How to Predict Financial Success of Movies based on Big Data

Analytics

Yifan Yao

Mechanical Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213, U.S.A.

Keywords: Analytic, Financial Movie, Machine Learning, Big Data, Prediction.

Abstract: The movie industry is a field with high potential values but movies’ success is highly unpredictable. In this

study, a model is developed to predict the financial performance of movies using big data analytics methods,

which can serve as a reference for movie decision makers. The box office is considered as the major indicators

of the financial performance of movies. Six features are defined to describe the movies and seven machine

learning-based algorithms are used to predict the box office of movies. Four models are selected among them

as component models to formulate the cinema ensemble model (CEM) by voting the estimates of four

component models. The accuracy of the CEM model is tested by the APHR test, which is about 58.5% of

Bingo rate. As a result, the accuracy of the CEM model is higher than all of the seven single component

models.

1 INTRODUCTION

Nowadays, the movie industry serves as a profitable

field of media all around the world, which produces

billions of dollars of revenue every year. However,

the cost of making a movie is also considerable.

Movie makers do not always make a profit but often

result in a loss. Research shows that the average

investment to produce a movie is 65 million USD.

Between 2000 and 2010, only one-third of movies in

the US were profitable (Murschetz et al. 2020).

Numerous cases of failed movies illustrate the high

risk of movie-making industry, where people are

always uncertain whether a movie will be highly

profitable or completely failed. As Lehrer et al.

mentioned, improved forecasts are valuable because

they could increase capital investments by reducing

investor uncertainty of the box office consequences

and also help marketing teams tailor effective

advertising campaigns (Lehrer, Xie 2021).

Therefore, it is critical for movie investors to

develop an approach to predict the success of a movie

before the movie is produced, in order to make better

decisions about whether or not it is worthwhile to

invest money in it. Additionally, for cinemas,

predicting the success of movies is also of significant

need because releasing an unsuccessful movie means

a loss in box-office revenue, which is the main profit

source of cinemas. Therefore, in this study, a model

is introduced to predict the success of movies before

investors and cinemas make financial decisions about

the movies. The cinema ensemble model consists of

4 component models out of 7 machine learning-based

models, and the result is voted by all the component

models. The introduced CEM model has relatively

high accuracy in predicting the box office level of a

movie before it is released. The model can provide

useful information for movie donators and cinemas to

make proper decisions.

2 DATA ACQUISITION

The analyzed dataset is collected from the Korean

Film Council webpage and naver.com. This dataset

includes top the 400 movies by their number of views,

which are released from October 25, 2012, to

December 31, 2014. The movies that go beyond the

top 400 are not considered in this study, because

according to the classification standard presented in

Table 1, most of those movies are classified into class

6 (Flop Class). Therefore, classifying those movies

will be way much easier, which will increase the

accuracy of prediction of our results, but the

improvement is not appreciated since it will only

decrease the effectiveness of our evaluation. In

238

Yao, Y.

How to Predict Financial Success of Movies based on Big Data Analytics.

DOI: 10.5220/0011304800003437

In Proceedings of the 1st International Conference on Public Management and Big Data Analysis (PMBDA 2021), pages 238-243

ISBN: 978-989-758-589-0

addition, film production companies and cinemas are

usually not interested in predicting the performance of

movies that have a limited budget, but are only

interested in the movies that have more budget which

will hopefully create a large amount of revenue.

Hence, including movies beyond the top 400 is not

worthwhile. Among those collected movies, the

author discarded 25 movies that miss the important

information for the features evaluated in this study,

and left the rest of 375 movies. The distribution of

classes for those data are presented in Figure 1 (Lee et

al. 2018).

Table 1: Movie performance classes.

Figure 1: Distribution of movie classes.

3 METHODOLOGY

3.1 Definition

To construct the predictive model of movies, some

parameters are needed to be defined to measure the

financial success of them. In this study, the box office

is considered as the major indicator of movies’

financial success, because it directly affects the total

revenue a movie produces. In other words, it decides

how much money movie investors and cinemas earn

if they buy that movie.

The objective of this study is to forecast the box

office during the first week after the movie is

released, due to the fact that movies obtain the

greatest part of box office in the first week. As

illustrated in Figure 2, the largest portion of a movie’s

revenue (40% on average) is obtained from the box

office sale during the first week of a release, when

other cinema managers are yet to decide whether to

show the movie or not (Murschetz et al. 2020).

