Latest Projects

The Causal Effect of Video Streaming on DVD Sales: Evidence from a Natural Experiment

Authors: Yinan Yu, Hailiang Chen, Chih Hung Peng, and Patrick Y. K. Chau

Abstract: Video streaming services recently become a revenue driver of the home entertainment industry. By contrast, revenue from physical media continuously declines. Content owners, such as movie studios, face the important question of whether streaming media cannibalize the sales of physical media and to what extent. We answer these questions by exploiting a natural experiment that occurred on October 1, 2015 when Epix switched its streaming partner from Netflix to Hulu. This event created an exogenous shock that reduced the streaming availability of Epix’s content because of the significant difference in the market shares of the two video streaming sites. This occurrence allowed us to investigate the causal effect of streaming services on physical DVD sales. Our difference-in-difference analyses show that the decline in the streaming availability of Epix’s content causes a 24.7% increase in their DVD sales in the three months after the event. Our results validate the industry’s concern that video streaming services displace physical DVD sales. In addition, we find that cannibalization between the two media is stronger for DVDs released more recently and for movies with better box office performances. This study contributes to the understanding of the competition between streaming media and physical media and provides important managerial implications for content owners in selecting appropriate movies for streaming.

 
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Network Structure and Predictive Power of Social Media for the Bitcoin Market

Authors: Peng Xie, Hailiang Chen, and Yu Jeffrey Hu

Abstract: Following the recent discovery of social media’s predictive power for financial markets, we try to advance the literature by evaluating the role of social media network structure in distinguishing between value-relevant information and noises. Using data from the Bitcoin market, we provide empirical evidence that loosely-connected social media discussion networks are more accurate in predicting future returns. Although social media information linkages cause information free riding and damage the overall network prediction accuracy, they nevertheless serve as landmarks for identifying informed social media participants: value-relevant information is more likely to be shared by authors who stimulate active discussions among their peers. We also document a positive relationship between network connectedness and future trading intensity. Our study highlights the importance of leveraging network structures to improve the prediction accuracy of social media analytics for financial markets.

 

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Does Monetary Incentive Lead to Better Stock Recommendations on Social Media?

Authors: Hailiang Chen, Yu Jeffrey Hu, and Shan Huang

Abstract: Social media not only is a new channel to obtain financial market information but also becomes the venue for investors to share and exchange investment ideas. We examine the performance consequences of providing monetary incentive to amateur analysts on social media and its implications for crowd-sourced equity research. We find that monetary incentive is effective in increasing the amount of content outputs but does not lead to better stock recommendations. Additional analysis suggests that monetary incentive results in wider stock and industry coverage, a sign of increased content diversity. This study contributes to the understanding of incentive mechanisms for social media communities in the financial context.

 

Download this working paper at SSRN