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.

引用

页数：12

共 50 条

[31] Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis
Martinez-Licort, Rosmeri
Sahelices, Benjamin
de la Torre, Isabel
Vegas, Jesus
HEALTH SCIENCE REPORTS, 2025, 8 (02)
[32] Predicting Renal Toxicity of Compounds with Deep Learning and Machine Learning Methods
Bitopan Mazumdar
Pankaj Kumar Deva Sarma
Hridoy Jyoti Mahanta
SN Computer Science, 4 (6)
[33] Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale
Su, Yang
Zhang, Huang
Gabrielle, Benoit
Makowski, David
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 10
[34] Machine learning approaches for predicting impact sensitivity and detonation performances of energetic materials
Liu, Wei-Hong
Liu, Qi-Jun
Liu, Fu-Sheng
Liu, Zheng-Tang
JOURNAL OF ENERGY CHEMISTRY, 2025, 102 : 161 - 171
[35] Predicting Firms' Performances in Customer Complaint Management Using Machine Learning Techniques
Peker, Serhat
INTELLIGENT AND FUZZY SYSTEMS: DIGITAL ACCELERATION AND THE NEW NORMAL, INFUS 2022, VOL 2, 2022, 505 : 280 - 287
[36] Comparing Machine Learning Classifiers for Movie WOM Opinion Mining
Kim, Yoosin
Kwon, Do Young
Jeong, Seung Ryul
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2015, 9 (08): : 3169 - 3181
[37] Accuracy Analysis of Machine Learning Methods for Predicting PM Concentration
Kim, Yeong-Il
Lee, Kwon-Ho
JOURNAL OF KOREAN SOCIETY FOR ATMOSPHERIC ENVIRONMENT, 2023, 39 (02) : 149 - 164
[38] An evaluation of machine-learning methods for predicting pneumonia mortality
Cooper, GF
Aliferis, CF
Ambrosino, R
Aronis, J
Buchanan, BG
Caruana, R
Fine, MJ
Glymour, C
Gordon, G
Hanusa, BH
Janosky, JE
Meek, C
Mitchell, T
Richardson, T
Spirtes, P
ARTIFICIAL INTELLIGENCE IN MEDICINE, 1997, 9 (02) : 107 - 138
[39] Predicting crop yields in Senegal using machine learning methods
Sarr, Alioune Badara
Sultan, Benjamin
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2023, 43 (04) : 1817 - 1838
[40] Machine Learning Methods for Predicting the Outcome of Safety Culture Attributes
Yap, Edwin
INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS, 2024, 23 (01): : 80 - 88

← 1 2 3 4 5 →