19%

20%

19%

25%

1 (Blockbuster)

6 (Flop)

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Figure 2: Average weekly box office performance and average number of movie theaters showing a movie.

3.2 Feature Description

3.2.1 Genre

In this study, movies are classified into 16 categories

of genres, which include: action, adventure,

animation, comedy, criminal, documentary, drama,

epic, family, fantasy, horror, independent, mystery,

romance, science fiction and thriller. Each movie may

subject to multiple categories.

3.2.2 Sequels

Sequels also have some impact on a movie’s success.

Producing sequels for released movies is less risky

than producing movies with new themes. Dhar et al.

have identified that sequels have a positive impact on

both supply and demand side of movie distribution.

More often than not, a sequel movie tends to be

distributed to a significantly larger number of theaters

(i.e., positive impact on the supply side) (Dhar et al.

2012). Firstly, sequel movies can attract a number of

people who have watched previous movies in that

series. Secondly, movie investors and cinemas can

look at the ratings and box office of previous movies

to make sure whether movies in these themes appeal

to the customers.

3.2.3 Number of Plays on the Initial Day of

Release

The third feature of movies is the number of plays on

the initial day of release. The reason why the number

of screens is not used as the feature is that the number

of plays is affected by the length of movies. For

example, a movie with a length of 3 hours will be

played fewer times compared to a movie of 1.5 hours

provided the same number of screens. Therefore, the

shorter movie will have a better chance to succeed in

box office. The data of plays at the initial days is

collected from the website of KMRB.

3.2.4 Movie Buzz: Before the Release

Movie buzz is the fourth feature that may have an

impact on the box office of movies. For example, Liu

(2006) has identified the explanatory power of movie

buzz in box-office prediction. In Liu's research, he

describes the volume of buzz as the major factor that

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explains box-office performance (Liu 2006). The

author counted the number of comments of movies

on Naver Movie (see http://movie.naver.com/) in the

review section before the movies are released. The

number of comments describes how many people

were discussing about the movie before it is released.

3.2.5 Transmedia Storytelling

The fifth feature for movies is whether the movie is a

transmedia storytelling movie or not. The value 1

means yes and 0 means not. A transmedia storytelling

movie is a movie based on television series, comics,

or novels, which have been exposed to the public

before the movie is released. Remade movies are not

considered as transmedia storytelling movies.

3.2.6 Star Buzz

The influence of star power is a strong factor to

decide the box office, because superstars always have

a number of fans, who will pay for the box office even

if the movie is not brilliant enough. Therefore, an

indicator is needed to measure the power of a star.

The method is to count the number of posts on Naver

Blog from two months before the release of the movie

and one month before it. That is because if the

influence of a star is greater, there will be more people

mentioning them on the internet. Also, before the

release of a movie, the stars will do some

advertisements, which will affect the number of

mentions in blogs. Therefore, to avoid this factor,

only the posts more than one month are considered

before the release when the advertisement has usually

not begun yet.

3.3 Algorithms and Models

In this study, 7 different algorithms are used: adaptive

tree boosting, gradient tree boosting, linear

discriminant, logistic regression, neural networks,

random forests, and support vector classifier, to build

some candidate classifiers for the dataset. Then all the

candidates are compared and the candidates with the

high accuracy in prediction are selected as component

models. In this paper, a plurality voting system is

used in which the winning estimate is the one with the

largest votes. Through such a process, an ensemble

model for the prediction of a movie's success can be

constructed. This model is called Cinema Ensemble

Model (CEM). The process is schematized in Fig. 3.

Figure 3: The process to build the CEM model.

4 RESULTS

4.1 Performance Metrics

In this study, the performance metrics of Sharda and

Delen (2006) are adopted. Average Percent Hit Rate

(APHR) is used to measure the accuracy of their

prediction models. There are 2 types of APHRs

calculated in this study: Bingo and 1-Away. Bingo

counts the number of classifications that exactly

matches their actual classes, while 1-Away represents

within-one-class hit rate. For example, if CEM

predicts a movie to be in the class 1 and the actual

outcome of the movie belongs to the class 1, it is

classified as Bingo. On the other hand, if CEM

predicts the movie to be in the class 2 and the actual

outcome of the movie belongs to the class 1 or 3, the

prediction is missed by one class so that it is classified

as 1-Away. If a prediction is missed by more than one

class, it is considered to be a misprediction (Sharda

2006).

