【主讲】谢天 (Wuhan University)
【主题】Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?
【时间】2015年10月14日 (周三) 15：30-17：00
【地点】上海财经大学经济学院楼701室
【语言】英文
【摘要】The term "big data" is now commonly reported in the popular press in part since there exists substantial excitement in industry about using data generated from the social web to predict people's reactions to a new product including movies. One of the main challenges researchers in this area face is determining the content from the approximately 350 million tweets and 6 billion Facebookmessages per day. While collaborations predominately between computer scientists and statisticians have led to the development of a set of tools to analyze the sentiment within these messages, the majority of models subsequently used for forecasting exercises do not allow for model uncertainty. In this paper, we first develop two econometric strategies involving the model averaging estimator that both minimize asymptotic risk and are computationally efficient. Second, using data on the universe of Twitter messages, we use an algorithm that calculates the sentiment regarding each film prior to, and after its release date via emotional valence to understand whether these opinions affect box office opening and retail movie unit (DVD and Blu-Ray) sales. Our results contrasting different empirical strategies indicate that accounting for model uncertainty can lead to large gains in forecast accuracy. Incorporating social media data greatly improves forecast accuracy and we find that the inclusion of these variables nearly doubles the explained variation in predicting box office opening. We relate these findings to the behavioral economics literature that examines how opinions of others influence decision-making.
