To Reboot or Not to Reboot? Analyzing the Financial Performance of Movie Franchises.

Alexander Spitz Mark Kurt Elon University Elon University

June 2, 2014 Preliminary Draft

ABSTRACT

Two features of the movie industry have generated more attention recently (i) a larger share of revenue is generated from movie franchises and (ii) overseas market now represents more than 50% of box office receipts. In 2011, the industry’s top ten worldwide grossing films where all extensions of successful franchises earning over $7.8 billion (Box Office Mojo, 2012). These new installments can be broken into the following categories: prequels, sequels, remakes, and increasingly reboots. Reboots are a subset of remakes which are unique in that they allow writers to “reset” an entire movie franchise rather than a single movie. Reboots give studios much more freedom to reimagine plots, revive characters, and replace actors with a new cast for multiple movies which should allow for more appealing and thus more profitable films while maintaining key elements which originally captivated audiences. This paper investigates what, if any, financial advantages there are from rebooting and remaking versus other forms of franchise installments and original works. We develop an empirical model and estimating it using Ordinary Least Squares, results indicate reboots and remakes generate fail to generate more revenue than other types of movies. However, reboots and remakes tend to have less variable returns on investment as measured by the coefficient of variation. While these types of movies do not generate as much revenue as their counterparts once other determinants of revenue are factored, they do appear to be a less risky investment. This feature may make remakes and reboots more appealing to studios when undertaking already risky large budget movie projects.

1. INTRODUCTION

Movies have been a dominant source of entertainment for a century, starting out as a novelty and evolving into a major medium for art, entertainment, and communication. The film industry has successfully competed against many other forms of entertainment: sports, radio, live action performances, and television. Furthermore, the industry has experienced significant growth recently despite concerns regarding piracy and the most recent global economic slowdown. Since 2006, worldwide box office receipts have grown 35%, with growth in both the North American market and overseas (Theatrical Market Statistics, 2011). Moreover, overseas growth has outpaced domestic box office growth and by 2011 accounted for almost 60% of revenue (“Hollywood Goes Global,” 2012).

There are many reasons for the movie industries more recent successes. The cinema format has a broad appeal; they allow people to escape from their everyday lives for a few hours. The price is also relatively modest for an afternoon’s or evening’s entertainment; $63 in 2011 for a family of four in the United States. Wide availability through 124,000 screens worldwide, also contribute to Hollywood’s success (Theatrical Market Statics, 2011). Beyond these reasons the type of movies being produced may also contribute to the formats success. The choice to produce additional installments from previous movies or parent movies rather than creating new original works may be playing a role, especially in overseas markets which account for the bulk of the revenue growth.1

As with firms in other industries, a production studio in the motion picture industry’s primary goal is to maximize profits. Firms have a portfolio of investment opportunities with which to devote scarce resources. A popular strategy for firms has been to continue a franchise rather than create original standalone movies as a means to achieve this goal.

While franchises have many attractive features, there are factors which make franchises limit their long-term viability. One such factor is the difficulty in keeping the cast and crew together. It is difficult to sign actors, directors, and crew, to long-term contracts, especially when they are contingent on success of previous installments. The Planet of the Apes franchise is one example. After the first sequel, Charlton Heston declined to sign on for any additional sequels. Along with losing cast and crew members, it can be difficult to develop new story arcs due to the necessity of scaffolding the story onto previous installments. This may constrain writers in ways that make the new movie less entertaining than previous movies.

Financial success for movies is far from certain before they enter the box office, but firms try to increase their chances of success by targeting an audience with an appropriate movie. Once a movie becomes successful, successive movies already have a target audience. Movies such as Star Wars have seen prequels and The Italian Job were remade. Despite this, there may be a tradeoff to continuing the franchise. Sequels and Prequels tend to lose some of their core audience. They also have difficulty attracting new followers who don’t know the entire story.

1 See Table 1 for definitions of various movie types considered.

The difficulty in producing long running franchises and in generating high levels of interest in additional installments often leads to decreased revenues. For example, even the sequel, Underneath Planet of the Apes, with Heston saw revenue fall by 50% from the original movie, Planet of The Apes (Internet Movie Database, 2013). The increasingly popular solution by major production studios to mitigate these factors is to reboot a successful movie franchise or remake an individual movie and begin anew.

Remaking (rebooting) a movie (franchises) is an appealing endeavor for production studios. It allows for the reuse of a tested story-telling framework or universe2 This universe may have captivating characters and compelling story arcs which fans are open to retellings of the same basic story; Batman is one successful example. It is also possible to create more intricate plots with planned sequels and prequels within the movie format with familiar characters that are now unconstrained from events in previous movies. Remakes and reboots of previous successful movies/franchises may be even more attractive due to the fact that the market is much more global, spanning many cultures and societies. Reimagining or restarting a story that people across the world are already familiar with has increasing advantages in penetrating this segmentation within the global market over original material or sequels and prequels. This is due to the fact that most of the growth is in developing economies such as China where people are just starting to go to movies with frequency and may have some reference for the movie but may not have seen previous installments,. (Theatrical Market Statistics, 2011).

In this paper, we seek to understand the financial benefits to remaking movies and rebooting franchises. We focus on the top 100 grossing films worldwide over a recent ten year period. The remakes we use in our sample are both remade movies and rebooted franchises. Overall, approximately 22% of movies in our sample where reboots or remakes. Given the rise in reboots and remakes, it is important to understand what role remakes have in determining the revenue from a new motion picture.

We develop an empirical model to estimate the effect of remaking a movie franchise on domestic and overseas revenues after controlling for other known determinants of box office revenues.3 The model isolates the financial benefits of remaking a movie (franchise) compared to producing any other type of film whether it is a sequel, prequel or an original movie, all else equal. We focus on two aspects, box office revenues (domestically and overseas), and develop a measure of risk for return on investment. We do not find evidence that remakes and reboots increase box office revenue over continuing a franchise through sequels or prequels. However, we develop a simple measure of risk and find that remakes and reboots tend to be less risky than other types of movies. If Hollywood is becoming more risk averse, then studios may be choosing to remake and reboot franchises as a way to minimize risk. This may be an optimal decision on the part of studios in an environment where production costs are increasing and revenues are increasingly coming from overseas markets.

