The Hollywood Stock Exchange: Efficiency and The Power of Twitter

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1 The Hollywood Stock Exchange: Efficiency and The Power of Twitter by Nathaniel Harley A special thanks to Professor Richard Walker for advising on this thesis. Also, thanks to Professor Joseph Ferrie, Sarah Ferrer, and the MMSS department. 1

2 Abstract Online prediction markets are becoming increasingly popular and useful for forecasting real world events. The Hollywood Stock Exchange is one of the most successful online prediction markets and forecasts real world box-office returns. This thesis sets forth to answer questions about whether The Hollywood Stock Exchange is an efficient market, and if it is not, what factors can be used to predict future changes in MovieStock prices? Most importantly, this thesis will focus in on the usefulness of social media specifically Twitter in predicting future changes to these prices. Introduction Markets are a place where individuals can exchange items. Prices are used to assign these items values so that buyers and sellers can easily trade them. Embedded in these prices is a large amount of information that reflects the collective opinion of informed and uninformed traders. The two main types of markets are financial markets and prediction markets. We are all familiar with financial markets, such as stock markets, bond markets, futures markets, commodity markets, currency markets, and money markets. Depending on the type of market, the price of an asset can represent different meanings. On a stock market, such as the New York Stock Exchange, the price of common stock represents how much an individual is willing to pay for one share of a specific company. On a futures market, the price represents a forecast of what the underlying asset will cost in the future. For prediction markets, the price of an asset is used to indicate the likelihood of an event occurring. Prediction markets are slowly becoming more popular and are being used as an informational resource to predict events. Some prediction markets, such as Intrade Prediction Market, forecast the likelihood of political events. Others, such as The Hollywood Stock Exchange, trade prediction shares of movies, actors, and other 2

3 film- related options. As more and more prediction markets expand onto the electronic platform, individuals have more access to trade on these markets. The question driving my thesis is if The Hollywood Stock Exchange is not an efficient market, what information can people use to predict future changes in stock prices? Recently, a lot of work has been done to try and capture social media data, such as twitter, and use it as a measurement to make quantitative predictions. Using Twitter data, along with other non- social media variables, I attempt to test whether MovieStock prices can be predicted by Twitter information. Prediction Markets There are many barriers that exist for establishing a new market, such as high costs, government regulation, and the threat of lawsuits; however, artificial online prediction markets do not have these barriers. Web market games are increasingly easy to create because they have small operating costs for setup, maintenance, advertising, searching, and transacting, and benefit from a global group of Internet users. They do not need to get permission from government officials and do not need to create strict rules that would limit trading because there is little risk of lawsuits against them. Users can remain anonymous and record keeping does not need to be as tight. As a result, online markets, such as The Hollywood Stock Exchange, can exist and function effectively [1]. However, as Justin Wolfers and Eric Zitzewitz illustrate in their paper Five Open Questions About Prediction Markets, there are five open questions that must be answered in order for prediction markets to fulfill their potential and ultimately succeed. 3

4 The first question Wolfers and Zitzewitz pose is how to attract uninformed traders? (Wolfers and Zitzewitz, p. 2). Uninformed traders are important to any market place because they create an uninformed order flow, which actually attracts informed profit motivated groups to trade. In order to attract these traders, it is essential to have low transaction costs as well as interest or buzz surrounding a new predictive market. The Hollywood Stock Exchange has successfully attracted both uninformed and informed traders by creating an attractive, easy to use platform that has positioned itself as the premier box- office forecasting market available. Their second question is how to tradeoff interest and contractibility? (Wolfers and Zitzewitz, p. 3). Wolfers and Zitzewitz conclude that it is important to establish clear guidelines outlining contracts traded on prediction exchanges, but that there is a lot of leeway in doing so. The Hollywood Stock Exchange does not face some of the complexities that other prediction markets encounter. For example, box- office revenue and which actor won the Oscar for best actor leaves little room for interpretation. The third question they pose is how to limit manipulation? (Wolfers and Zitzewitz, p. 3). This question addresses two types of manipulation: First, manipulation of the outcomes on which the prediction markets are based and, second, manipulation of the market prices themselves. Theoretically, traders could go out and buy a mass amount of tickets for a specific movie in order to increase box- office revenue, but practically, they would never do so because it would have little impact on national box- office revenue and the cost of buying movie tickets greatly out ways the potential profit since the exchange uses Hollywood Dollars and 4

