Pre-production forecasting of movie revenues with a dynamic artificial neural network

A model based upon a dynamic artificial neural network (DAN2) for the forecasting of movie revenues during the pre-production period and an alternative modeling strategy by adding production budgets, pre-release advertising expenditures, runtime, and seasonality to the predictive variables are presented.

Abstract

We show DAN2 to be an effective tool for forecasting movie revenues.DAN2 improved upon benchmark ANN-based revenue forecasting models by 32.8%.We develop a new model to forecast movie revenues during the pre-production period.We offer new insights into the role of various movie attributes in revenue forecasts.DAN2 achieves an accuracy rate of 94.1% with this new model and variable-set. The production of a motion picture is an expensive, risky endeavor. During the five-year period from 2008 through 2012, approximately 90 films were released in the United States with production budgets in excess of $100 million. The majority of these films failed to recoup their production costs via gross domestic box office revenues. Existing decision support systems for pre-production analysis and green-lighting decisions lack sufficient accuracy to meaningfully assist decision makers in the film industry.Established models focus primarily upon post-release and post-production forecasts. These models often rely upon opening weekend data and are reasonably accurate but only if data up until the moment of release is included. A forecast made immediately prior to the debut of a film, however, is of limited value to stakeholders because it can only influence late-stage adjustments to advertising or distribution strategies and little else.In this paper we present the development of a model based upon a dynamic artificial neural network (DAN2) for the forecasting of movie revenues during the pre-production period. We first demonstrate the effectiveness of DAN2 and show that DAN2 improves box-office revenue forecasting accuracy by 32.8% over existing models. Subsequently, we offer an alternative modeling strategy by adding production budgets, pre-release advertising expenditures, runtime, and seasonality to the predictive variables. This alternative model produces excellent forecasting accuracy values of 94.1%.