Predicting box-office success of motion pictures with neural networks

Predicting box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, the use of neural networks in predicting the financial performance of a movie at the box-office before its theatrical release is explored. In our model, the forecasting problem is converted into a classification problem-rather than forecasting the point estimate of box-office receipts, a movie based on its box-office receipts in one of nine categories is classified, ranging from a `flop' to a `blockbuster.' Because our model is designed to predict the expected revenue range of a movie before its theatrical release, it can be used as a powerful decision aid by studios, distributors, and exhibitors. Our prediction results is presented using two performance measures: average percent success rate of classifying a movie's success exactly, or within one class of its actual performance. Comparison of our neural ne...

• Type: Journal
• Year: 2006
• Where: ESWA
• Authors: Ramesh Sharda, Dursun Delen
• Keywords: Box-office Receipts, ESWA 2006, Neural Networks, Theatrical Release