4.2 Performance Results

The training process and predictions were done for

each of the seven algorithms, and their accuracies

were recorded using APHR values. The result is

shown in Table 2. Base on the percentage of Bingo

and 1-Away values, the performances of models are

ranked respectively, shown in Table 3.

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Table 2: APHRs of candidate models.

Table 3: Ranking of model performance.

It is noticed that gradient tree boosting has 55.1%

of Bingo rate and 88.3% of 1-Away rate, both are the

highest among seven models. Random forest, logistic

regression and linear discriminant also have

relatively high Bingo rate and 1-Away rate. Neural

networks, adaptive tree boosting and support vector

classifier have relatively low Bingo rate. Therefore,

GTB, LD, LR and RF are chosen as the four models

that have the best performance in predicting the

movies’ box office. These four models serve as the

component models in our CEM model. In the

ensemble approach of CEM, the four models vote and

the largest vote wins the estimate. Since there are four

votes presented, when the number of votes is equal

(e.g., two votes A and two votes B), the class that

GTB votes will be chosen because GTB has the

highest accuracy. The result of the CEM model is

shown in Table 4. It has 58.5% of Bingo rate and

88.3% of 1-Away rate, which performs better than

any of the seven single models.

Table 4: APHRs of CEM.

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5 CONCLUSION

In this study, Cinema Ensemble Model (CEM) is

introduced to predict the financial success of a movie.

The model is based on machine learning methods and

six features of movies: genre, sequel, number of plays

on the first day of release, movie buzz before the

release, transmedia storytelling and star buzz. Then

seven different machine learning classification

algorithms are used to predict the level of box office

for movies: adaptive tree boosting, gradient tree

boosting, linear discriminant, logistic regression,

neural networks, random forests and support vector

classifier. After evaluating the performance of each

model, it is shown that GTB, LD, LR and FR have the

best performances, so they are selected to be the

component models in the ensemble model (CEM).

The four component models estimate the class and

the class of the largest number of votes wins the

estimation. The result shows that CEM model has

58.5% of accuracy, which is generally higher than

previous researches.

This approach of movie box office prediction can

be applied by movie investors and cinemas to make

financial decisions before movies are released. That

is, to decide whether to invest the movie or how much

screen will be provided for that movie. It will help

them to make more accurate and advisable decisions

and therefore produce more revenue for the film

industry.

For researches in the future, firstly, more kinds of

component models can be used and more times of

trials can be applied to increase the accuracy of the

CEM model. Secondly, more features can be used to

identify the movies. For example, sentiment analysis

for dialogues in movies can be applied to describe the

structure of movies and the peaks in movies.

REFERENCES

Dhar, TI., Sun, G.,& Weinberg, C. B. (2012). The long-term

box office performance of sequel movies. Marketing

Letters, 23(1), 13-29.

Lee, K., Park, J., Kim, I. et al. Predicting movie success

with machine learning techniques: ways to improve

accuracy. Inf Syst Front 20, 577–588 (2018).

https://doi.org/10.1007/s10796-016-9689-z.

Liu, Y. (2006). Word of mouth for movies: its dynamics and

impact on box office revenue. Journal of Marketing,

70(3), 74 89.

Murschetz, P. C., Bruneel, C. ., Guy, J.-L. ., Haughton, D. .,

Lemercier, N. ., McLaughlin, M.-D. ., Mentzer, K. .,

Vialle, Q. ., Zhang, C. ., Murschetz, P. C. ., &

Bakhtawar, B. (2020). Movie Industry Economics:

How Data Analytics Can Help Predict Movies’

Financial Success. Nordic Journal of Media

Management, 1(3), 339–359.

https://doi.org/10.5278/njmm.2597-0445.5871.

Sharda, R., & Delen, D. (2006). Predicting box-office

success of motion pictures with neural networks.

Expert Systems with Applications,30(2), 243-254.

Steven F. Lehrer, Tian Xie (2021) The Bigger Picture:

Combining Econometrics with Analytics Improves

Forecasts of Movie.

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