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Are box office revenues equally unpredictable for all movies? Evidence from a Random forest-based model

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  • Evgeny A. Antipov

    (National Research University Higher School of Economics)

  • Elena B. Pokryshevskaya

    (National Research University Higher School of Economics)

Abstract

In this study we develop a model for early box office receipts forecasting that, in addition to traditionally used regressors, uses several inputs that have never been used before, but appeared to be very useful predictors according to our variable importance analysis. New predictors account for the power of actors and directors, as well as for the intensity of competition at the time of movie release. Instead of Motion Picture of Association of America (MPAA) ratings commonly used in movie success prediction, textual information about the reasons for giving a movie its MPAA rating was formalized using word frequency and principal components analyses. The expert system is based on the Random forest algorithm, which outperformed a stepwise regression and a multilayer perceptron neural network. A regression tree-based diagnostic approach allowed us to detect the heterogeneity of model accuracy across segments of data and assess the applicability of the model to different movie types.

Suggested Citation

  • Evgeny A. Antipov & Elena B. Pokryshevskaya, 2017. "Are box office revenues equally unpredictable for all movies? Evidence from a Random forest-based model," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(3), pages 295-307, June.
    • Handle: RePEc:pal:jorapm:v:16:y:2017:i:3:d:10.1057_s41272-016-0072-y
    DOI: 10.1057/s41272-016-0072-y
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    References listed on IDEAS

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    1. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    2. Jehoshua Eliashberg & Sam K. Hui & Z. John Zhang, 2007. "From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts," Management Science, INFORMS, vol. 53(6), pages 881-893, June.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Flores, Benito E, 1986. "A pragmatic view of accuracy measurement in forecasting," Omega, Elsevier, vol. 14(2), pages 93-98.
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    Cited by:

    1. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    2. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    3. Liza Lee & Ying-Sing Liu, 2024. "The impact of major infectious disease events and government relief packages on the film industry: evidence from Taiwan," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

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