2 The universe is a self-consistent fictional setting with elements which may differ from the real world and governs

the general environment in which all the stories within the franchise are set. 3 We define domestic to be United States and Canada box offices. We will refer overseas revenue as all markets

outside of the United States and Canada.

2. LITERATURE REVIEW Over the last 20 years there has been a growing literature devoted to the economics of the movie industry, including a literature studying demand for movies through the determinants of box office revenue and a few of the more prominent categories study the effect of stars’ effect, critic reviews, and continuations for certain types of franchises.4 De Vany and Walls (1999), for instance, look at whether star power reduces the risk in making a movie. They find no significant results, and conclude “anything can happen.” Essentially, there is no efficient way of predicting a movies’ success. The audience makes a movie a hit, not the actor, so it comes down to whether an audience is actually going. De Vany and Walls focus on the uncertainty of movie returns and how star power affected the distribution of earnings rather than how the movie type impacts the earnings. While we use a similar estimation strategy we differ from De Vany and Walls by omitting Pareto ranking but controlling for many additional factors which help explain variation in returns including the impact of remakes and reboots.

Albert (1998) takes a different approach in identifying the effect of star actors by focusing on the consumer behavior aspect in movie-going. Albert posits movie-goers choose to make the choice to see a particular movie based on their previous movies they have seen. Rather than directly modeling financial success for a movie, Albert’s employs a consumer choice stochastic process, using actors as markers. He finds that this process predicts particular distributions of successful films marked by actors, allows for failures of actors, and finally it provides a way for investors to calculate probabilities of success of using such stars. Similar to Albert (1998), Hand (2002) focuses on the success of cinema admissions, not revenue but the demand to go to the movie theater. Hand’s star model reinforces that idea particular movies are difficult to forecast accurately.

Basuroy, Chatterjee, and Ravid (2003) discuss the role of critic reviews in the movie industry. Critics have played an important role in consumers’ decisions in general. Basuroy et. al. investigate three issues related to how critics affect box office success. They realize that critics influence and predict a movie, they can affect a movie with a good or bad review, and finally the use of star power and budgets which would moderate the impact of a critic review. The authors find that negative and positive reviews are correlated with the success of a movie. More importantly, they find that a negative review has more of an impact than a positive review. The authors also find star power lessens the impact of a negative review. Sùarz-Vàzquez (2011) uses individual mover-goers surveys to disentangle the impact of critics’ reviews and star power. Sùarz-Vàzquez also finds that negative reviews are a statistically significant determinant. However, the authors do not control for any franchise effects. Basuroy and Chatterjee (2008) extend their previous work by focusing on revenue generated from sequels. They find that sequels perform worse than the original movie. Among sequels, ones which follow the original movie quickly perform better at the box office. While their study deals with several characteristics of franchises it study does not investigate the impact of prequels, remakes or reboots. Ginsburgh, Pestieau, and Weyers (2006) focused their research on remakes compared to their originals. They found that the majority of remakes have lower quality than originals. In

4 For a thorough survey of the recent literature, see (McKenzie 2012).

addition they also found that on average, remakes with a lower quality have smaller financial returns than the original movies. Brewer, Kelley, and Jozefowics (2009) ask a similar question to Ginsburgh et al. (2006) which analyze the performance of the US film industry. Brewer et al divide the question into two pieces: ex ante and ex post. The ex-ante component is a measure they develop to proxy for a “pre-existing” audience. Using data from 1997 to 2001, they analyze variables such as: budget, theaters, critic reviews, ratings, genre, word-of-mouth, release dates, nominations, star power, personal income and a measure for pre-existing audience. Their results indicate that budget, summer and holiday releases, critic reviews, select genres have a positive significant impact on box office success. Moreover, they find that sequels have a positive correlation to a movies’ chance of success. Terry, De’Armond, and Butler (2005) findings mirror Brewer et al (2009) in that critic reviews, sequels, award nominations, the release date and budget are positively correlated with the success of a movie, while R-rated movies are negatively correlated with its success. While find study several aspects of franchises including installment in franchise and sequel type, they do not look at either reboots or remakes impact on revenues. The absence of reboots and remakes into models of financial determinants for box office revenues seems striking. The reason is most likely due to the rapid popularization of the remake and reboot by studios. While the remakes have been around for a long time, reboots and the frequency of their production is a recent phenomenon.

3. Theory and Empirical model

3.1 Supply: Production studios face a market with little influence on prices, few large competitors, with potentially high fixed costs depending on the movie type produced and low marginal costs. This environment describes one of monopolistic competition (Tirole, 1988). Studios are forced to differentiate their product along many different factors including: genre, franchise, screenplay, NCAA rating, stars, special effects, release date, and production quality. Studios may choose a large scale production with extremely high fixed costs into the hundreds of millions of dollars or small scale at less than fifty thousand dollars.

Given, the profit maximization problem the studio faces, which factors are important in production studios’ decision regarding the type of movie to produce? Considering the staggering cost for these one-off productions and the static nature of the price for movie tickets; studios have strong incentives both to generate the most box office admissions at the least possible cost to maximize profits.

Franchises represent one way of reducing costs of a movie. Adding a new installment in a franchise represents lower costs, enhancing their appeal through lower fixed costs. Further, there are several ways to lower fixed costs of a particular installment. One way is to buy rights to the franchise or original movie with options to produce new installments. A second way is to create long-term optioned contracts for cast and crew as in The Dark Knight trilogy for the second and third installments (Jesser and Pourroy, 2012). In The Lord of the Rings trilogy, multiple cost savings techniques were employed including, most notably, shooting all three films simultaneously over 15 months (Braddock, 2002). This simultaneous production method

is being repeated for The Hobbit and planned for James Cameron’s Avatar sequels (Bishop, 2013).

