Finding Nemo: Predicting Movie Performances by Machine Learning Methods

被引：1

作者：

Kim, Jong-Min ^{[1
]}

Xia, Leixin ^{[2
]}

Kim, Iksuk ^{[3
]}

Lee, Seungjoo ^{[4
]}

Lee, Keon-Hyung ^{[5
]}

机构：

[1] Univ Minnesota, Div Sci & Math, Stat Discipline, Morris, MN 56267 USA

[2] Univ Texas Hlth Sci Ctr Houston, Dept Biostat & Data Sci, Houston, TX 77030 USA

[3] Calif State Univ Los Angeles, Dept Mkt, 5151 State Univ Dr, Los Angeles, CA 90032 USA

[4] Cheongju Univ, Dept Big Data & Stat, Chungbuk 28503, South Korea

[5] Florida State Univ, Askew Sch Publ Adm & Policy, Tallahassee, FL 32306 USA

来源：

JOURNAL OF RISK AND FINANCIAL MANAGEMENT | 2020年 / 13卷 / 05期

关键词：

quantile regression; neural network; machine learning; forecasting; BOX-OFFICE; SUCCESS; REVIEWS;

D O I：

10.3390/jrfm13050093

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

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