5 not actual money. In terms of price manipulation, buying a mass amount of a specific asset would have no effect on the real world outcome, so it would be pointless. The fourth question Wolfers and Zitzewitz ask is are markets well calibrated on small probabilities? (Wolfers and Zitzewitz, p. 3). They conclude that declining transaction costs and carefully framed contracts will produce more accurate responses from traders who are inherently bad at distinguishing small probabilities and overvalue unlikely events. Although traders on The Hollywood Stock Exchange are subject to this bias, it is important to realize that this behavior exists not only in prediction markets, but also in real- world markets. The final question Wolfers and Zitzewitz pose is how to separate correlation from causation? (Wolfers and Zitzewitz, p. 3). The assets traded on The Hollywood Stock Exchange, such as MovieStocks, are directly correlated with real world events and the outcome of one event, in the movie world, does not cause the probability of another event to change [2]. Therefore, there is not the problem of determining correlation versus causation. By examining these five open questions proposed by Wolfers and Zitzewitz, it is clear that The Hollywood Stock Exchange has all the elements to make it a successful prediction market and function effectively. However, this does not guarantee that The Hollywood Stock Exchange is an efficient market. Efficient Markets If markets are, in fact, efficient, the market asset price is the best estimate of value; however if markets are not efficient, the market price may deviate from the 5

6 true value. That being said, market efficiency does not require that the market price be equal to true value at every point in time, but that if there is a deviation from the true value, that the deviation is random. There are three main types of efficient markets: Weak Form, Semi- Strong Form, and Strong Form [3]. o Weak Form Future changes in prices are not predictable based on information contained in all past prices suggesting that analysis of past prices alone would not be helpful in determining undervalued or overvalued assets. o Semi-Strong Form Future changes in prices are not predictable based on past prices or any currently available public information (including prices, economic variables, etc.). o Strong Form Future changes in prices fully reflect all information available, public and private. Informed experts cannot consistently outperform uninformed traders. The Hollywood Stock Exchange The Hollywood Stock Exchange is a online artificial prediction market game. Participants can buy and sell virtual shares of celebrities and movies with a currency called the Hollywood Dollar (H$). New users can join for free, and when they do, they receive 2 million H$. They can trade various assets such as MovieStocks, StarBonds, TVStocks, MovieFunds, and Deriviatives. The Hollywood Stock Exchange then syndicates the data collected from the Exchange and sells it as 6

7 market research to entertainment companies, consumer product companies and financial institutions. For the purpose of this thesis, I will focus on MovieStocks and whether it is possible to predict future changes in MovieStock prices. MovieStocks represent films both still in production and currently in theaters. The price of the MovieStock reflects how much money traders think the film will make with each $1 million earned domestically equal to $1 Hollywood Dollar. The price of a MovieStock is adjusted to reflect its exact earnings in the box- office. The price begins to adjust after the movie s opening weekend in order to bring the expected box- office gross revenue in line with the actual box- office gross revenue. For example, if Movie A grosses $20 million its first week in theaters, then the price after the first week would be something like H$45 on the exchange. However, if Movie A only grossed $3 million in the second week, then the price of Movie A would most likely drop drastically to something like H$28. On average, a film makes 2.7 times its opening weekend box- office during its first four weeks of wide release. MovieStocks delist and cash out from the market on the first business day after its fourth weekend of wide release or 12 weeks of limited release. The driving question behind this Thesis is whether or not The Hollywood Stock Exchange is an efficient market, specifically looking at MovieStocks; and if it is not, what factors can be used to predict future changes in MovieStocks? 7

8 Twitter Social media is quickly changing the social landscape because it is easy to use and reaches a global audience extremely quickly. As a result, social media is setting trends in topics that range across the board from politics to technology to the entertainment industry. Social media can be a very powerful tool and the question becomes whether it is possible to aggregate social media and use it as a measurement to gauge collective opinion. We all know Twitter. Twitter is essentially a real- time information network that connects you to the latest stories, ideas, opinions and news. Tweets are only 140 words, but they can be very powerful. Twitter uses the # symbol, called a "hash- tag", to mark keywords or topics in a Tweet. Interestingly enough, twitter users created it organically as a way to categorize messages. People use the hash- tag symbol before relevant keywords or phrases in their Tweet to categorize those Tweets and help them show up more easily in Twitter Search. Hash- tagged words that become very popular are often referred to as trending topics. Twitter as a result has a lot of power because it can identify important topics and also, the sentiment surrounding those topics. For example, if a keyword is being used a lot we can come to the conclusion that many people find it important. Looking further, analyzing the individual tweets can help us identify whether people feel positively or negatively. Using this, we can create a measurement of collective opinion and use it to make quantitative predictions. Many people have already started to use Twitter to build forecasting models. Specifically, they look at the sentiment of a Tweet and use it to gauge collective 8

