Forecasting Box-Office Receipts of Motion Pictures Using Neural Networks

Forecasting box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, we explore the use of neural networks in forecasting the financial performance of a movie at the boxoffice before its theatrical release. In our model, we convert the forecasting problem into a classification problem—rather than forecasting the point estimate of box-office receipts, we classify a movie based on its box-office receipts in one of nine categories, ranging from a “flop” to a “blockbuster.” Because our model is designed to predict the financial success of a movie before its theatrical release, it can be used as a powerful decision aid by studios, distributors, and exhibitors. We present our exciting prediction results using three different performance measures: average percent success rate, improvement over random sampling, and similarity to perfect classification. Using sensitivity analysis we also present an evaluation of the decision variables and their impact on the box-office success.