@inproceedings{8e2ac6e5cd0c4a148b68012844e6a6b2,
title = "Forecasting financial success of hollywood movies a comparative analysis of machine learning methods",
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).",
keywords = "Box-office receipts, Hollywood, Machine learning, Neural networks, Prediction, Sensitivity analysis",
author = "Dursun Delen and Ramesh Sharda",
year = "2012",
month = oct,
day = "26",
language = "English",
isbn = "9789898565211",
series = "ICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics",
pages = "653--656",
booktitle = "ICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics",
note = "9th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2012 ; Conference date: 28-07-2012 Through 31-07-2012",
}