The bigger picture: Combining econometrics with analytics improve forecasts of movie success

Arbeitspapier

There exists significant hype regarding how much machine learning and incorporating social media data can improve forecast accuracy in commercial applications. To assess if the hype is warranted, we use data from the film industry in simulation experiments that contrast econometric approaches with tools from the predictive analytics literature. Further, we propose new strategies that combine elements from each literature in a bid to capture richer patterns of heterogeneity in the underlying relationship governing revenue. Our results demonstrate the importance of social media data and value from hybrid strategies that combine econometrics and machine learning when conducting forecasts with new big data sources. Specifically, while both least squares support vector regression and recursive partitioning strategies greatly outperform dimension reduction strategies and traditional econometrics approaches in forecast accuracy, there are further significant gains from using hybrid approaches. Further, Monte Carlo experiments demonstrate that these benefits arise from the significant heterogeneity in how social media measures and other film characteristics influence box office outcomes.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Queen’s Economics Department Working Paper ; No. 1449

Classification and Topics

• Classification: Wirtschaft ; Model Evaluation, Validation, and Selection ; Entertainment; Media ; Business Economics ; Forecasting Models; Simulation Methods
• Subject: Machine Learning ; Model Specification ; Heteroskedasticity ; Heterogeneity ; Social Media ; Big Data

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lehrer, Steven F. ; Xie, Tian
• Event: Veröffentlichung
• (who): Queen's University, Department of Economics
• (where): Kingston (Ontario)
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/230601
• Last update: 10.03.2025, 11:44 AM CET