The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success

• 成果类型: Article
• 署名作者: Lehrer, Steven F.; Xie, Tian
• 署名单位: Queens University - Canada; National Bureau of Economic Research; Shanghai University of Finance & Economics
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3911
• 发表日期: 2022
• 页码: 189-210
• 关键词: Machine learning; model specification; heteroskedasticity; MOVIES; Social media; big data

摘要

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, although 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.