Finding Nemo: Predicting Movie Performances by Machine Learning Methods

Jong-Min Kim, Leixin Xia, Iksuk Kim, Seungjoo Lee and Keon-Hyung Lee
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Jong-Min Kim: Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA
Leixin Xia: Department of Biostatistics and Data Science, University of Texas Health Science Center, Houston, TX 77030, USA
Iksuk Kim: Department of Marketing, California State University, Los Angeles 5151 State University Dr, Los Angeles, CA 90032, USA
Seungjoo Lee: Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea
Keon-Hyung Lee: Askew School of Public Administration and Policy, Florida State University, Tallahassee, FL 32306-2250, USA

JRFM, 2020, vol. 13, issue 5, 1-12

Abstract: Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this research combined Bayesian variable selection and machine learning methods for forecasting the return on investment (ROI). We also attempt to compare machine learning methods including the quantile regression model with movie performance data in terms of in-sample and out of sample forecasting.

Keywords: quantile regression; neural network; machine learning; forecasting (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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