The Wisdom of Crowds: Pre-Release Forecasting via Functional Shape Analysis Of the Online Virtual Stock Market

Abstract

Pre-release demand forecasting is crucial for allocating limited marketing resources and is one of the most challenging tasks facing decision makers. In this study, we present a powerful pre-release forecasting method via functional shape analysis (FSA) of the trading price histories of an online virtual stock market (VSM). Specifically, we use FSA to extract the key characteristics that capture the similarities and differences in the shapes across various trading histories and then use these key characteristics to produce forecasts. We analyze one of the best-known online VSMs, the Hollywood Stock Exchange, in forecasting release week box office revenues of motion pictures. While several conventional forecasting methods result in forecasting errors as high as 60%, our method has a forecasting error of only 8.31%. As compared to the VSM literature that has largely focused on the most recent trading prices alone, our approach of analyzing the trading histories reduces the forecasting errors by up to 50%. Moreover, applying our method to the early, partial trading histories yields early and dynamic forecasts that are highly valuable to decision makers and are not readily available from using conventional methods. Our approach represents a novel contribution to both the VSM and marketing literature. We further provide conceptual interpretations of the shapes of the trading histories that help decision makers identify the key indicators (e.g. a last-moment price spurt) of a potentially successful new product or service.