Black swan models for the entertainment industry with an application to the movie business

Success in popular entertainment is highly unpredictable. Yet it is the high-impact low-probability events—known as ‘black swans’—that drive entertainment industry profitability because success is highly concentrated on a small number of winners. In this research, we apply recently developed statistical tools to model motion-picture success; these tools explicitly account for rare extreme events while permitting valid statistical inferences to be made on how product attributes are associated with product success. The specific empirical application relates the attributes of a film and its theatrical release to the distribution of worldwide cumulative box-office revenue. A regression model with skew-stable random disturbances is applied to a large sample of motion pictures to quantify the correlates of film success while explicitly accounting for skewness and heavy tails. The skew-stable estimates are compared to estimates obtained from symmetric-stable regression, ordinary least-squares regression, and several alternative robust-to-outliers regression models. Failure to control explicitly for heavy tails and skewness generates misleading statistical inferences, particularly regarding the impact of production budget, opening week screens, and star power on a film’s success at the box-office.