A large literature in neuroscience and social psychology shows that humans are wired to be meticulous about how they are perceived by others. In this paper, we propose that impression management considerations can also end up guiding the content that investors transmit via word of mouth and inadvertently lead to the propagation of noise. We analyze server log data from one of the largest investment-related websites in the United States. Consistent with our proposition, we find that investors more frequently share articles that are more suitable for impression management despite such articles less accurately predicting returns. Additional analyses suggest that high levels of sharing can lead to overpricing.
- PhD, Purdue University
- MS, Purdue University
- BM, Tsinghua University
Hailiang Chen is interested in the research areas of social media, fintech, artificial intelligence, business analytics, venture capital, entrepreneurship, mobile and social commerce, economics of information systems, and design science. His research has been published in elite business journals in information systems, finance, and management, including Information Systems Research (ISR), Journal of Financial Economics (JFE), Journal of Management Information Systems (JMIS), Management Science (MS), Review of Financial Studies (RFS), and Strategic Management Journal (SMJ). His research received media coverage in outlets such as Wall Street Journal, Forbes, New York Times, Reuters, Seeking Alpha, TechSpot, and so on.
- Business Analytics
- Social Media
- Capstone Project
- Chen, Hailiang, Byoung-Hyoun Hwang. 2022. Listening in on Investors’ Thoughts and Conversations. Journal of Financial Economics 145(2) 426-444.
- Yu, Yinan, Hailiang Chen, Chih-Hung Peng, Patrick Y.K. Chau. 2022. The Causal Effect of Subscription Video Streaming on DVD Sales: Evidence from a Natural Experiment. Decision Support Systems 157, 113767.
- Clarke, Jonathan, Hailiang Chen, Ding Du, Yu Jeffrey Hu. 2021. Fake News, Investor Attention, and Market Reaction. Information Systems Research 32(1) 35-52.
- Xie, Peng, Hailiang Chen, Yu Jeffrey Hu. 2020. Signal or Noise in Social Media Discussions: The Role of Network Cohesion in Predicting the Bitcoin Market. Journal of Management Information Systems 37(4) 933-956.
- Chen, Hailiang, Yu Jeffrey Hu, Shan Huang. 2019. Monetary Incentive and Stock Opinions on Social Media. Journal of Management Information Systems 36(2) 391-417.
- Chen, Hailiang, Yu Jeffrey Hu, Michael D. Smith. 2019. The Impact of E-book Distribution on Print Sales: Analysis of a Natural Experiment. Management Science 65(1) 19-31.
- Akcura, Tolga, Kemal Altinkemer, Hailiang Chen. 2018. Noninfluentials and Information Dissemination in the Microblogging Community. Information Technology and Management 19(2) 89-106.
- Lee, Joon Mahn, Byoung-Hyoun Hwang, Hailiang Chen. 2017. Are Founder CEOs more Overconfident than Professional CEOs? Evidence from S&P 1500 Companies. Strategic Management Journal 38(3) 751-769.
- Chen, Hailiang, Prabuddha De, Yu Jeffrey Hu. 2015. IT-Enabled Broadcasting in Social Media: An Empirical Study of Artists’ Activities and Music Sales. Information Systems Research 26(3) 513-531.
- Chen, Hailiang, Prabuddha De, Yu Jeffrey Hu, Byoung-Hyoun Hwang. 2014. Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media. Review of Financial Studies 27(5) 1367-1403.
- Chen, Hailiang, Hongyan Liu, Jiawei Han, Xiaoxin Yin, Jun He. 2009. Exploring Optimization of Semantic Relationship Graph for Multi-relational Bayesian Classification, Decision Support Systems 48(1) 112-121.
Prior studies have shown that social media discussions can be helpful in predicting price movements in financial markets. With the increasingly large amount of social media data, how to effectively distinguish value-relevant information from noise remains an important question. We study this question by investigating the role of network cohesion in the relationship between social media sentiment and price changes in the Bitcoin market. As network cohesion is associated with information correlation within the discussion network, we hypothesize that less cohesive social media discussion networks are better at predicting the next-day returns than more cohesive networks. Both regression analyses and trading simulations based on data collected from Bitcointalk.org confirm our hypothesis. Our findings enrich the literature on the role of social media in financial markets and provide actionable insights for investors to trade based on social media signals.
Digital distribution introduces many new strategic questions for the creative industries—notably, how the use of new digital channels will impact sales in established channels. We analyze this question in the context of e-book and hardcover sales by exploiting a natural experiment that exogenously delayed the release of a publisher’s new Kindle e-books in April and May 2010. Using new books released simultaneously in e-book and print formats in March and June 2010 as the control group, we find that delaying e-book availability results in a 43.8% decrease in e-book sales but no increase in print book sales on Amazon.com or among other online or offline retailers. We also find that the decrease in e-book sales is greater for books with less prerelease buzz. Together, we find no evidence of strong cannibalization between print books and e-books in the short term and no support for the sequential distribution of books in print versions followed by e-book versions.