There are lower marketing costs as well. People are familiar with the story and which means that awareness of the new installation can be made with less marketing dollars. The Hangover II had such a “controversial” trailer that Warner Brothers demanded all movie theaters to stop running it. Many believed the controversy was hyped by Warner Brothers actions in order to garner additional publicity immediately preceding the sequel’s release. The success of the original movie aided to the believability of the possibly overly explicit or offensive content. The familiarity of the comedy film added to the “controversy” and consequently created a larger marketing presence than what could have been achieved as an original film, thereby lowering marketing costs (Port, 2011).

Essentially, 30 seconds for a movie trailer about the continuation of a successive franchise can transmit more about a movie compared to an original movie, as people know the characters and story arcs. Typically, millions of people have heard of the franchise which saves on marketing expenses. It is also easier to distribute as theater owners are dealing with a product in which they are familiar.

Recently, we have seen the industry move towards the continuation of franchises but also remakes and reboots. While production and domestic marketing costs are clearly important, another factor is the changing market for movies. We posit this move is motivated in part by the rising dependence on foreign revenues. Barriers to entry are higher for foreign markets as cultural and social norms as well as language make it more difficult for a movie to be successful. Thus it makes it expensive to market an original movie. However, once audiences across the globe are familiar with the original movie, it is less expensive to market remakes and reboots. Today, any return on investment is more valuable than in past years and with each new movie, Hollywood is hampered by marketing costs. With these expensive problems, industries are forced to make commercials short, and to the point. When you continue a previous film adaptation this is relatively easy.

3.2 Demand Mover-goers face a market with many other potential movie-goers with no influence on the price, small to no barriers to entry. This is very similar to a perfect competition on the demand side of the market. Movie-goers draw utility from attending movies and how many and which movies they would like to see. There are many factors which impact a mover-goer’s utility defined in the following utility function:

( ), for all (1)

∑ { } { } (2)

where Equation 1 is the maximization problem for the movie goer. is the movie

specific enjoyment factor, is the number of times the movie-goer chooses to go to movie up to movies. is the composite consumption and is the composite price. Equation 2

governs the movie-goers budget constraint. We assume the price of a movie, , is fixed and represents wealth.5

The key component in the preceding equations for studios is . This movie-specific enjoyment component governs the number of times the movie-goer will see movie and the distribution of determines the total number of tickets sold for movie , all else equal. There are many factors involved in determining this component and in fact it is not particularly well understood. Hence, difficulty in forecasting box office returns (Hand, 2002).

It is this component of demand which we will estimate. As mentioned earlier, there are several determinants which have been previous studied. These include: stars, critic reviews, ratings, genres, awards and award nominations, and franchise including sequel and prequels. We add to this literature by including the role of remakes and reboots into our analysis as determinants of box office revenue.

3.2.1 The Role of Remakes and Reboots as Determinants While remakes and reboots share many advantages of other franchise extensions, they

also have several distinct advantages which studios may find attractive. For example, developing an original screenplay and producing a movie where the story and characters are unfamiliar to potential audiences contains more risk over a new screenplay based on an existing franchise. One reason is that there is a track record of success with an established audience base. The universe for franchises tends rich environment to create new stories with multiple plots, long story arcs, and compelling, yet familiar, characters. Batman is an excellent example. There are many rich characters including the protagonist and several antagonists. Reboots and Remakes recast and update the story which allows writers much more latitude for character development and new stories while maintaining some continuity which ideally is what drew audiences to the franchise, and it allows for a new cast. People become invested in these characters and are interested in how the story unfolds around the characters. Studios can leverage the existing audience base who may know have children. Parents take their kids to the remake or reboot and both groups can relate to the movie content.

One successful reboot is Star Trek directed by J.J. Abrams. The story of James Kirk and his crew was completely reset, allowing writers the latitude for character development and new stories which could not occur in a sequel or prequel as it would create huge inconsistencies within the universe. The planet Vulcan was destroyed and many characters were altered. In short, the movie was altered in such a way it was no longer directly related to previous movies. However, it did draw on the same universe and basic story lines. Therefore, it is not a new franchise but a reboot. Not only did it prove to be successful, but it rejuvenated a franchise that had not received much popularity as of late (IMDB, 2011).

5 While there is some price variation across geographical location and projection format such as 3D,

within region and format for newly released movies prices tend to be highly elastic. See Orbach and

Einav (2007) for an investigation into the uniformity in movie ticket prices.

Another advantage of reboots and remakes is that they allow for a new cast and are more likely to attract higher caliber directors. You are not left with actors who have aged beyond the plausibility of the role. There is unlikely to be another Indiana Jones sequel as Harrison Ford is currently 69. A reboot would allow for a new Indiana Jones who would fit better into the fedora. It can also be difficult to attract a marque director to work on a sequel or prequel.6 These stories are constrained and can limit the creative vision of the director.

There are also some financial benefits to rebooting a franchise. Sony was in talks with Tobey Maguire on a Spider-Man 4, before those discussions collapsed in January 2010. They were left with a few options: one being find a new Spiderman and continue the franchise. The other option, which they are now doing, is starting over. They are now rebooting the franchise focusing on Spiderman’s teenage life. There were also concerns of a budget upwards of $250 million with Spider-Man 4; by rebooting they are now going to spend less than $100 million. Finally, the original Spider-Man movies grossed over $2.5 billion worldwide, by rebooting they can create sequels afterwards that can potentially equal that revenue (Movie reboots, 2010).

3.3 Empirical Model

We adopt the estimation strategy from Brewer et al (2009) and Gemser, Van Oostrum,

and Leenders (2007) and Terry et al. (2005). We use a reduced form model to estimate the

effect of the movie specific demand, on box office revenues via ordinary least squares. Our

model differs from previous work in that we add reboots and remakes as possible determinants

of box office revenues. Our methodology is designed to isolate the effects of remakes and

reboots on total revenue from a movie. The base specification is displayed in Equation 3.

