This paper compares the mean and variance of cumulative abnormal returns following announcements of two types of information technology (IT

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Empirical studies of the determinants of box office revenues have mostly focused on post-production factors – that is, ones known after the film has been completed and/or released. Relatively few studies have considered pre-production factors – that is, ones known before a decision has been made to green-light a film project. The current study directly addresses this gap in the literature. Specifically, we develop and test a relatively parsimonious, pre-production model to predict the opening weekend box office of 170 US-produced, English-language feature films released in the years 2010 and 2011. Chief among the pre-production factors we consider are those derived from the textual and content analysis of the screenplays of these films. The most important of these is determined through the application of network text analysis (NTA) – a method for rendering a text as a map or network of interconnected concepts. As predicted, we find that the size of the main component of a screenplay’s text network strongly predicts the completed film’s opening weekend box office.

This paper describes a novel method of network text analysis, one that involves a new approach to 1) the selection of words from a text, 2) the aggregation of those words into higher-order concepts, 3) the kind of the relationship that establishes statements from pairs of concepts and 4) the extraction of meaning from the text network formed by these statements. After describing the method, I apply it to a sample of the seven most recent winners of the Academy Award for Best Original Screenplay-Little Miss Sunshine, Juno, Milk, The Hurt Locker, The King's Speech, Midnight in Paris, and Django Unchained. Consistent with prior research, I demonstrate that structure encodes meaning. Specifically, it is shown that statements associated with a text network's least constrained nodes are consistent with themes in the films' synopses found on Wikipedia, the International Movie Database, and Rotten Tomatoes.