Forecasting Financial Success of Hollywood Movies - A Comparative Analysis of Machine Learning Methods

Dursun Delen, Ramesh Sharda

2012

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).

References

  1. Eliashberg, J. and Sawhney, M. S.: Modeling Goes to Hollywood: Predicting Individual Differences in Movie Enjoyment, Management Science 40(9), (1994) 1151-1173.
  2. Eliashberg, J., Junker, J. J., Sawhney, M. S. and Wierenga B.: MOVIEMOD: An Implementable Decision Support System for Prerelease Market Evaluation of Motion Pictures. Marketing Science, 19(3), (2000) 226-243.
  3. Litman B. R. and Ahn, H.: Predicting Financial Success of Motion Pictures. In The Motion Picture Mega-Industry by B. Litman. Allyn & Bacon Publishing, Boston, MA. (1998).
  4. Litman, B. R.: Predicting Success of Theatrical Movies: An Empirical Study, Journal of Popular Culture, 16(9), (1983) 159-175.
  5. Principe, J. C., Euliano, N. R. and Lefebre, W. C.: Neural and Adaptive Systems: Fundamentals Through Simulations. New York: John Wiley and Sons (2000) Ravid, S. A.: Information, Blockbusters, and Stars: A Study of the Film Industry, Journal of Business, 72(4), (1999) 463-492.
  6. Sawhney, M. S. and Eliashberg, J.: A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures, Marketing Science, 15(2), (1996) 113-131.
  7. Seni, G. and Elder, J.: Ensemble Methods in Data Mining: Improving Accuracy through Combining Predictions. Morgan & Claypool Publisher (2010).
  8. Sochay, S.: Predicting the performance of motion pictures, The Journal of Media Economics 7(4), 1-20.
  9. Baldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford Digital Library Metadata Architecture. Int. J. Digit. Libr. 1 (1994) 108-121.
  10. Valenti, J.: Motion Pictures and Their Impact on Society in the Year 2000. Speech given at the Midwest Research Institute, Kansas City, April 25, (1978) 7.
  11. Zufryden, F. S., Linking Advertising to Box Office Performance of New Film Releases: A Marketing Planning Model,” Journal of Advertising Research, July-Aug. (1996) 29-41.
Download


Paper Citation


in Harvard Style

Delen D. and Sharda R. (2012). Forecasting Financial Success of Hollywood Movies - A Comparative Analysis of Machine Learning Methods . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ANNIIP, (ICINCO 2012) ISBN 978-989-8565-21-1, pages 653-656. DOI: 10.5220/0004125006530656


in Bibtex Style

@conference{anniip12,
author={Dursun Delen and Ramesh Sharda},
title={Forecasting Financial Success of Hollywood Movies - A Comparative Analysis of Machine Learning Methods},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ANNIIP, (ICINCO 2012)},
year={2012},
pages={653-656},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004125006530656},
isbn={978-989-8565-21-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ANNIIP, (ICINCO 2012)
TI - Forecasting Financial Success of Hollywood Movies - A Comparative Analysis of Machine Learning Methods
SN - 978-989-8565-21-1
AU - Delen D.
AU - Sharda R.
PY - 2012
SP - 653
EP - 656
DO - 10.5220/0004125006530656