College of Business
Shanghai University of Finance and Economics
Shanghai, China, 200433
Institutional Affiliation: Shanghai University of Finance and Economics
NBER Working Papers and Publications
|November 2019||Does High Frequency Social Media Data Improve Forecasts of Low Frequency Consumer Confidence Measures?|
with , : w26505
Social media data presents challenges for forecasters since one must convert text into data and deal with issues related to these measures being collected at different frequencies and volumes than traditional financial data. In this paper, we use a deep learning algorithm to measure sentiment within Twitter messages on an hourly basis and introduce a new method to undertake MIDAS that allows for a weaker discounting of historical data that is well-suited for this new data source. To evaluate the performance of approach relative to alternative MIDAS strategies, we conduct an out of sample forecasting exercise for the consumer confidence index with both traditional econometric strategies and machine learning algorithms. Irrespective of the estimator used to conduct forecasts, our results sho...
|June 2018||The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success|
with : w24755
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 recursive partitioning strategies greatly outperform dimens...
|December 2016||Box Office Buzz: Does Social Media Data Steal the Show from Model Uncertainty When Forecasting for Hollywood?|
with : w22959
Substantial excitement currently exists in industry regarding the potential of using analytic tools to measure sentiment in social media messages to help predict individual reactions to a new product, including movies. However, the majority of models subsequently used for forecasting exercises do not allow for model uncertainty. Using data on the universe of Twitter messages, we use an algorithm that calculates the sentiment regarding each film prior to, and after its release date via emotional valence to understand whether these opinions affect box office opening and retail movie unit (DVD and Blu-Ray) sales. Our results contrasting eleven different empirical strategies from econometrics and penalization methods indicate that accounting for model uncertainty can lead to large gains in for...
Published: 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, vol 99(5), pages 749-755. citation courtesy of