pages 8-24, 01-02.
    12. Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, vol. 15(2), pages 113-131.
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