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

Yi Liao, Yuxuan Peng, Songlin Shi, Victor Shi and Xiaohong Yu ()
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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

Annals of Operations Research, 2022, vol. 308, issue 1, No 12, 338 pages

Abstract: 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.

Keywords: Artificial intelligence; Film industry; Predictive model; Machine learning; Box office forecast; Stacking fusion model (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-020-03804-4

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