As one of the industries most impacted by the pandemic, the U.S./Canada movie box office market plummeted by 80% in 2020. However, anecdotal reports suggest that not all categories of movies were equally affected. This study focuses on investigating Covid’s influence on different types of movies in the U.S. movie market, specifically how the movie box office of different movies changes under Covid. By utilizing a movies release database from SNL Kagan spanning 5 years, additional data from various sources, and machine learning methods, the study compares their actual box office receipts to the receipts predicted by a machine learning program. The predicted receipts are the movies’ box office receipts if they were released in non-Covid times. The data shows that the overall movie budget is reduced in Covid and the return on investment (ROI) also declines significantly. In addition, the results show that PG-rated movies suffer the greatest loss in box office and R-rated movies have the least loss. The study proposes a way to inquire into different genres’ resistance and resilience to Covid and theaters shutdowns.
