Latest Projects

Fake News, Investor Attention, and Market Reaction

Information Systems Research forthcoming

Authors: Jonathan Clarke, Hailiang Chen, Ding Du, and Yu Jeffrey Hu

Abstract: Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? We use the SEC crackdown of stock promotion schemes in April 2017 to examine investor attention and the stock price reaction to fake news articles. Using data from Seeking Alpha, we find that fake news stories generate significantly more attention than a control sample of legitimate articles. We find no evidence that article commenters can detect fake news. Seeking Alpha editors have only modest ability to detect fake news. The broader stock market appears to price fake news correctly. The stock price reaction to the release of fake news is not significantly different than a matched control sample over short and longer-term windows. We conclude by presenting a machine learning algorithm that is successful in identifying fake news articles.


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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. 2020.

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

Abstract: 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 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.


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Listening in on investors' thoughts and conversations

Authors: Hailiang Chen and Byoung-Hyoun Hwang

Abstract: One of the most established theories in social psychology suggests that when people consider whether to share a particular content with another person, they tend to be careful about what image sharing such content might create. We find evidence that such impression-management considerations are also important among investors. Utilizing server-log data from one of the biggest investment-related websites in the United States, as well as experimental data, we find that investors more frequently share articles more suitable for impression management, even if such articles less accurately predict returns and even if such articles are infrequently read by sharers themselves.


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Look Before You Leap: Stock Market Valuation of Mobile App Investments

Authors: Ziqing Yuan, Hailiang Chen, and Choon Ling Sia

Abstract: With the continuous growth of the mobile market, many firms are considering investing in mobile apps. Prior research and anecdotal evidence have shown that the success of a mobile app is associated with positive changes in user behavior. However, it is unclear whether the performance implications of mobile app investment can be generalized to a wide range of firms. To further our understanding of IT investments in the mobile domain, we utilized the event study methodology to analyze 761 US public firms that released their first mobile app between 2008 and 2017 on the two leading mobile app stores, Apple’s App Store and Google Play. Our results show that, on average, public firms experience a negative abnormal stock return on the day they release their first mobile app and that the value creation process of mobile app investment is highly contingent on a firm’s market position, IT capability, and industry competition. As the first empirical study to rigorously and systematically document the impact of IT investment in the mobile domain, our findings imply that firms should act cautiously when planning to enter the mobile app market.


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Overpricing of Streaming Licenses? Evidence from a Natural Experiment

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

Media Coverage: International Business Times (IBTimes) TechSpot

Abstract: Subscription-based video streaming has become increasingly popular in recent years. An important question for content owners is how to set the price of streaming licenses for their video content. We provide a perspective on this question by examining the opportunity cost involved in streaming, such that streaming license fees should at least make up for the loss of physical sales due to channel cannibalization. In October 2015, the content owner Epix switched its streaming partner from Netflix to Hulu. The large difference between the market shares of Netflix and Hulu led to a significant decrease in the streaming of Epix’s content and an estimated loss in the annual streaming license fees of $110,000–$120,000 per movie title. Our analysis of this natural experiment shows that Epix’s partner change resulted in additional annual physical sales of only $48,155 per movie title. Our findings imply that content owners may overprice their streaming licenses in practice.

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Product Fit Uncertainty and Interplay between Traditional Advertising and Social Media Marketing

Authors: Yinan Yu, Liangfei Qiu, Hailiang Chen, and Benjamin P. C. Yen

Abstract: Although brands have widely adopted multiple marketing communication media, our understanding of how to effectively coordinate traditional advertising and social media marketing to improve business outcomes is still limited. This paper examines the role of product fit uncertainty in determining how traditional advertising, social media marketing, and their interaction affect product sales differently in the context of the motion picture industry. We first find that traditional advertising is more effective for products with a lower level of fit uncertainty, while social media marketing benefits products with a higher level of fit uncertainty more. More importantly, the interplay between traditional advertising and social media marketing is more likely to be substitutive for low fit uncertainty products and complementary for high fit uncertainty products. Consistent with this finding, we show that marketers’ social media posts featuring experience attributes have a larger effect on the sales of high fit uncertainty products, while social media posts featuring search attributes benefit low fit uncertainty product more. This study sheds lights on how firms can align their multi-channel marketing strategy with product characteristics and effectively communicate the relevant product information with customers to enhance sales.


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