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Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?

Author

Listed:
  • Steven Lehrer

    (Queen’s University, NYU Shanghai, and NBER)

  • Tian Xie

    (Wang Yanan Institute for Studies in Economics, Department of Finance, MOE Key Lab of Econometrics, and Fujian Key Lab of Statistical Sciences, Xiamen University)

Abstract

Business decision makers are increasingly using predictive social media analytic tools in forecasting exercises but ignoring potential model uncertainty. Using data on the universe of Twitter messages, we calculate the sentiment regarding each film to understand whether these opinions affect box office opening and DVD retail sales. Our results contrasting eleven different econometric strategies including penalization methods indicate that accounting for model uncertainty can lead to large gains in forecast accuracy. While penalization methods do not outperform model averaging on forecast accuracy, evidence indicates they perform equivalently at the variable selection stage. Finally, incorporating social media data greatly improves forecast accuracy.

Suggested Citation

  • Steven Lehrer & Tian Xie, 2017. "Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 749-755, December.
    • Handle: RePEc:tpr:restat:v:99:y:2017:i:5:p:749-755
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    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00671
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    Cited by:

    1. Carstensen, Kai & Bachmann, Rüdiger & Schneider, Martin & Lautenbacher, Stefan, 2018. "Uncertainty is Change," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181572, Verein für Socialpolitik / German Economic Association.
    2. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    3. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    4. Hui Xiao & Yiguo Sun, 2019. "On Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study," JRFM, MDPI, vol. 12(3), pages 1-16, June.
    5. Zhao, Shangwei & Xie, Tian & Ai, Xin & Yang, Guangren & Zhang, Xinyu, 2023. "Correcting sample selection bias with model averaging for consumer demand forecasting," Economic Modelling, Elsevier, vol. 123(C).
    6. Steven Lehrer & Tian Xie & Tao Zeng, 2021. "Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures? [Regression Models with Mixed Sampling Frequencies]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 910-933.
    7. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    8. Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021. "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 179-201.
    9. Yuying Sun & Shaoxin Hong & Zongwu Cai, 2025. "State-Varying Model Averaging Prediction," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202507, University of Kansas, Department of Economics.
    10. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    11. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    12. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
    13. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    14. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    15. Xie, Tian, 2017. "Heteroscedasticity-robust model screening: A useful toolkit for model averaging in big data analytics," Economics Letters, Elsevier, vol. 151(C), pages 119-122.

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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