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Finding Nemo: Predicting Movie Performances by Machine Learning Methods

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Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA
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Department of Biostatistics and Data Science, University of Texas Health Science Center, Houston, TX 77030, USA
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Department of Marketing, California State University, Los Angeles 5151 State University Dr, Los Angeles, CA 90032, USA
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Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea
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Askew School of Public Administration and Policy, Florida State University, Tallahassee, FL 32306-2250, USA
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Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(5), 93; https://doi.org/10.3390/jrfm13050093
Received: 11 April 2020 / Revised: 30 April 2020 / Accepted: 2 May 2020 / Published: 9 May 2020
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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. View Full-Text
Keywords: quantile regression; neural network; machine learning; forecasting quantile regression; neural network; machine learning; forecasting
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Kim, J.-M.; Xia, L.; Kim, I.; Lee, S.; Lee, K.-H. Finding Nemo: Predicting Movie Performances by Machine Learning Methods. J. Risk Financial Manag. 2020, 13, 93.

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