(3) The dependent variable is gross revenue. We estimate our model for domestic gross revenue, overseas gross revenue, and worldwide gross revenue. In order to isolate the effects of remakes and reboots, we need to capture other influences on revenue. We control for other factors such as budget, various measures of movie quality, genre, season, number in franchise, as well as year and month of release fixed effects. For a thorough description of each variable, please see Appendix B. As a further specification, we present another model that controls for the number in series. The number in series differs from number in franchise in a nuanced manner. While number in franchise refers to the explicit number in the franchise, number in series refers to the number since the reboot or remake.7

6 Cornet (2012) interviews director Bryan Singer regarding the difficulty of stepping into the middle of a franchise.

7 For example, when Daniel Craig replaced Pierce Brosnan in the James Bond franchise a new series was created

with the film Casino Royale. Casino Royale was the 22nd

movie in the James Bond franchise and the first movie in the new series.

DATA

Our dataset is comprised of 816 movies collected from Box Office Mojo, a subsidiary of Internet Movie Database Incorporated. These data comprise the top 100 grossing movies in the United States from 2002-20118. This dataset includes prequels, sequels, remakes, reboots, parent and original movies. We adopt a data collection method similar to Terry et al. (2005) only we include additional movie types and update the sample. Table 2 lists the remakes and reboots in the sample. This differs from the dataset used in Basuroy and Chatterjee (2008) and Ginsburgh et al. (2006) which only include parents and sequels. The scope of their paper is only to compare the parent movie from the sequel. We are concerned with performance of all types of movies including non-parent original movies. This is an important distinction as studios have select which movies they which to produce from a portfolio which includes all of these movie types.

In addition to the domestic gross revenue, we also have overseas revenue, and worldwide revenue. We also have other characteristics of movies which may be determinants of box office revenue including release date, NPAA rating, director, cast, movie type, genre, etc. were collected from Box Office Mojo. Critic reviews and nominations were retrieved from the International Movie Database. All monetary values were adjusted for inflation with 2010 being the base year.

Descriptive statistics are shown in Table 3. The average worldwide movie revenue is $211 million, ranging from $1.33 billion (The Lord of the Rings: The Return of the King, 2003) and $26.7 million (Grindhouse, 2007). 7.2% of the sample contained reboots and remakes; the most successful being $1.02 billion (Alice in Wonderland, 2010) while the least successful was $38.1 million (The Stepfather, 2009). Finally, sequels and prequels made up 15.2% of the sample. The most successful movie was $1.33 billion (The Lord of the Rings, 2003) and the least successful was $33.1 million (Agent Cody Banks @: Destination London, 2004). Finally, the most popular month for a release is December, having 12.1%.

RESULTS and DISCUSSION

The results from the OLS estimation of Equation 3 are listed in Table 4. Displayed are results covering domestic, foreign, and worldwide box office receipts. The variables theaters, opening weekend theaters, and opening weekend revenues are only available for U.S. releases; however we keep them for the foreign and worldwide regressions to serve as a proxy.

The domestic model explains 65 percent of box office success, the overseas model explains 52 percent, and the worldwide model explains 66 percent of the variation. Results from all three models suggest that reboots and remakes do not significantly influence box office performance. Moreover, results from all three models are relatively unvarying and indicate similar findings for separate geographies.

While remakes and reboots do not impact box office performance, there is evidence that sequels and prequels have a significant effect on receipts. In the U.S. and worldwide markets, the results suggest that sequels and prequels yield higher box office receipts than any

8 One hundred eighty four movies were omitted from the sample due to omitted indicators from Box Office Mojo.

other script by 32.29 percent and 40.32 percent, respectively. Relating to sequels, prequels, reboots, and remakes, the number in franchise has a significant impact on box office success across all models, suggesting that an established movie franchise is appealing to audiences. For example, the results indicate that each successive movie produced yields a 2.7 percent increase, a 5.1 percent increase, and a 3.5 percent increase in box office receipts for domestic, foreign, and worldwide releases, respectively.

In addition to movie type, other characteristics such as budgets, reviews, nominations, release demographics, and R-rated films have significant effects on box office revenues. The results indicate that the budget of a movie is only significant in the foreign market, which is producing the significance for the worldwide industry as well. The results indicate that a one percent increase in the budget will increase foreign revenues by 0.55 percent and have a net effect of worldwide revenues with only a 0.20 percent increase in revenue. Results for critic review and nominations are also consistent with theory. For an additional unit increase in the critic review rating, revenues increase by 0.79 percent, 0.49 percent, and 0.75 percent for domestic, foreign, and worldwide receipts. Meanwhile, for an additional movie nomination, the results indicate that revenues increase by 2.28 percent, 4.07 percent, and 2.67 percent for domestic, foreign, and worldwide receipts. Interestingly, the results suggest that R-rated movies are significant only in foreign markets and not anywhere else. Compared to any other rating, R-rated movies have 46.2 percent higher revenues. Finally, the amount of theaters a movie plays in is significant for box office performance. This result is consistent with theory, as any increase in demand for a movie is only going increase the amount of theaters that supply the movie. For a one percent increase in the number of theaters a movie played, revenues increase by 1.49 percent, 2.49 percent, and 1.52 percent for domestic, foreign, and worldwide receipts.

Other significant covariates include opening weekend performance, opening weekend theaters, and January as the most profitable month to release a movie. Some results from this regression are not as expected as reboots and remakes, and the number in series does not have a significant impact on revenue performance. Further R-rated movies seem to have a significant impact on foreign box office performance. Further, movies tend to not strive for the R-rated movie as it limits its viewers at the movie theaters; thus surprising that the results suggest an increase in the revenue of a movie overseas. Despite statistical insignificance, the worldwide and domestic revenues are negatively signed, fitting correctly with theory. Finally, the release date of the movie shows some evidence that fits with theory. The general thinking is that summer and Christmas time movies are where most blockbusters are produced. Results suggest some evidence of this. All of the results are compared against January, of the significant months, all coefficients are negative, suggesting that revenues are lower in those months compared to January. However May, June, and July are insignificant, suggesting that those months are not statistically different from January. One aspect of reboots and remakes that our baseline does capture is related to the decision reboot a movie franchise. An important factor is the revenue an additional installment can be expected to earn relative to the previous movie in the franchise. Our hypothesis is that movie franchises exhibit diminishing returns to additional installments. In order to test the

hypothesis, we estimate a model with non-linearity in the number in franchise covariate, see Equation 4.

