Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.Comment: 3rd International Conference on Knowledge Engineering and Applications (ICKEA 2018) - Moscow, Russia (June 25-27 2018

The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

Twitter’s Relationship with Overreaction in Individual Security Returns

Using stock market return data from 2007 to 2019 from The Center for Research in Security Prices, I inquire into the impact that Twitter has on the overreactions of individual stock returns by breaking down returns into pre and post-Twitter periods. I examine negative serial correlation, demonstrating return reversals, between a lag crossed Twitter dummy variable and initial returns. With stock reversals serving as an indicator of initial overreaction and assuming stationarity of overreactions over time, I find that the presence of Twitter results in significantly more overreactions for highly followed companies when using monthly returns. However, when assessing Twitter’s influence using weekly returns, the results suggest the possibility of return momentum. Similarly, Twitter’s influence on overreaction is a highlighted when evaluating only negatively or positively large returns, producing greater significance despite a decrease sample size. While these promising results are not economically significant and thus do not reveal a viable contrarian investment strategy, my paper lays the foundation for a predictive model based of Twitter’s influence on company returns

Twitter and the US stock market: the influence of micro‑bloggers on share prices

With the increased interest in social media over recent years, the role of information disseminated through avenues such as Twitter has become more widely perceived. This paper examines the mention of stocks on the US markets (NYSE and NASDAQ) by a number of financial micro-bloggers to establish whether their posts are reflected in price movements. The Twitter feeds are selected from syndicated and nonsyndicated authors. A substantial number of tweets were linked to the price movements of the mentioned assets and an event study methodology was used to ascertain whether these mentions carry any significant information or whether they are merely noise

It’s more about the Content than the Users! The Influence of Social Broadcasting on Stock Markets

Social broadcasting networks facilitate the public exchange of information and contain a large amount of stock-related information. This data is increasingly analyzed by research and practice to predict stock market developments. Insights from social broadcasting networks are used to support the decision-making process of investors and are integrated into automatic trading algorithms to react quickly to broadcasted information. However, a comprehensive understanding about the influence of social broadcasting networks on stock markets is missing. In this study, we address this gap by conceptualizing and empirically testing a model incorporating three dimensions of social broadcasting networks: users, messages, and discussion. We analyze 1.84 million stock-related Twitter messages concerning the S&P 100 companies between January and April 2014 and corresponding intraday stock market data from NYSE and NASDAQ. Our research model is constructed applying factor analyses and tested using a fixed effects panel analysis. The results show that the influence of social broadcasting on stock markets is driven by the message and discussion dimensions whereas the user dimension has no significant influence. Specifically, the influence of user mentions, financial sentiment, discussion reach, and discussion volume has the largest impact and should carefully be considered by investors making trading decisions

Forecasting power of social media sentiment time series

Social media are not only the new form of communication, but also give the ability to big industry players, like Facebook, to analyze overwhelming amounts of data about customers’ behavior. The focus of this study was to analyze if social media time series have the power to predict the evolution of financial markets. Despite the short time frame being analyzed, the study delivered promising results that social media time series may be a leading indicator for market behavior after special events. Building on my findings, an applied sentiment trading strategy delivered positive abnormal returns and statistically significant positive alpha in and out-of-sample

Using Social Media Analytics: The Effect of President Trump’s Tweets on Companies’ Stock Performance

With the recent political development in the United States I was presented with a unique opportunity to examine social media’s influences on the stock market. Specifically, I analyzed the impact of tweets from President Trump’s official Twitter accounts from his election to the office to February 1st, 2019 that targeted a publicly traded company. I find that these tweets have a very minimal effect on companies’ stock prices, but there is a significant effect on the stocks’ trading volumes