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

Dursun Delen, Ramesh Sharda

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 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