Working Papers

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.

We study non-traditional investment research as a source of information for corporate strategic decisions, such as investments into innovation. Using a comprehensive sample of social media analyst reports from Seeking Alpha and exogenous variation in social media analysts' coverage overlaps, we show that firms are more likely to invest into technologies similar to firms covered by the same analyst. The effect varies with social media analysts' characteristics and differences in their contributed content that capture their unique information set. Overall, our results are consistent with non-traditional investment research enhancing firms' information environment as an additional source of information that guides corporate strategic decisions.

Does social media amplify bank fragility absent systemic crises (e.g., the SVB crisis)? Using a sample of U.S. commercial banks from 2009 to 2022, we show that heightened Twitter attention increases the sensitivity of non-core deposits-but not core deposits-to bank performance deterioration. This effect intensifies for banks with greater liquidity mismatch and when Twitter discussions are more influential. Neither enhanced bank transparency nor negative sentiment in social media discussions explains these results. Our findings indicate that social media is not merely an information transmitter; it heightens depositors' awareness of peer attention to banks, amplifying deposit outflow sensitivity to weak fundamentals even during calm periods.

Our research augments the expanding body of literature concerning the capability of prevalent LLM tools in supporting financial professionals. We introduce a framework to leverage ChatGPT to assess market sentiment through the analysis of social media data. We use the LLM models to construct market sentiment indicators based on Twitter tweets and use those indicators to explain Bitcoin return. Our analysis uncovers that sentiment indicators crafted with ChatGPT4o/ChatGPT3.5 significantly affect Bitcoin returns, even when accounting for a broad array of control variables and other pre-established sentiment indicators. These insights imply that ChatGPT4o/ChatGPT3.5 could empower financial professionals to discover sentiment information from Twitter tweets that were overlooked by previously introduced sentiment indicators concerning Bitcoin.

This paper relies on shocks to the CAPTCHA technology to study the effects of social media bot activity. We observe that bots drive a large amount of attention to corporate accounts around earnings announcements. In line with theoretical research, bot activity is a significant predictor of investor disagreement, which is persistent long-term. Moreover, social media bots increase analyst dispersion for the following earnings announcement. The failure of the CAPTCHA implementation technology leads to negative abnormal returns, and the failure of CAPTCHA bypassing technology leads to positive abnormal returns

Do firms' tweets improve stock price discovery at quarterly earnings announcements? We address this question using a comprehensive sample of tweets released by S&P 1500 firms at quarterly earnings announcements from 2008 through 2021. Firms' tweets are associated with stronger stock price and volume reactions to earnings announcements. In addition, firms' tweets reduce investor uncertainty, increase the timeliness and efficiency with which stock prices reflect information, and reduce the post-earnings announcement drift. We document that firms' tweets improve stock price discovery by enhancing firm visibility and increasing retail investor trading, which facilitate faster incorporation of information into stock prices. Our inferences are robust to a host of control variables, including alternative measures of media coverage, and persist in both a propensity score matched sample and an entropy balanced sample, where firms that use Twitter are matched with similar firms that do not. Our findings are of interest to regulators who wish to improve the informativeness of security prices, investors who are interested in information that affects prices and volume, and managers who seek channels to communicate with investors. 

We examine how political geography shapes retail investor behavior through partisan myside bias-a location-specific form of confirmation bias. Using household-level data from the Panel Study of Income Dynamics (PSID) and detailed brokerage records, we find that investors in politically aligned states exhibit greater trust in the market, stronger peer-driven trading, and a higher tendency toward behavioral biases such as local bias, disposition effect, and overconfidence. These effects are most pronounced when political sentiment is strong, highlighting how regional partisanship can distort financial decision-making.

We examine Twitter discussion of sell-side analysts' stock recommendation revisions. While many investors lack direct access to analyst research, we observe revision-related Twitter discussion associated with approximately 90% of the revisions in our sample, usually within three hours of their announcement. Revision-related Twitter discussion is more extensive for upgrades and for analysts from larger brokerages. Examining within-revision intraday price discovery, we also observe increased levels of price discovery during intraday windows with more revision-related tweets, especially for tweets with more user engagement, those posted by more influential authors, and for stocks with more intense retail trading volume. Finally, we find that revision-related retail trading is more intense and better predicts future returns for revisions with more revision-related Twitter discussion. However, we observe no such evidence for institutional investors who typically have direct access to sell-side research. Overall, our results suggest that Twitter is an important channel in facilitating price discovery following analyst revisions, particularly among retail investors.

Using events of high-profile U.S. accounting frauds, we examine investor reactions of more than 18,000 unrelated firms across 40 countries.  Despite having no direct economic ties with the U.S. fraud firm whether at the firm, industry or country level, unrelated firms experience stock price decreases during the fraud events. The negative stock price reaction is most evident among firms with higher accruals, indicating investor apprehension about potential earnings overstatements. Examining how country-level characteristics shape investor reactions worldwide, we find that the negative reactions are moderated by legal institutions such as penalties on misstatements, infrastructure facilitating news flows, and perceived similarities in accounting and auditing practices. Analyses examining high-profile accounting frauds in countries outside of the U.S. confirm the global contagion of accounting concerns. Overall, our findings highlight a far-reaching contagion effect where accounting frauds, even from an unrelated source in one country, can trigger worldwide investor concerns about similar accounting failures elsewhere.

Social media-fueled retail trading poses new risk to institutional investors. I develop a model to analyze the origin and pricing of this risk. Retail investors participate in a social network with concentrated linkages, which means their idiosyncratic sentiment shocks can lead to aggregate fluctuations in retail sentiment. These fluctuations shift investor composition, which in turn determines the price of retail sentiment risk. I calibrate the model to match price, quantity, and sentiment dynamics around the January 2021 short squeeze. Using the calibrated model, I quantify the impact of evolving social network topology on asset prices.