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Authors: Dursun Delen and Ramesh Sharda

Affiliation: Oklahoma State University, United States

Keyword(s): Prediction, Box-office Receipts, Hollywood, Machine Learning, Neural Networks, Sensitivity Analysis.

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).

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Paper citation in several formats:
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 (ICINCO 2012) - Volume 1: ANNIIP; ISBN 978-989-8565-21-1; ISSN 2184-2809, SciTePress, pages 653-656. DOI: 10.5220/0004125006530656

@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 (ICINCO 2012) - Volume 1: ANNIIP},
year={2012},
pages={653-656},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004125006530656},
isbn={978-989-8565-21-1},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2012) - Volume 1: ANNIIP
TI - Forecasting Financial Success of Hollywood Movies - A Comparative Analysis of Machine Learning Methods
SN - 978-989-8565-21-1
IS - 2184-2809
AU - Delen, D.
AU - Sharda, R.
PY - 2012
SP - 653
EP - 656
DO - 10.5220/0004125006530656
PB - SciTePress

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