Forecasting financial success of hollywood movies a comparative analysis of machine learning methods

Dursun Delen, Ramesh Sharda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Forecasting financial success of a particular movie has intrigued many scholars and industry leaders as a worthy but challenging problem. In this study, we explore the use of machine learning methods to forecast the financial performance of a movie at the box-office before its theatrical release. In our models, we convert the forecasting problem into a multinomial 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." Herein, we present our comparative prediction results along with variable importance measures (using sensitivity analysis on trained prediction models).

Original languageEnglish
Title of host publicationICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics
Pages653-656
Number of pages4
Publication statusPublished - 26 Oct 2012
Externally publishedYes
Event9th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2012 - Rome, Italy
Duration: 28 Jul 201231 Jul 2012

Publication series

NameICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics
Volume1

Conference

Conference9th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2012
CountryItaly
CityRome
Period28/07/1231/07/12

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Keywords

  • Box-office receipts
  • Hollywood
  • Machine learning
  • Neural networks
  • Prediction
  • Sensitivity analysis

Cite this

Delen, D., & Sharda, R. (2012). Forecasting financial success of hollywood movies a comparative analysis of machine learning methods. In ICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics (pp. 653-656). (ICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics; Vol. 1).