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Product cover image for Glitz Investments: Predicting a Blockbuster

Glitz Investments: Predicting a Blockbuster

Shelly Bajaj; Sean Bandyopadhyay; Srinivas Krishnamoorthy; Kyle Maclean

Product #:9B19E013
Supplier:Ivey
Discipline:International Business, Management Science
Setting:India, 2018
Details:Case (PDF) • 8 pages • 2020-02-11

Subjects:

Business managementAnalytics and data scienceInternational businessData science

Industries:

Media, entertainment, and professional sports

Geography:

AsiaIndia
Your Price:$9.71

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An investment manager at Glitz Investments, a firm based in the United States with a focus on the entertainment industry, wanted to identify potential blockbuster movies in the Indian film industry for the firm’s potential investment purposes. The investment manager received a report compiled by analysts at the firm indicating that Bollywood would be an attractive industry for investment. The firm’s analysts had collected data but could the manager determine a quantitative relationship between box office performance and the factors the team had identified? Could Glitz Investments choose movies based on this analysis alone? The investment manager's boss was looking for more specific data on which to base the firm’s investment decisions.

Learning Objectives

This case is suitable to illustrate the application of quantile regression in an advanced analytics course at the undergraduate or graduate level. The case can also be used as part of a core course in a master of science program in analytics. It illustrates the extreme uncertainty associated with revenue outcomes in the film industry—a factor that is common across various cultural industries, including theatre, music, and book publishing. The two analytical techniques explored in this case are log linear regression and quantile regression. Quantile regression is a particularly useful technique in scenarios where outcomes follow a power-law distribution, as is the case with Bollywood and other cultural industries. After completion of this case, students will be able to
  • interpret the coefficients from log linear and quantile regression analyses; and
  • understand how these regression techniques can be useful in an environment with power-law distributed outcomes.
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Case Study: Glitz Investments: Predicting a Blockbuster (English version) | Ivey Publishing