Working Papers

Fake social media accounts, or bots, are often used to spread or amplify content on social media platforms. I examine the effect of this artificial amplification on capital markets. I obtain a large dataset of posts from the social media platform Twitter (now X) and use several methods to identify accounts that are likely bots. The descriptive analysis suggests bot accounts produce about a third of all tweets mentioning company tickers. Bot tweets are associated with higher market activity, particularly among retail traders. Bot tweets are generally not associated with return reversals, but those with stock price-related content are. Bot tweets are generally associated with increased liquidity, but those with earnings-related content are associated with reduced liquidity, consistent with higher informed trading. Using an exogenous shock where Twitter suspended a large number of bots, I find evidence consistent with bots impacting capital markets. Overall, the results suggest that fake social media accounts may amplify both helpful and harmful signals to investors.

This study sheds light on the "social" (relational) dimension of social media. We present novel evidence that firms use positive peer tweets as a form of corporate social signaling to indicate economic ties with other firms. By analyzing firms that share positive information about their peers, we examine the determinants, information content, and capital market consequences of positive peer tweeting. We find that positive peer tweeting is associated with economic relatedness, the demand for legitimacy, and litigation risk. These tweets predict future inter-firm collaborations, conveying information not readily available through traditional disclosures channels. Furthermore, positive peer tweets generate significantly positive market reactions for both the tweeting and tweeted firms. However, this corporate social signaling is not without costs: tweeting firms experience negative market reactions when the firms they have tweeted about subsequently release negative news. These findings highlight social media's social dimension beyond its well-studied disclosure and dissemination roles.

This paper develops daily market-wide sentiment and attention indexes derived from millions of posts across major investor social media platforms. We find that sentiment extrapolates from past market-wide returns and exhibits a strong reversal. In contrast, attention predicts negative returns as a continuation of previous trends. The two indexes have distinct predictions for aggregate trading: abnormal trading rises when sentiment is low and attention is high. To identify the drivers of attention and sentiment, we use a shock to data sharing networks: We find sentiment spreads through real firm connections while attention does not. Moreover, attention rises after abnormally high trading, while sentiment rises after abnormally high returns. This extrapolative return pattern is asymmetric, primarily driven by negative market jumps. These findings provide new evidence on the daily market dynamics of sentiment and attention.

Social media influencers in the stock market are commonly assumed to have a significant impact on investor decision-making. We have examined how YouTube influencers at different power levels exert varying degrees of impact on trading volume and stock returns. We present four main findings (1) Theory and common belief are nearly correct in that high-influencer groups exert stronger effects on stock returns and trading volume than lower-influencer groups; (2) Furthermore, the uploading behavior of high-influence groups is associated with significantly positive BHAR over the 10- to 60-day horizon; (3) However, on average, compared with the most-high and less-high influencer group, the less-high influencer group showed the more effect on short-term stock returns and trading volume; (4) The weaker effect of most-high influencer group tends to be associated with their coverage of large firms and their choice of a more cautious tone in their videos. We have also found evidence of spillover effects from influencers, implying that video uploads by high-influencer groups lead to more uploads from others. Furthermore, influencers' effects are more pronounced under short-selling constraints.

We examine institutional investors' presentations at charity-hosted investment conferences and show presenters act as information intermediaries at these events by contextually evaluating presented firms. Our study uses a unique setting where the information processing and analyses of sophisticated institutional investors are publicly disseminated, allowing us to examine how expert interpretation of public information aids the information processing of other market participants. Consistent with presentations improving other investors' information processing, prices reflect earnings news faster for presented firms in the quarters after presentations. The effect is stronger for presentations with in-depth analyses, multiple arguments, and longer presentation notes. Presenters are distinct information intermediaries as they use novel arguments compared to pre-conference analyst reports. After the conferences, the content of analysts' reports changes and they improve their earnings forecast accuracy. Our study sheds light on the previously unexplored role of institutional investors as information intermediaries and introduces a novel mechanism-termed the expertise effect-which complements the traditional awareness effect. Together, these effects underscore how institutional investors' presentations reduce information integration costs for other market participants.

We leverage rich social media data and large language models (LLMs) to examine the relationship between investor trading strategies, sentiment, and market outcomes. Extracting trading strategies  embedded in 96 million social media posts, we find that  strategy adoption is heterogeneous and dynamic, with substantial differences in performance outcomes. Our results show that news arrivals decrease users' reliance on technical signals and increase their utilization of fundamental signals. Technical sentiment negatively predicts stock returns, particularly among short-term or inexperienced users, whereas fundamental sentiment positively forecasts returns. Additionally, message sentiment correlates positively with aggregate retail buying, with technical sentiment strongly associated with aggressive buying by Robinhood investors. Our study demonstrates the promise of using AI to understand investor behaviors and their implications for market dynamics.