9 opinion. For example, Duncan Watts, an Internet researcher who heads one of Yahoo! s research labs in New York, uses Twitter to forecast video- game and music sales. He found that adding Twitter data greatly increased the accuracy of his forecasting model. Similarly, Derwent Capital Markets, a hedge fund based in London, implements a Twitter model to help guide their investments [4]. Related Studies When looking at previous studies, I came across a few that were very influential in shaping how I tested The Hollywood Stock Exchange for efficiency. In The Power of Play: Efficiency and Forecast Accuracy in Web Market Games by David M. Pennock et al. they analyzed the efficiency and forecast accuracy of two market games: The Hollywood Stock Exchange and the Foresight Exchange. For the purpose of this thesis, I will focus on their results regarding The Hollywood Stock Exchange. In their paper, they focused on the question of whether or not efficiency breaks down in artificial markets when there is no monetary incentive. The goal of their research was to test whether The Hollywood Stock Exchange holds for two types of market efficiency: internal coherence and strong form. They presented evidence that some market simulations can act sufficiently well as both aggregators and disseminators of information. In conclusion, they found that The Hollywood Stock Exchange MovieStock prices were good indicators of what movies will do well in the box- office. First, it is important to understand what internal coherence is. Internal coherence is defined as when prices are self- consistent or arbitrage- free: no trader 9

10 can make a sure profit without any risk. In efficient markets, arbitrage should not exist. For example, arbitrage exists when you can buy a security on one exchange, such as The New York Stock Exchange, for a certain price and then sell the same security on the Tokyo Exchange for a higher price. The security should have the same price on both exchanges. Another example can be shown in relation to the securities market. Take for instance a security that pays $1 if and only if it rains tomorrow. If another security existed that pays $1 if an only if it does not rain tomorrow, then owning both securities would guarantee a $1 payoff. In order for there not to be arbitrage opportunity, the price to buy both securities should always be greater than $1 and the price to sell both securities should always be less than $1. One of the driving questions behind their study was: do HSX players have utility for Hollywood dollars and, if so, are their resulting incentives strong enough to maintain internal price consistency in the game? (Pennock, p. 7). In order to determine the degree of internal coherence in MovieStocks, they tested how closely MovieStocks and options prices conformed to the put- call parity. In conducting their experiment, they used weekend halt prices (the price before the movie adjusts to approximately 2.7 times the opening weekend box- office proceeds) for 75 MovieStocks and their corresponding options during the period of March 3, 2000 to September 1, They found that the relationship between the stock estimates of weekend box- office returns versus the option estimates adhered relatively closely to the put- call parity at the halt price. They then wanted to test whether prices adhered to the put- call parity at all times, not just at the halt price. Their results indicated that The Hollywood Stock Exchange market was not completely free of 10

11 arbitrage because prices diverged at times from parity by as much as H$6.5. When examining whether the market showed signs of internal coherence, they concluded that it did because when prices were too high, they were much more likely to correct and go down on subsequent days. Similarly, when they were too low, they were more likely to increase. They hypothesized that this price self- correction could be attributed to traders taking advantage of arbitrage opportunities. Pennock et al. wanted to test the forecast accuracy of The Hollywood Stock Exchange and whether MovieStocks were good predictors of box- office returns. In order to understand their process and results, it is important to understand Rational Expectations Theory: prices are not only coherent, but also reflect the sum total of all information available to all market participants. Essentially, the Rational Expectations Theory states that even when some individuals have insider information, prices equilibrate as if everyone has access to all the same information. They wanted to go further and test where strong form efficiency holds in The Hollywood Stock Exchange because internal coherence is only a minimal standard of market efficiency, where as stronger forms of efficiency imply market competence as well and coherence: prices actually reflect an aggregation of information distributed among the participants, and market forecasts are as accurate as expert assessments (Pennock, p. 11). Ultimately, they proposed that if The Hollywood Stock Exchange holds for strong form efficiency, then the implications would be more relevant to the societal benefit in the form of cheap and reliable forecasts (Pennock, p. 11). In order to test strong form efficiency, they assessed the forecast accuracy of The Hollywood Stock Exchange stock. Pennock, et al, quantified and 11