(4) The distinction between Equations 3 and 4 is that we include squared terms on the number in franchise and number in series variables. We add these to determine if there is a diminishing impact on sequels and prequels in general, and also sequels preceding a rebooted franchise. Regression results are shown in Table 5, however yield consistent results with significance levels and signs of coefficients from Equation 3. In addition, however, the squared terms do have significance and are negatively signed, suggesting that there is a diminishing impact. For the number in franchise, the turning point for domestic markets is after the 28th movie and for worldwide markets, the turning point is after the 31st movie. For the number in the series, the turning point is on the third movie for the domestic market and after the sixth movie for the worldwide market. Important to note, our model may still contain some unobserved heterogeneity. As the literature suggests, there are other factors that influence a movies’ success that cannot be controlled for. This can range anywhere from what actors or directors are in the movie, on demand viewing, the state of the economy, and geographic tastes, among others. COEFFICIENT OF VARIATION Given the regression results and that reboots and remakes were not significant, we still want to investigate a reason why filmmakers are producing reboots and remakes. As such, we investigate other statistical factors regarding the decision to produce these types of scripts. In the last decade, movie goers saw resurgence in reboots and remakes, which is likely to not be an anomaly. Given the generally poor state of the economy, it could be that these movies were just considered to be a safe asset. Movie industries are spending tens and hundreds of million dollars, at which point studios seek to have a return on the investment. Thus, risk aversion may provide an explanation as to why movies were being rebooted.

We first analyze the return of the asset. Equation 5 displays the return on investment, (ROI).

(5)

In our case, the gain would be revenue and the cost would be the budget. This is a useful measure because quite simply, it measures the profitability of an investment opportunity. However ROI does not explain the likelihood of profitability and in addition does not explain the risk attached with an investment. Given that the standard deviation is the

average distance from the mean, it is difficult to fully interpret what is considered risky when comparing different sized assets. In order to control for this, we calculate the ROI’s coefficient of variation (CV). The CV is displayed in Equation 6.

(6)

This statistic is useful because it measures the volatility in each particular asset. For a less risky asset that number should be as close to zero as possible, as it would show a steady return in your investments. We took the CV on the ROI for reboots, and original movies and the results are shown in Table 6. We see that reboots are significantly less risky than an original script or even a sequel or a prequel. So what we learn from this is reboots may not help maximize revenue in a movie, but it is more likely to return a safe return on the investment. CONCLUSION Based off of the empirical evidence, there is a financial benefit to rebooting a movie; however it does show to be a safe asset, especially compared against remakes, sequels/prequels and an original script. As previous literature suggested, the success of a movie is based on the audience. The movie industry can manipulate this and try to both retain fans and create a larger audience and still be successful, as long as they do not “drag it on.” However, we can contribute knowledge on strategies that the movie industry can focus on. We saw that there are diminishing returns to a movie franchise, and that at some point it is worthwhile to stop making sequels, take time off, and reboot the movie. Future research in this area could address the success of making a book into a movie franchise, or drawing from a television series.

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Hand, C. (2002). The distribution and predictability of cinema admissions. Journal of Cultural Economics, 26(1), 53-64. doi: 10.1023/A:1013389211323 Harris, P. (2011, Jan 1). Hollywood takes a safe bet for 2011 with the year of the repeat. The Observer, Retrieved from http://www.guardian.co.uk/film/2011/jan/02/hollywood- remakes-sequels-weinstein International movie database. (2011). Retrieved from http://www.imdb.com on June 15, 2012. Jesser, J. D. and Pourroy, J. (2012). The art and making of the dark knight trilogy, New York,

New York: Abrams. Litman, B. R. (1983). Predicting success of theatrical movies: An empirical study. The Journal of Popular Culture, 16(4), 159–175. doi: 10.1111/j.0022-3840.1983.1604_159.x McKenzie, J. (2012). The economics of movies: a literature survey. Journal of Economic Surveys.

26 (1), 42-70. Movie 'reboots' are the latest fad in hollywood. (2010, Sep 18). Herald Review, Retrieved from http://www.herald-review.com/entertainment/movies/article_78400556-c1c1-11df- a838-001cc4c002e0.html Port, L. (2011, April 7). Monkey business and marketing genius: Why the hangover 2 trailer incident is worth studying. Legal Productivity, DOI: http://legalproductivity.rocketmatter.com/legal-marketing/monkey-business-and- marketing-genius-why-the-hangover-2-trailer-incident-is-worth-studying/ Orback, B. and Einav, :. (2007) Uniformity prices for differentiated goods: The case of the movie-theater industry. International Review of Law and Economics, 27(2): 129-153. Ravid, S. A. (2003). Are they all crazy or just risk averse? Some movie puzzles and possible solutions. Economics of the Arts and Culture, 33-47. doi: 10.1108/S0573- 8555(2003)0000260006 Sibley, Brian (2002). The lord of the rings: The making of the movie trilogy. Boston, MA. Houghton Mifflin Harcourt. Terrry, N., Butler, M., & De'Armond, D. (2005). The determinants of domestic box office performance in the motion picture industry. Southwestern Economic Review, 137-148.

