Digest Finance

The Method for Forecasting Box-Office Grosses of Movies with Neural Network

Vol. 22, Iss. 3, SEPTEMBER 2017

PDF  Article PDF Version

Received: 20 January 2017

Received in revised form: 31 January 2017

Accepted: 22 February 2017

Available online: 21 September 2017


JEL Classification: C02, C45, C53, C83, D83

Pages: 298-309


Yasnitskii L.N. Perm State National Research University, Perm, Russian Federation

Beloborodova N.O. Higher School of Economics, Perm, Russian Federation

Medvedeva E.Yu. Higher School of Economics, Perm, Russian Federation

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.

Keywords: film-making industry, revenue, box-office grosses, neural network, forecast


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