12 compared MovieStock prices (The Hollywood Stock Exchange prediction) to Brandon Gray s published forecasts at Box- office Mojo for 50 movies appearing on both sources. Their results showed that there was a slight bias to underprice the best performing movies and overprice the worst performing movies. They attributed this bias to a manifestation of risk- seeking behavior where traders preferred potential sleepers with the opportunity for a very large payoff, rather than known quantities with a moderate payoff. They also found a correlation between MovieStock estimates and Box- office Mojo estimates, which they hypothesized resulted from the possibility that Box- office Mojo observes Hollywood Stock Exchange prices, and/or some Hollywood Stock Exchange traders read Box- office Mojo forecasts. Ultimately, they concluded that The Hollywood Stock Exchange showed signs of efficiency, in the form of price coherence and forecast accuracy. They deduced that The Hollywood Stock Exchange is a good forecast for box- office returns and provides a reasonable likelihood assessment of uncertain events (the final four week box- office returns). The implications that Pennock et al. derived from their study were that existing artificial markets, like The Hollywood Stock Exchange, could be a valuable resource for information. Also, The Hollywood Stock Exchange provides a good example for a successful artificial market and should promote the creation of similar markets in the future [1]. In Sitaram Asur and Bernardo A. Huberman s, Predicting the Future with Social Media, they demonstrated how social media content could be used to predict real- world outcomes. In particular, they used the chatter from Twitter.com to 12

13 forecast box- office revenues for movies. They showed how a simple model built from the rate at which tweets were created about particular topics could outperform market- based predictors. Furthermore, they demonstrated how sentiments extracted from Twitter could be further utilized to improve the forecasting power of social media. Social media has the ability to aggregate opinions and act as a form of collective wisdom that can be used to make quantitative predictions that outperform those of artificial markets (Asur & Huberman, p. 1). Their goal was to assess how buzz and attention was created for different movies and how it changed over time. Also, they focused on the mechanism of viral marketing and pre- release hype on Twitter, and the role that attention played in forecasting real- world box- office performance. They also focused on how sentiments were created and how positive and negative opinions influenced people. Their hypothesis was that movies that are well talked about will be well- watched (Asur & Huberman, p. 1). Asur and Huberman wanted to look at how attention and popularity were generated for movies on Twitter, and what affects this had on box- office performance for the movies considered. Their results indicated that movies that had greater publicity, in terms of linked urls on Twitter, did not necessarily perform better in the box- office. Their initial analysis of tweet rates (defined as tweets for a movie per hour) showed a positive correlation. When they compared their results to The Hollywood Stock Exchange index, they found that their model outperformed The Hollywood Stock Exchange based model in predicting actual box- office returns. 13

14 They then tested whether they could predict the price of The Hollywood Stock Exchange MovieStock at the end of the opening weekend for the movies they considered. In order to do so, they used historical Hollywood Stock Exchange prices as well as the tweet- rates for the week prior to the release as predictive variables. They created a simple time series regression of tweet- rates, over 7 days before the weekend, to predict the box- office revenue for a particular weekend. Again, they found that their tweet- rate model was better at predicting the actual values than the historical Hollywood Stock Exchange prices. Their results showed how twitter could be used as an accurate indicator of future outcomes. Asur and Huberman also wanted to see if the sentiment of Tweets could increase forecasting accuracy. They sectioned off tweets into Positive, Negative, and Neutral. Their results indicated that tweets after the release had more value than tweets before as coincided with their expectations that people would hold more weight to a tweet after they had seen the movie. They also found that there were more positive sentiments than negative for all most all of the movies. They concluded that adding Twitter sentiment to the equation did not significantly increase the predictive power of tweets themselves. In conclusion, they found that social media feeds could be an effective indicator of real- world performance. Specifically, the rate at which movie Tweets were generated could be used to build a powerful model for predicting movie box- office revenue. They showed how their predictions were more accurate than The Hollywood Stock Exchange prices. Finally, the sentiment of tweets could improve 14

