Managerial decision making in the US motion pictures theatrical distribution industry: Demand uncertainty and real options analysis

Abstract

The master thesis is dedicated to decision making algorithm for the distributors of motion pictures at the post-release period, when a movie’s premiere took place and new data became available in order to predict the box office revenue with a higher accuracy than traditional models can do. Two elements of the model are artificial neural network and real option analysis. The former is developed to generate an accurate forecast, while the latter employs this result on a final stage of decision making: whether a certain movie is worth to be supported with additional marketing budget spending or not. A big sample of movies was drawn to design the neural network capable of making credible predictions and working with new patterns of data. On the other hand, a simple valuation model of real option of delayed exit was prepared to make it understandable for the industry practitioners and easy-to-use in real life circumstances.