Predicting movie revenue before committing significant investments

Abstract

ABSTRACT A movie originates at the development stage when investors evaluate multiple scripts, attach key cast and crew, and shape the budget. Investors prefer to consider various scenarios before committing a large sum of money when selecting the key cast and crew. An accurate revenue prediction at this early stage reduces the risk of investment. We use a little information available at the early stage to predict movie revenue to enhance potential investors’ decision-making before greenlighting the project. In this study, we develop a movie revenue prediction model using multiple predictors, which are essentially based on key cast and crew, the storyline (movie genre), and the distinctive characteristics of the movie, which are available at the development stage. Our study applies multiple machine learning algorithms to predict movie revenue and demonstrates significant improvement over traditional statistical regression methods. The most accurate machine learning algorithm reduces the forecast error of total and opening-week box office revenue by 10.02% and 9.20%, respectively, over the benchmark multivariate linear regression method. Our results exhibit that including a movie’s distinctive characteristics reduces the average forecast error of total and opening-week box office revenue across all algorithms by 9.22% and 8.43%, respectively.