15 box- office revenue predictions based on tweet rates, but only after the movies were released [5]. In Twitter Mood Predicts the Stock Market by Johan Bollen et al., they looked at the question of whether societies can experience mood states that affect their collective decision- making and by extension whether the public mood was correlated or even predictive of economic indicators. They investigated whether measurements of collective mood states obtained from twitter feeds were correlated to the value of the Dow Jones Industrial Average over time. In their study they analyzed the text in Tweets using two mood- tracking tools, OpinionFinder (measures positive vs. negative mood) and Google- Profile of Mood States (measures mood in terms of 6 dimensions: Calm, Alert, Sure, Vital, Kind, and Happy). Their results found that changes in the public mood state could be tracked from the content of large- scale Twitter feeds by text processing techniques and that such changes respond to a variety of socio- cultural drivers in a highly differentiated manner (Bollen, p. 7). Also, they found that the inclusion of specific public mood dimensions, but not others could significantly improve the accuracy of Dow Jones Industrial Average predictions. They found that the calmness of the public was predictive of the Dow Jones Industrial Average rather than general levels of positive sentiment as measured by OpinionFinder [6]. These three studies helped shape how I wanted to form my own study of MovieStock prices in relation with Twitter. 15

16 Data Summary Before collecting my movie data, I needed to establish consistent criteria so that all movies in the data set would share similar properties. First I collected data for the top 2-3 grossing movies opened in wide release (so that every movie in my data set would delist after four weeks) for each week over the time period of September 2011 to December This gave me a data set of 26 movies. For every movie, I collected the release date MovieStock price and then the price at the end of each week, up until the delist date. This gave me five data points for each movie. Ultimately, I only used the end of week stock price for my regression analysis and dropped the release date stock price [12]. In order to capture Twitter data, I used a program called Hootsuite. First, I tracked how many times a movie title was mention as a keyword on Twitter over # Weekly Twi+er Hits (Thousands) Week the four- week period. The keyword analysis performed by Hootsuite gave me daily Twitter hits for each keyword. In order to make my Twitter data line up with the 16

17 MovieStock price data, I summed the number of daily Twitter hits and calculated weekly Twitter hit totals for each week. This gave me four data points for each movie: Week 1 twitter total, Week 2 twitter total, Week 3 twitter total, and Week 4 twitter total [13]. I then calculated the log of the number of weekly twitter hits in order to analyze the percent change from week to week. For the regression analysis, I wanted to 14 Log # Weekly Twi+er Hits Week determine whether a change in Twitter hits was more predictive than the total number of Twitter hits. This also would allow me to control for movies that were more popular due to external factors, such as a higher budget or more proactive advertising, and as a result, generated more discussion on Twitter [13]. In order to capture the sentiment of each Tweet, I used Hootsuite s twitter sentiment analytics, which capture the conversational tone of my keyword search. I was able to analyze Tweet sentiment for each week, which gave me a total of four data points. Hootsuite analyzed the data by breaking it out into eight different categories based on the sentiment of the tweet: affection friendliness, enjoyment 17

18 elation, amusement excitement, contentment gratitude, sadness grief, anger loathing, fear uneasiness, and finally, humiliation and shame. The analysis gave me a percentage break down of the weekly Tweets for each category [13]. I considered affection friendliness, enjoyment elation, amusement excitement, and contentment gratitude as a positive Twitter sentiment, and sadness grief, anger loathing, fear uneasiness, and finally, humiliation and shame as a negative Twitter sentiment. I aggregated the collective positive Tweet sentiments on a weekly basis in order to capture the percent of Twitter hits that were positive. This gave me again, four data points for each movie [13]. % Twi+er Hits Posi<ve Week I then calculated the log of the percent of Twitter hits that were positive in order to capture the percent change from week to week. For the regression analysis, 18

19 I wanted to determine whether a change in Twitter sentiment was more predictive than total sentiment [13]. 5 Log % Twi+er Hits Posi<ve Week I then used BoxOfficeMojo.com, a movie web site with the most comprehensive box- office database, to capture the weekly box- office returns for each movie. Also, I captured the weekly number of theaters the movie was released in. I then calculated the log of both weekly box- office revenue and weekly number of theaters in order to capture the percent change from week to week [14]. Finally, I used Rottentomatoes.com a website devoted to reviews, information, and news of films, widely know as a film review aggregator to incorporate user ratings. I used a dummy variable (1 or 0) to indicate whether the movie had received a positive rotten tomatoes rating or negative one (rotten) [15]. 19