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APPENDIX: Figures and Tables Figure 1: Overseas Share of Worldwide Box Office Receipts 2002-2011*

*Source IMDB, share calculated using top 100 grossing films in each year. Table 1: Movie Types and Definitions

40.0%

45.0%

50.0%

55.0%

60.0%

65.0%

2001 2003 2005 2007 2009 2011

Pe

rce

nta

ge

Year

Name Definition

Original Not based on prior movie art

Parent Original movie which spawn additional movies

Franchise a group of movies relating to the same source content

Sequel

Additional movie which takes place after previous

installment in franchise

Prequel

Additional movie which takes place before previous

installment in franchise

Remake

Additional movie which presented an updated version of

the parent movie

Rebook

Additional movie which resets a franchise by updating the

parent movie and serves as new parent movie in franchise

Table 2: Remakes and Reboots from 2002-2011

Movie Title Franchise Year

Halloween: Resurrection Halloween 2002

Freddy Vs. Jason Freddy vs. Jason 2003

The Italian Job Italian Job 2003

Cheaper by the Dozen Cheaper by the Dozen 2003

Spy Kids 3D: Game Over Spy Kids 2003

Freaky Friday Freaky Friday 2003

The Texas Chainsaw Massacre Texas Chainsaw Massacre 2003

Alien Vs. Predator AVP 2004

The Grudge Grudge 2004

Exorcist: The Beginning Exorcist 2004

King Kong King Kong 2005

Fun with Dick and Jane Fun With 2005

Charlie and the Chocolate Factory Charlie and the Chocolate Factory 2005

House of Wax House of Wax 2005

War of the Worlds War of the Worlds 2005

The Fog (2005) The Fog 2005

Bad News Bears Bad News Bears 2005

The Amityville Horror (2005) Amityville Horror 2005

Wallace and Gromit: The Curse of the Were-Rabbit Wallace and Gromit 2005

The Longest Yard Longest Yard 2005

Herbie: Fully Loaded Herbie 2005

The Legend of Zorro Zorro 2005

Batman Begins Batman 2005

The Hills Have Eyes Hills Have Eyes 2006

Charlotte's Web (2006) Charlotte's Web 2006

When a Stranger Calls When a Stranger Calls 2006

Eight Below Antartica 2006

Superman Returns Superman 2006

Casino Royale James Bond 2006

The Departed Internal Affairs 2006

The Pink Panther (2006) Pink Panther 2006

Alvin and the Chipmunks Alvin and the Chipmunks 2007

Transformers Transformers 2007

TMNT Teenage Mutant Ninja Tutrtles 2007

3:10 to Yuma (2007) 3:10 to Yuma 2007

The Day the Earth Stood Still (2008) Day the Earth Stood Still 2008

Prom Night (2008) Prom Night 2008

A Christmas Carol (2009) Christmas Carol 2009

Friday the 13th (2009) Friday the 13th 2009

The Taking of Pelham 1 2 3 Taking of Pelham 1 2 3 2009

My Bloody Valentine 3-D My Bloody Valentine 2009

The Stepfather (2009) Stepfather 2009

Star Trek Star Trek 2009

Clash of the Titans (2010) Clash of the Titans 2010

The Crazies Crazies 2010

Robin Hood Robin Hood 2010

Predators Predator 2010

True Grit True Grit 2010

Death at a Funeral (2010) Death at a Funeral 2010

Alice in Wonderland (2010) Alice in Wonderland 2010

The Karate Kid The Karate Kid 2010

A Nightmare on Elm Street (2010) Nightmare on Elm Street 2010

Arthur (2011) Arthur 2011

Rise of the Planet of the Apes Planet of the Apes 2011

Footloose (2011) Footloose 2011

The Muppets Muppets 2011

The Girl with the Dragon Tattoo (2011) Dragon Tattoo 2011

Table 3: Descriptive Statistics from 2002-2011 Top 100 Movies

Variable Mean

Domestic Revenue $98,100,000

Foreign Revenue $112,000,000

Worldwide Revenue $211,000,000

Return on Investment (Worldwide) $20

Reboot/Remake 59

Sequel/Prequel 124

Table 4a: Results from OLS Estimation from Equation 1 by Revenue Type

N 816 816 816

R20.6482 0.5212 0.6585

*** Denotes 99% significance

** Denotes 95% significance

*Denotes 90% significance

(0.2044)

0.0330

(0.1165)Reboot/Remake

Woldwide Gross

(0.0039)

0.0261

(0.0079)***

0.0029

(0.2312)***

0.0079

(0.0011)***

0.0378

(0.0238)

-0.0033

Domestic Gross

0.1183

0.3463

(0.1510)**

2.7202

(0.6680)***

0.0050

(0.0026)*

(0.00634)

May

June

July

August

Results from OLS Estimation by Domestic, Overseas, and Worldwide Gross Box Office Reciepts*

Coefficients

InTheaters

lnOpening Theaters

Adult

February(.0715)

0.5579

(0.0876)***

-0.0005Time between Installment

Number in Franchise

Nominations

(0.0983)

Sequel/Prequel

Opening Weekend

Critic Review

InBudget

Overseas Gross

-0.1241

2011

0.3129

(0.0563)***

-2.1651

(0.1007)***

0.0359

(0.0188)*

-0.0239

-0.0239

(0.0339)

2005

2006

2007

2008

2009

2010

September

October

November

December

2003

2004

March

April

(0.0647)***

-0.2972

(0.0657)***

-0.2202

(0.0701)

-0.0749

(0.0674)***

-0.1537

(0.0747)

(0.0672)***

-0.3247

-0.1008

(0.0692)

-0.2013

(0.0702)***

0.1446

-0.0743

(0.0638)***

-0.1718

(0.0708)

-0.1536

-(.0664)

0.0331

-0.0151

(0.0744)***

-0.2643

(0.0648)***

-0.0893

(0.0727)

-0.2359

(0.0706)

0.0584

(0.0165)***

0.0405

(0.0115)***

2.4940

(0.0716)***

-0.0065

(0.0648)

(0.0775)*

(0.0046)***

1.4992

(0.1727)*

-0.6349

(0.2017)***

0.0313

(0.3410)***

-0.1284

(0.0501)***

0.4602

(0.0927)***

-0.4876

-0.0612

(0.0704)

-0.2296

0.4195

(0.0682)***

2.1958

(0.2747)***

0.0075

(0.0013)***

0.2035

(0.1758)

0.0999

(0.1592)**

0.0697

(0.1728)

0.1704

(0.1636)

