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Early box office prediction in China’s film market based on a stacking fusion model

Author

Listed:
  • Yi Liao

    (Southwestern University of Finance and Economics)

  • Yuxuan Peng

    (Southwestern University of Finance and Economics)

  • Songlin Shi

    (Southwestern University of Finance and Economics)

  • Victor Shi

    (Wilfrid Laurier University)

  • Xiaohong Yu

    (Shanghai Business School)

Abstract

Artificial intelligence has been increasingly employed to improve operations for various firms and industries. In this study, we construct a box office revenue prediction system for a film at its early stage of production, which can help management overcome resource allocation challenges considering the significant investment and risk for the whole film production. In this research, we focus on China’s film market, the second-largest box office in the world. Our model is based on data regarding the nature of a film itself without word-of-mouth data from social platforms. Combining extreme gradient boosting, random forest, light gradient boosting machine, k-nearest neighbor algorithm, and stacking model fusion theory, we establish a stacking model for film box office prediction. Our empirical results show that the model exhibits good prediction accuracy, with its 1-Away accuracy being 86.46%. Moreover, our results show that star influence has the strongest predictive power in this model.

Suggested Citation

  • Yi Liao & Yuxuan Peng & Songlin Shi & Victor Shi & Xiaohong Yu, 2022. "Early box office prediction in China’s film market based on a stacking fusion model," Annals of Operations Research, Springer, vol. 308(1), pages 321-338, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03804-4
    DOI: 10.1007/s10479-020-03804-4
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    References listed on IDEAS

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    Cited by:

    1. 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.

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