Forecasting Movie Box Office Profitability

This study intends to estimate the profit of a movie through the construction of a predictive model that uses several Data Mining techniques, namely neural networks, regression and decision trees. The model will allow obtaining the prediction of box office revenue. Three different dependent variable approaches were used (interval, categorical and binary) aiming to study the difference and predictive influence that each one has on the results. Two metrics were used to determine the most accurate predictions: the misclassification error for the categorical models and the average squared error for the continuous one. In this study, the best predictive results were obtained through the use of multi-layer perceptron. Regarding the representative distinction between the dependent variable, the multiclass model presents a much higher error rate comparing to the remaining ones, which is explained with the increase of the number of classes to predict.

[1]  Sebastián Ventura,et al.  Data mining in course management systems: Moodle case study and tutorial , 2008, Comput. Educ..

[2]  Nian Shong Chok.  PEARSON'S VERSUS SPEARMAN'S AND KENDALL'S CORRELATION COEFFICIENTS FOR CONTINUOUS DATA , 2010 .

[3]  M. Holbrook,et al.  The role of actors and actresses in the success of films: how much is a movie star worth? , 1993 .

[4]  Cherukuri Aswani Kumar,et al.  Intrusion detection model using fusion of chi-square feature selection and multi class SVM , 2017, J. King Saud Univ. Comput. Inf. Sci..

[5]  Jay Prag,et al.  An empirical study of the determinants of revenues and marketing expenditures in the motion picture industry , 1994 .

[6]  Pat Langley,et al.  Models of Incremental Concept Formation , 1990, Artif. Intell..

[7]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[8]  Starling David Hunter,et al.  Predicting Box Office from the Screenplay: An Empirical Model , 2015 .

[9]  Petr Hajek,et al.  Modelling innovation performance of European regions using multi-output neural networks , 2017, PloS one.

[10]  Minh Thao Nguyen,et al.  PREDICTING BOX-OFFICE SUCCESS OF MOVIES IN THE U.S. MARKET , 2011 .

[11]  Arundeep Kaur,et al.  Predicting Movie Success Using Neural Network , 2013 .

[12]  Steven M. Shugan,et al.  Film Critics: Influencers or Predictors? , 1997 .

[13]  W. D. Walls,et al.  Screen wars, star wars, and sequels , 2009 .

[14]  J. Simonoff,et al.  Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers , 2000 .

[15]  J. Eliashberg,et al.  A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures , 1996 .

[16]  Barry Litman.  Predicting Success of Theatrical Movies: An Empirical Study , 1983 .

[17]  Borga Deniz,et al.  WHAT DETERMINES BOX OFFICE SUCCESS OF A MOVIE IN THE UNITED STATES , 2010 .

[18]  Byeng-Hee Chang,et al.  Devising a Practical Model for Predicting Theatrical Movie Success: Focusing on the Experience Good Property , 2005 .

[19]  S. Ravid.  Information, Blockbusters and Stars? A Study of the Film Industry , 1997 .

[20]  Patricia B. Cerrito.  The Difference Between Predictive Modeling and Regression , 2008 .

[21]  Anindita Chakravarty,et al.  The Differential Effects of Online Word-of-Mouth and Critics’ Reviews on Pre-release Movie Evaluation , 2009 .

[22]  E. Deuchert,et al.  For Oscar Glory Or Oscar Money? , 2005 .

[23]  Suman Basuroy,et al.  How Critical are Critical Reviews? The Box Office Effects of Film Critics, Star Power, and Budgets , 2003 .

[24]  Jude W. Shavlik,et al.  Using neural networks for data mining , 1997, Future Gener. Comput. Syst..

[25]  Andrew Whinston,et al.  The Dynamics of Online Word-of-Mouth and Product Sales: An Empirical Investigation of the Movie Industry , 2008 .

[26]  Christopher K. I. Williams.  Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[27]  Scott Sochay.  Predicting the Performance of Motion Pictures , 1994 .

[28]  Pradeep K. Chintagunta,et al.  A Bayesian Model to Forecast New Product Performance in Domestic and International Markets , 1999 .

[29]  Ramesh Sharda,et al.  Predicting box-office success of motion pictures with neural networks , 2006 .

[30]  Barry Litman,et al.  Predicting financial success of motion pictures: The '80s experience , 1989 .

[31]  Roberto Henriques,et al.  Forecasting model of a movie's profitability , 2018, 2018 13th Iberian Conference on Information Systems and Technologies (CISTI).

[32]  Brian Moon,et al.  Pre-production forecasting of movie revenues with a dynamic artificial neural network , 2015, Expert Syst. Appl..

[33]  Ramesh Sharda,et al.  PREDICTING THE FINANCIAL SUCCESS OF HOLLYWOOD MOVIES USING AN INFORMATION FUSION APPROACH , 2008 .

[34]  W. D. Walls,et al.  Modeling Movie Success When ‘Nobody Knows Anything’: Conditional Stable-Distribution Analysis Of Film Returns , 2005 .