0.1648

(0.2066)

-0.2508

(0.1904)

0.1318

(0.1656)

0.3566

(0.1777)**

-0.4798

(0.1890)***

(0.0392)***

-0.0024

(0.0046)

0.0358

(0.0087)***

0.0267

(0.1625)

0.2069

(0.1672)

(0.1687)

0.2682

(0.1469)*

0.1890

-0.3973

(0.2100)*

-0.2991

(0.1857)

-0.0432

(0.1595)

-0.1625

(0.1682)

-0.4177

(0.2201)**

-0.3113

(0.0414)

-0.0642

(0.0832)

-0.1016

(0.0856)

-0.2455

(0.0060)***

1.5232

(0.1458)***

0.0068

(0.0243)

0.0560

(0.0851)

-0.2236

(0.0799)***

-0.2963

(0.0952)***

-0.1972

(0.0863)***

0.2382

(0.0934)***

0.0748

(0.0843)

0.0017

(0.0781)

0.1136

(0.0801)

-0.0590

(0.0816)

-0.0432

(0.0813)***

-0.0043

(0.0864)

-0.1254

(0.0914)

0.0372

(0.0801)

-0.0154

(0.0764)

-0.0770

(0.0807)

(0.0770)

0.0360

(0.0871)

-0.0048

(0.0826)

0.0462

Table 4b: Results from OLS Estimation of Equation 2 by Revenue Type

N 816 816 816

R20.6446 0.4182 0.4992

*** Denotes 99% significance

** Denotes 95% significance

*Denotes 90% significance

(0.2820)

0.1395

(0.1417)Reboot/Remake

Woldwide Gross

(0.0038)

0.0126

(0.0106)***

0.0225

(0.2360)***

0.0081

(0.0011)***

0.0349

(0.0244)

-0.0019

Domestic Gross

0.0950

0.0912

(0.0521)*

---

---

0.0089

(0.0026)***

(0.0072)

May

June

July

August

Results from OLS Estimation by Domestic, Overseas, and Worldwide Gross Box Office Reciepts*

Coefficients

InTheaters

lnOpening Theaters

Adult

February(.0705)

0.8078

(0.1108)***

0.0039Time between Installment

Number in Franchise

Nominations

(.1038)

Sequel/Prequel

Opening Weekend

Critic Review

InBudget

Overseas Gross

-0.0159

2011

0.0605

(0.0169)***

-2.0579

(0.1031)***

0.0286

(0.0192)

-0.0148

-0.0102

(0.0342)

2005

2006

2007

2008

2009

2010

September

October

November

December

2003

2004

March

April

(0.0663)***

-0.2951

(0.0666)***

-0.2237

(0.0723)

-0.0743

(0.0698)**

-0.1714

(0.0765)

(0.0661)***

-0.3180

-0.1023

(0.0684)

-0.2018

(0.0689)***

0.1449

-0.0734

(0.0663)**

-0.1668

(0.0723)**

-0.1664

(0.0675)

0.0567

-0.0029

(0.0752)***

-0.2502

(0.0666)***

-0.0941

(0.0732)

-0.2133

(0.0703)

0.0335

(0.0343)

0.0503

(0.0113)***

---

(0.0727)***

0.0123

(0.0659)

(0.0793)**

(0.0046)***

1.5703

(0.1983)

-0.5090

(0.2292)**

0.3033

---

---

---

0.1939

(0.0967)**

-0.3817

-0.0427

(0.0709)

-0.2039

0.1002

(0.0268)***

---

---

0.0099

(0.0014)***

0.3999

(0.1997)*

0.2923

(0.1783)***

0.1342

(0.1910)

0.3437

(0.1790)*

0.3581

(0.2285)

0.0303

(0.1907)

0.2479

(0.1729)

0.4729

(0.1899)**

-0.2933

(0.2378)***

(0.0547)***

0.0008

(0.0045)

0.0065

(0.0166)

0.0249

(0.1813)***

0.5169

(0.1831)***

(0.1879)

0.5352

(0.1892)***

0.4669

-0.1989

(0.2322)*

-0.1225

(0.2175)

0.2246

(0.1851)

0.0663

(0.1938)

-0.1512

(0.2467)

-0.2307

(0.0468)***

0.0048

(0.1034)

-0.0608

(0.1075)

-0.1753

(0.0064)***

---

---

---

---

-0.1379

(0.1123)

-0.0299

(0.0973)

-0.1694

(.1234)

-0.6440

(0.1081)

0.4188

(0.1255)***

0.2487

(0.1109)**

0.1453

(0.0859)

0.1833

(0.0943)*

-0.0375

(0.0964)

0.0729

(0.1083)

0.0784

(0.1111)

0.0274

(0.1097)

0.1247

(0.1118)*

0.1636

(0.0876)*

0.1326

(0.0919)

(0.0887)

0.1468

(0.1050)

0.0972

(0.0954)

0.2046

Table 5: OLS Results from Alternative Specification of Model by Revenue Type

N 816 816 816

R20.6482 0.5212 0.6585

*** Denotes 99% significance

** Denotes 95% significance

*Denotes 90% significance

Results from OLS Estimation by Domestic, Overseas, and Worldwide Gross Box Office Reciepts*

