Published Papers

Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement returns. Using a broad sample from 2009 to 2012, we find that the aggregate opinion in individual tweets successfully predicts a firm's forthcoming quarterly earnings and announcement returns. These results hold for tweets that convey original information, as well as tweets that disseminate existing information, and are stronger for tweets providing information directly related to firm fundamentals and stock trading. Importantly, our results hold even after controlling for concurrent information or opinion from traditional media sources, and are stronger for firms in weaker information environments. Our findings highlight the importance of considering the aggregate opinion in individual tweets when assessing stocks' future prospects and value.

We examine the determinants and market consequences associated with earnings announcements going viral on social media, a phenomenon we label "earnings virality." Using a comprehensive panel of historical Twitter data, we find that the typical earnings announcement receives relatively little social media coverage, but others go viral on social media, quickly reaching the feeds of millions of people. We find that viral earnings announcements generally have Twitter content that is more extreme in tone and contains less unique content. Further, earnings virality is positively associated with revenue surprises, investor recognition, retail investor ownership, and retail investor trading around the announcement. Earnings virality appears to be detrimental to markets, as it coincides with lower market liquidity and slower price formation. Overall, our evidence suggests that user-driven dissemination through social media platforms, when amplified and taken to extreme levels, may be harmful to markets.

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.

Social media has become a popular venue for individuals to share the results of their own analysis on financial securities. This paper investigates the extent to which investor opinions transmitted through social media predict future stock returns and earnings surprises. We conduct textual analysis of articles published on one of the most popular social media platforms for investors in the United States. We also consider the readers' perspective as inferred via commentaries written in response to these articles. We find that the views expressed in both articles and commentaries predict future stock returns and earnings surprises.

We study whether social media can play a negative information role by impeding price discovery in the presence of highly speculative rumors. We focus on merger rumors, where most do not materialize. We find that merger rumors accompanied by greater Twitter activity elicit greater immediate market reaction even though rumor-related Twitter activity is unrelated to the probability of merger realization. The price distortion associated with tweet volume persists weeks after a rumor and reverses only after eight weeks. The price distortion is more pronounced for rumors tweeted by Twitter users with greater social influence, for target firms with low institutional ownership, and for rumors that supply more details. Our evidence suggests that social media can be a rumor mill that hinders the market's price discovery of potentially false information.

We examine why buy-side analysts share investment ideas on SumZero.com, a private social networking website designed to facilitate interaction and information sharing among buy-side professionals. We first document that our sample of more than 1,000 buy-side analysts issue recommendations that have investment value. In particular, recommendations generate significant returns when they are posted to the website and the returns to both buy and sell recommendations drift in the direction of the recommendation. These returns are the most dramatic for contrarian recommendations (i.e., those issued contrary to the sell-side consensus). We explore labor-market motivations for sharing information and document that analysts who have strong incentives to seek new jobs (those at small funds), are significantly more likely to issue recommendations. We also show that analysts who share investment ideas are more likely to change jobs, and that the ratings their recommendations receive are positively related to changing employment. Overall, we show that social networking is an effective reputation building and job seeking tool for buy-side analysts.

When will a large group provide an accurate answer to a question involving quantity estimation? We empirically examine this question on a crowd-based corporate earnings forecast platform (Estimize.com). By tracking user activities, we monitor the amount of public information a user views before making an earnings forecast. We find that the more public information users view, the less weight they put on their own private information. Although this improves the accuracy of individual forecasts, it reduces the accuracy of the group consensus forecast because useful private information is prevented from entering the consensus. To address endogeneity concerns related to a user's information acquisition choice, we collaborate with Estimize.com to run experiments that restrict the information available to randomly selected stocks and users. The experiments confirm that "independent" forecasts result in a more accurate consensus. Estimize.com was convinced to switch to a "blind" platform from November 2015 on. The findings suggest that the wisdom of crowds can be better harnessed by encouraging independent voices from among group members and that more public information disclosure may not always improve group decision making.

We study the effects of social media on the informativeness of retail trading. Our identification strategy exploits the editorial delay between report submission and publication on Seeking Alpha, a popular crowdsourced investment research platform. We find the ability of retail order imbalances to predict the cross-section of stock returns and cash-flow news increases sharply in the intraday post-publication window relative to the pre-publication window. The findings are robust to controlling for report tone and stronger for reports authored by more capable contributors. The evidence suggests that recent technology-enabled innovations in how individuals share information help retail investors become better informed.

Social interaction contributes to some traders' disposition effect. New data from an investment-specific social network linked to individual-level trading records builds evidence of this connection. To credibly estimate causal peer effects, I exploit the staggered entry of retail brokerages into partnerships with the social trading web platform and compare trader activity before and after exposure to these new social conditions. Access to the social network nearly doubles the magnitude of a trader's disposition effect. Traders connected in the network develop correlated levels of the disposition effect, a finding that can be replicated using workhorse data from a large discount brokerage.

Crowdsourcing-when a task normally performed by employees is out-sourced to a large network of people via an open call-is making inroads into the investment research industry. We shed light on this new phenomenon by examining the value of crowdsourced earnings forecasts. Our sample includes 51,012 forecasts provided by Estimize, an open platform that solicits and reports forecasts from over 3,000 contributors. We find that Estimize forecasts are incrementally useful in forecasting earnings and measuring the market's expectations of earnings. Our results are stronger when the number of Estimize contributors is larger, consistent with the benefits of crowdsourcing increasing with the size of the crowd. Finally, Estimize consensus revisions generate significant two-day size-adjusted returns. The combined evidence suggests that crowdsourced forecasts are a useful supplementary source of information in capital markets.

This study examines whether crowdsourced forecasts of earnings and revenues help investors unravel bias in earnings announcement news, which is commonly derived from analyst forecasts. Our results suggest that investors, on average, understand and price the predictive signals reflected in crowdsourced forecasts about the bias in analyst-based earnings and revenue surprises. Using the staggered addition of firms to the Estimize platform, we find that crowdsourced coverage is associated with reductions in the mispricing of forecast bias and declines in earnings announcement premia. We further find some evidence that managers use income-increasing accruals to meet the crowdsourced forecast benchmark and that they respond to crowdsourced coverage through increased downward earnings and revenue guidance. Overall, we conclude that user-generated content on crowdsourced financial information platforms helps investors discount biases in traditional equity research and thereby better process the news in earnings announcements.

We model visibility bias in the social transmission of consumption behavior. When consumption is more salient than nonconsumption, people perceive that others are consuming heavily, and infer that future prospects are favorable. This increases aggregate consumption in a positive feedback loop. A distinctive implication is thatdisclosure policy interventions can ameliorate undersaving. In contrast with wealth-signaling models, information asymmetry about wealth reduces overconsumption. The model predicts that saving is influenced by social connectedness, observation bi-ases, and demographic structure, and provides new insight into savings rates. These predictions are distinct from other common models of consumption distortions.