Using Virtual Stock Exchanges to Forecast Box-Office Revenue via Functional Shape Analysis

In this paper we propose a novel model for forecasting innovation success based on online virtual stock markets. In recent years, online virtual stock markets have been increasingly used as an economic and efficient information gathering tool for the online community. It has been used to forecast events ranging from presidential elections to sporting events and applied by major corporations such as HP and Google for internal forecasting. In this study, we demonstrate the predictive power of online virtual stock markets, as compared to several conventional methods, in forecasting demand for innovations in the context of the motion picture industry. In particular, we forecast the release weekend box office performance of movies which serves as an important planning tool for allocating marketing resources, determining optimal release timing and advertising strategies, and coordinating production and distributions for different movies. We accomplish this forecasting task using novel statistical methodology from the area of functional data analysis. Specifically, we develop a forecasting model that uses the entire trading path rater than only its final value. We also employ trading dynamics and we tease out differences between different trading paths using functional shape analysis. Our results show that the model has strong predictive power and improves tremendously over competing approaches.