Coefficients Domestic Gross Overseas Gross Woldwide Gross

Reboot/Remake0.2238 0.3102 0.2867

(0.0839)*** (0.1832)* (0.1063)***

0.1977 0.3815 0.3167

(0.0289)*** (0.0634)*** (0.0395)***Number in Series

-2.1072 --- ---

(0.2323)*** --- ---Opening Weekend

-0.0089 -0.0162 -0.0144

(0.0016)*** (0.0035)*** (0.0024)***Number in Series2

0.0390 0.8009 0.3953

(0.0244)* (0.1107)*** (0.0547)***InBudget

0.0081 0.0091 0.0101296

(0.0011)*** (0.0026)*** (0.0014)***Critic Review

0.0226 0.0515 0.0260

(0.0048)*** (0.0115)*** (0.0065)***Nominations

-0.0051 -0.0043 -0.0035

(0.0037) (0.0061) (0.0043)Time between Installment

0.0321 --- ---

(0.0190)* --- ---lnOpening Theaters

1.5309 --- ---

(0.1021)*** --- ---InTheaters

-0.0147 -0.3953 -0.0077

(0.0726)* (0.2478) (0.1061)February

-0.0245 0.1795 -0.1531

(0.0345) (0.0965)* (0.0466)***Adult

-0.2045 -0.5292 -0.1794

(0.0712)*** (0.2292)** (0.10780)*April

-0.0968 -0.2213 -0.0512

(0.0705) (0.1994) (0.1098)March

0.0520 0.2111 0.2385

(0.0712) (0.1851) (0.1119)**June

0.1549 0.2310 0.3630

(0.0789)** (0.2162) (0.1218)***May

-0.2120 -0.1763 -0.0507

(0.0678)*** (0.1892) (0.0982)August

-0.0492 0.0464 0.1352

(0.0721) (0.1929) (0.1128)July

-0.2719 -0.2661 -0.1072

(0.0678)*** (0.2298) (0.1082)October

-0.3105 -0.2872 -0.1629

(0.0753)*** (0.2373) (0.1234)September

-0.2286 0.0042 0.0113

(0.0737)*** (0.1916) (0.1111)December

-0.0610 -0.0748 0.1228

(0.0738) (0.2295) (0.1120)November

-0.0114 0.4624 0.1691

(0.0669) (0.1782) (0.0935)**2004

0.0007 0.2306 0.1039

(0.0653) (0.1728) (0.0847)2003

-0.1843 0.3124 0.0368

(0.0658)*** (0.1773) (0.0858)2006

-0.1787 0.1169 -0.0559

(0.0712)*** (0.1900) (0.0946)2005

-0.1591 0.3125 0.1075

(0.0682)** (0.1865) (0.0916)2008

-0.0959 0.3080 0.1046

(0.0751) (0.1993) (0.1032)2007

2011

-0.2482 0.4081 0.1139

(0.0658)*** (0.1811)** (0.0865)2010

-0.0983 0.4868 0.1552

(0.0723) (0.1871)*** (0.1108)2009

-0.3159

(0.0655)***

0.4669

(0.1827)***

0.0881

(0.9102)

Table 6: Coefficient of Variation by Revenue Type

Appendix B: Variable Description

Equation 1

Equation 2

s

LnDomesticGross is the dependent variable. This variable is logged to measure percentage increases or decreases in box office revenue for domestic releases only , inflation adjusted.

LnForeignGross is the dependent variable. This variable is logged to measure

percentage increases or decreases in box office revenue for foreign releases only, inflation adjusted.

Movie Type CV- Domestic ROI CV- Overseas ROI CV- Worldwide ROI

Reboot 156.25 144.83 77.16

Sequel/Prequel 218.55 184.79 157.24

Remake 123.37 416.31 81.19

Original 1778.19 1803.99 1744.97

LnWorldWideGross is the dependent variable. This variable is logged to measure percentage increases or decreases in box office revenue for worldwide (domestic + foreign) releases, inflation adjusted.

RebootRemake is our variable of interest. It is a dummy variable measuring whether

the movie was rebooted and remade or not. We expect this to have a positive correlation given the amount of recognizable franchises rebooted over the last few years.

Sequelprequel is a dummy variable that combines both sequels and prequels. We

expect this to be a positive correlation for the same reason as reboots. Movies are producing sequels and every few years to retain their current audience and attract more fans.

PercentOpen measures the percentage of the opening weekend revenue to the overall

revenue. This is likely to have a negative correlation given that most movies make the majority of their revenues on the opening weekend. After that opening weekend revenues start to drop off. This is domestic data only, however since data is unavailable for opening weekend success for foreign movies, remains in the model to serve as a proxy.

Adult is a dummy variable indicating whether the movie is rated R or not. We expect

this number to have a negative correlation because R-rated movies have a limited audience and can negatively impact revenue.

lnBudget is the logged version of the production budget used for the given movie,

inflation adjusted. This is likely to have a positive correlation because it is more likely to receive higher revenues if the movie spends more on actors, directors, special effects, etc. It is important to note this does not include marketing budgets.

CriticReview is a critical review of the movie. The critic review is based off of a

“metascore,” this is pooled major critic reviews such as the Wall Street Journal, Los Angeles Times, New York Times. The score is based off a scale from 1-100.

TimeBetweenInstallment is dependent upon whether the movie is a reboot sequel or a

prequel. This variable measures how much time there was in between the most recent film that was released in that franchise. If the movie was an original subsequently it did not have a movie released beforehand, it received a zero.

NumberInFranchise indicates what number movie it was in that franchise. If the movie

was an original the number received was a one. Nominations indicate how many Academy Award, Golden Globes, and Screen Actors

Guild Award nominations the movie received. This tries to capture “star power” within a movie. It is hard to measure A-level actors or directors and this attempts to measure that by accounting for strong performances.

HolidayWeek is a dummy variable signifying if the movie was released during a holiday week, the holidays included are: New Year’s, Martin Luther King Day, Valentine’s Day, Easter, Memorial Day, Independence Day, Columbus Day, Halloween, Veteran’s Day, President’s Day and Christmas.

LnTheaters measures the percentage change of theaters that movie played in. This is

domestic data only; however since data is unavailable for the amount of theaters played for foreign movies, remains in the model to serve as a proxy.

LnOpenTheaters measures the percentage change of theaters that movie played in for

its opening weekend. This is domestic data only, however since data is unavailable for the amount of opening theaters played for foreign movies, remains in the model to serve as a proxy.

xiMonth is a dummy variable that controls for all months of the year. The results are compared against January. The purpose is to show consumer tastes throughout the year (for example a summer time movie or winter movie).

xiYear is a dummy variable that controls for all the years in our sample. The results are

compared against 2002. The purpose of this variable is to control for the states of the economy and also the quality of movies that were released that year.