Boycotts triggered by public companies' practices perceived as ideologically polarizing can lead to negative investor reactions. In this study, I examine how the stock market responds to such boycotts and whether ideology-driven social media discourse shapes this response, given investors' increasing reliance on social media information for decision-making. On average, polarizing boycotts are associated with a 1% (2.3%) drop in equity value over the 7 (60) trading days after gaining online traction. Immediate price decline is more pronounced when social media discussions are dominated by users ideologically aligned with the boycotters, particularly when their posts attract online engagement, emphasize financial impact, or come from influential, prolific users. I also find modest evidence that return volatility following boycotts increases when the ideological beliefs of social media posters are more diverse. My findings suggest that polarizing boycotts against corporate actions have stock market ramifications, and that ideology-driven social media opinions seem to amplify both price decline and volatility.

We use data from HotCopper, Australia's largest stock message board, and exploit its unique announcement-specific thread structure to examine whether investors' accounting-related discussions help process less readable corporate announcements. We find that less readable disclosures, particularly unanticipated nonearnings announcements, generate more accounting-related discussions and are more pronounced for firms with low institutional ownership, limited coverage, and high operational complexity. Market reaction analyses reveal that these discussions are associated with stronger price and volume responses and improve market reactions around less readable announcements, suggesting that social media can function as a low-cost supplement for traditional information intermediaries. Further comparing New Zealand based stock message board, Sharetrader.co.nz, with the absence of announcement-linked threads shows weaker and less consistent effects, underscoring that social media's informativeness in capital markets hinges not just on investor participation but critically on platform design. Our findings contribute to the disclosure processing cost literature by showing that structured forums reduce cognitive frictions and improve market efficiency, especially in retail investor-dominated settings.

This paper shows that intense discussion on the Reddit social-media platform increases noise-trader risk for informed investors, resulting in lower price informativeness about earnings and delayed mispricing correction. Increased social discussion is associated with a decreased magnitude of pre-earnings-announcement drift, increased earnings-response coefficients, and a sizeable return reversal, demonstrating the decline in price informativeness around earnings announcement dates. In addition, stock prices of firms with high social discussion incorporate future earnings news more slowly. Social discussion leads to a decrease in trading activity by informed investors, such as hedge funds and short sellers. Consequently, social discussion results in delayed price correction of well-documented anomalies for up to two months; a corresponding trading strategy earns about 1.4% monthly. The main findings are corroborated using a matched sample. The findings suggest that intense social discussion reduces the production of value-relevant information.

Using debit and credit card data, we find that a one standard deviation increase in local firms' earnings surprises leads to a 2% increase in biweekly household consumption among households located near the firms' headquarters. This relation is stronger when earnings news is relevant to the local economy, widely disseminated, linked to increased information acquisition, or a credible signal of firm performance. Consumption responses (a) span various households, including small business owners, investors, employees, and other unaffiliated households, (b) are more pronounced among financially sophisticated households likely able to access earnings news, and (c) are concentrated in inexpensive, discretionary items, such as dining out. A two-round survey of 432 households before and after earnings announcements in late 2024 shows that household consumption responds to local earnings news, primarily because households update their beliefs about financial prospects, with media and word of mouth serving as key news dissemination channels. Our findings altogether indicate that earnings news informs local household consumption decisions.

This paper examines the existence, drivers, and implications of toxic content in financial social media. Using state-of-the-art machine learning algorithms to measure toxicity on Seeking Alpha, we find persistent toxicity primarily in the comments rather than articles, with over 50% of firms on the platform experiencing toxicity in recent years. Comment toxicity is greater for firms with more investor attention and disagreement, and for those led by female CEOs. We find three key results. First, toxicity displays a feedback loop in platform participation: past toxicity predicts more future toxic contributors for a given firm. Second, firms receiving more toxic comments have greater retail trading volume but less informative retail trades. Third, toxicity is associated with slower price discovery around earnings announcements, indicating potential broader market efficiency implications. Our findings suggest financial social media toxicity influences both user behavior and market outcomes, raising important considerations for platform governance in financial markets.

This study investigates how social media network structure influences short-term stock market reactions to buy recommendations issued by self-identified social media analysts (SMAs) within online retail investor communities. Consistent with social learning theories-and in contrast to social utility theories-we find that communities with lower network cohesion experience more positive short-term market reactions to buy recommendations. However, this negative relation diminishes at higher levels of network cohesion. Further supporting the social learning theories, the impact of network cohesion is more pronounced for SMAs with higher recommendation accuracy. In line with behavioral finance theories, we also find the effect of network cohesion to be stronger for stocks facing greater limits to arbitrage. Our exploratory analysis suggests that part of the initial price reaction reflects investor overreaction, as indicated by subsequent price reversals.