Importance The article focuses on the neural network forecasting in the film-making industry. Objectives The article examines what opportunities economic and mathematical modeling provides to forecast revenue and profit from coming movie distribution and identifies factors that determine whether film-making business becomes a commercial success. Methods The economic and mathematical model relies upon the neural network trained with available historical data on movie distribution and including 20 input parameters. Computer experiments were performed with the ‘freezing’ method. We used the neural network for computations if any of input data changes, meanwhile the rest of them remain the same. Results Root-mean-square relative error of the model accounted for 13.8 percent, with the coefficient of determination being 0.86 percent. We refer to The Da Vinci Code, Star Wars to demonstrate what the model is capable of. Conclusions and Relevance A virtual increase in the film budget influences projections of box-office grosses and revenue differently. Other aspects of films also have an effect on the film-making success. Having conducted computer experiments, we provided our recommendations, which could boost box-office grosses of films. The proposed economic and mathematical model can be used to optimize financial costs and choose parameters to plan new films to come. The model allows for forecasting box-office grosses and profit from film-making, and examines how various aspects influence the commercial result of film-making.
Holbrook M.B., Hirschman E.C. The Experiential Aspects of Consumption: Consumer Fantasies, Feelings and Fun. Journal of Consumer Research, 1982, vol. 9, iss. 2, pp. 132–140.
Eliashberg J., Sawhney M.S. Modeling Goes to Hollywood: Predicting Individual Differences in Movie Enjoyment. Management Science, 1994, vol. 40, iss. 9, pp. 1151–1173. URL: Link
Sharda R., Delen D. Predicting Box-Office Success of Motion Pictures with Neural Networks. Expert Systems with Applications, 2006, vol. 30, iss. 2, pp. 243–254. URL: Link
Litman B.R. Predicting Success of Theatrical Movies: An Empirical Study. The Journal of Popular Culture, 1983, vol. 16, iss. 4, pp. 159–175. URL: Link
Riwinoto M.T., Selly Artaty Zega, Gia Irlanda. Predicting Animated Film of Box-Office Success with Neural Networks. Jurnal Teknologi, 2015, vol. 77, iss. 23, pp. 77–82.
Nevolin I.V., Tatarnikov A.S. [Models to project box-office grosses of film-making on the basis of emotional drivers of demand]. Ekonomika i sotsium, 2014, no. 4, pp. 1244–1259. (In Russ.) URL: Link%202014%204.pdf
Wasserman M., Mukherjee S., Scott K. et al. Correlations Between User Voting Data, Budget and Boxoffice for Films in the Internet Movie Database. Journal of the Association for Information Science and Technology, 2015, vol. 66, iss. 4, pp. 858–868. URL: Link
Ghiassi M., Lio D., Moon B. Pre-Production Forecasting of Movie Revenues with a Dynamic Artificial Neural Network. Expert Systems with Applications, 2015, vol. 42, iss. 6, pp. 3176–3193. URL: Link
Dhar T., Sun G., Weinberg C.B. The Long-Term Box Office Performance of Sequel Movies. Marketing Letters, 2012, vol. 23, iss. 1, pp. 13–29. URL: Link
McCulloch W.S., Pitts W.A. Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 1990, vol. 52, iss. 1-2, pp. 73–97.
Cherepanov F.M., Yasnitskii L.N. [Neural network filter for excluding outliers in statistical data]. Vestnik Permskogo universiteta. Seriya: Matematika. Mekhanika. Informatika = Perm University Herald. Series: Mathematics. Mechanics. Informatics, 2008, no. 4, pp. 151–155. (In Russ.)
Rosenblatt F. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. New York, Spartan Books, 1962, pp. 245–248.