20 Graphs Before creating my regression equation, I graphed the relationship between MovieStock prices and a few key variables. I used the log of MovieStock price because I wanted to focus on the percent change from week to week. Looking at the relationship between the logstockprice and log#weeklytwitterhits, there appeared to be some positive correlation with a few outliers Correla<on Bewtween Change in Stock Price and Change in # Twi+er Hits Log Stock Price Log # Weekly Twitter Hits 20

21 The relationship between logstockprice and log%twitterhitspositive did appear to have a clear correlation and appeared to be random at first glance Correla<on Bewtween Change in Stock Price and Change in % Twi+er Hits Posi<ve Log Stock Price Log % Twitter Hits Pos. 21

22 Initially, it looked like there was a clear correlation between logstockprice and logweeklyboxoffice. This would be expected given that the MovieStock price is a forecast of actual box- office revenue. Correla<on Bewtween Change in Stock Price and Change in Weekly Box Office Revenue Log Stock Price Log Weekly Box Of;ice Revenue 22

23 Finally, I looked at the relationship between logstockprice and logweeklytheaters. Based on the relationship pictured in the graph, it was hard to conclude that there was a strong positive correlation. I would expect that a positive change in the number of theaters a movie was released in would be positively correlated with the MovieStock price because if they were increasing the number of theaters it most likely indicates that people were going to see the movie. Correla<on Bewtween Change in Stock Price and Change in Weekly # Theaters Log Stock Price Log Weekly Theaters 23

24 Regression Equation Using the data described above, I was able to create a comprehensive panel data set. It was important to create a panel data as opposed to a normal linear regression because not only did I want to see how each weekly change in MovieStock price was effected by the corresponding week data, but also, I wanted to incorporate time series variables to test whether previous weeks had an effect on the current week. As a result, I was able to create a regression equation that tested whether or not the change in MovieStock price could be determined by specific independent variables. logstockprice = logstockpricex-1 + logstockpricex-2 + ReleaseDateTwitterHits + WeekTwitterHits + WeekTwitterHitsx-1 + WeekTwitterHitsx-2 + logweektwitterhits + logweektwitterhitsx-1 + logweektwitterhitsx-2 + TwitterHitsPositive + TwitterHitsPositivex-1 + TwitterHitsPositivex-2 + logtwitterhitspositive + logtwitterhitspositivex-1 + logtwitterhitspositivex-2 + logweekboxoffice + logweekboxofficex-1 + logweekboxofficex-2+ WeekTheater + WeekTheaterx-1 + WeekTheaterx-2 + logweektheater + logweektheaterx-1 + logweektheaterx-2 + RottenTomatoes Results and Discussion logstockprice Coef. Std. Err. z P> z [95% Conf. Interval] logstockpricex logstockpricex ReleaseDateTwitterHits WeekTwitterHits WeekTwitterHitsx WeekTwitterHitsx logweektwitterhits logweektwitterhitsx

25 logweektwitterhitsx TwitterHitsPositive TwitterHitsPositivex TwitterHitsPositivex logtwitterhitspositive logtwitterhitspositivex logtwitterhitspositivex logweekboxoffice logweekboxofficex LogWeekBoxOfficex WeekTheater WeekTheaterx WeekTheaterx logweektheater logweektheaterx logweektheaterx RottenTomatoes Constant R 2 Within Between Overall Value The variables highlighted in green are all significant at a critical value greater than or equal to 1.96 indicating a 90% confidence level. The variables highlighted in yellow are all significant at a critical value greater than or equal to indicating a 90% confidence level. The R- squared between is equal to.9994 which is very high; however, the R- squared within, which is the R- squared for a fixed- effect regression is much lower. Since I used a random- effects model, the R- squared 25

