Predicting box-office success of motion pictures with neural networks

Ramesh Sharda, Dursun Delen

Research output: Contribution to journalArticle

156 Scopus citations

Abstract

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 network to models proposed in the recent literature as well as other statistical techniques using a 10-fold cross validation methodology shows that the neural networks do a much better job of predicting in this setting.

Original languageEnglish
Pages (from-to)243-254
Number of pages12
JournalExpert Systems with Applications
Volume30
Issue number2
DOIs
StatePublished - 1 Feb 2006
Externally publishedYes

Keywords

  • Box-office receipts
  • CART
  • Forecasting
  • Logistic regression
  • Motion pictures
  • Neural networks
  • Prediction
  • Sensitivity analysis

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