26 between is the significant number. One reason the R- squared is so high, could be due to the fact that there are a lot of independent variables in the regression. The most significant variables are LogStockPrice lagged one week, Release Date Twitter Hits, Log Week Twitter Hits, Log Week Twitter Hits lagged one week, and % Weekly Twitter Hits Positive. If The Hollywood Stock Exchange was a completely efficient market, then past prices would have no correlation with current prices; however, this is not the case: the previous week change in MovieStock price has a direct correlation with the current week change in MovieStock price. We would also expect that a positive change in Twitter hits would indicate a positive change in MovieStock prices. It is interesting that a change in twitter hits one- week prior also indicates a positive change in MovieStock stock price. This relationship suggests a momentum effect: if a movie generates a lot of buzz on Twitter, more people will go to see it and talk about it. In terms of percent change of the percent of the weekly Twitter hits that are positive, we would also expect for this to have a direct correlation to an increase in MovieStock price. If individuals are feeling positive about the movie, and the collective opinion is increasingly more positive, then people will recommend the movie, and more people will go to see it. The most interesting finding however is that release date Twitter hits are inversely correlated. We would expect the opposite, especially since the change in weekly Twitter hits is positively correlated. One possible explanation could be that people who Tweet on the day a movie is released are complaining about the movie and giving it bad reviews. The coefficient is so small though, that it almost seems negligible even though the variable is considered significant. 26

27 Conclusion and Looking Further Overall, the results show that Twitter can provide some indication of when the MovieStock price will increase or decrease; however, it is hard to determine exactly how accurate this relationship is. It would also appear that The Hollywood Stock Exchange is not a completely efficient market despite successfully operating as an online market game. Traders could use information from Twitter to help them predict how MovieStocks will perform in the future and potentially exploit this information to make excess returns. Ideally, I would have liked to capture data for more movies in order to a get a more comprehensive data set. Also, due to a lack of resources, the collection of twitter data could have been more comprehensive and I would not have solely relied on Hootsuite as my main form of collection. It is questionable how accurate Hootsuite s method of capturing the number of keywords was. Also, in Hootsuite s Twitter sentiment analysis, there are some problems in how they assigned the different categories. For example, the movie Killer Elite had an extremely high percentage in the fear uneasiness category. This was probably due to the fact that they assigned the word Killer in the movie title to sentiments of fear. In order to get a more comprehensive and accurate data set, every individual tweet would need to be analyzed, but clearly this process is too arduous for one person. When thinking about the effects of twitter other questions arise. Is there a threshold effect for movies meaning that after a certain amount of chatter on 27

28 twitter, does the power of twitter become less significant? Also, how important are the number of Tweets and the change in number of Tweets prior to the release of the Movie. It would be interesting to try and predict how well a movie would do in the box- office for opening weekend. I would also have liked to break down the twitter and box- office returns based on geographical regions in the US to see if certain geographic regions have more predictive power than others. For example, if more people in LA are talking about a movie on Twitter, does that have implications on how well the movie performs just in LA or because LA is a central city in the movie industry, does it have implications about national box- office revenue. Also, it would be interesting to compare different major cities, such as LA, New York, and Chicago, to test whether one city had more influence and predictive power than another. In conclusion, it does appear that Twitter has some effect on MovieStock prices and in turn, some predictive power in determining real world box- office returns; however, it is unclear to what extent. In order to predict future changes in MovieStock price, one could use information they collect from Twitter, but based on these results, it cannot be definitively determined how accurate such analysis would be. 28

29 References [1] David M. Pennock, Steve Lawrence, C. Lee Giles, and Finn Arup Nielsen. The Power of Play: Efficiency and Forecast Accuracy in Web Market Games [2] Justin Wolfers and Eric Zitzewitz. Five Open Questions About Prediction Markets [3] Aswath Damodaran. Market Efficiency: Definitions and Tests. m- h.org/damo.pdf. [4] The Economist. Can Twitter predict the future? Internet forecasting: Businesses are mining online messages to unearth consumers moods and even make market predictions. [5] Sitaram Asur and Bernardo A. Huberman. Predicting the Future with Social Media [6] Johan Bollen, Huina Mao, and Xiaojun Zeng. Twitter Mood Predicts the Stock Market [7] Ian Saxon. Intrade Prediciton Market Accuracy and Efficiency: An Analysis of the 2004 and 2008 Democratic Presidential Nomination Contests [8] Shyam Gopinath, Pradeep K. Chintagunta, and Sriram Venkataraman. Blogs and Local- market Movie Box- office Perfromance [9] Eugene F. Fama. Market Efficiency, Long- Term Returns, and Behavioral Finance [10] Allan Timmermann and Clive W.J. Granger. Efficient Market Hypothesis and Forecasting [11] Eugene F. Fama. Efficient Capital Markets: II [12] HSX.com [13] Hootsuite.com [14] Boxofficemojo.com [15] Rottentomatoes.com Websites 29

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