Published Papers

We analyze online stock posts to identify dynamic intercity investment preferences among Chinese investors. By inferring city connections using recent posts on local stocks mentioning other cities, we find that firms in highly connected cities exhibit higher stock valuations, greater turnover, higher idiosyncratic volatility, improved liquidity, and reduced crash risk. The network effects are more pronounced among less visible firms and induce intercity return comovement. Better stock performances in connected cities predict subsequent local stock return reversals as well as elevated intercity retail block trading. Our findings suggest that city connectivity, revealed through social media content, influences firm outcomes, investor behavior, and market efficiency.

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.

We unveil asset managers' social media communications as a distinct new channel for attracting flows of money to mutual funds. Combining a database of more than 1.6 million posts on X/Twitter by U.S. mutual fund families with textual analysis, we find that flows of money to mutual funds respond positively to both the number and tone of the posts. While the link between social media communications and flows of money is not explained by conventional marketing efforts, our findings suggest that the social media channel is not independent from asset management companies' broader marketing strategies. A high-frequency analysis that exploits intraday ETF trade data allows us to isolate the effect of tweets on investor decisions from potential confounders. We then consider and test four different economic mechanisms. The results of these tests do not support the hypothesis that asset managers' social media communications reduce search costs for potential investors. The results do not support, either, that asset management companies' Twitter activity increases investor attention or alleviates information asymmetries by communicating performance-relevant information to investors. In contrast, our evidence suggests that asset managers use social media as an effective persuasion tool.

Using the reconstitution of MSCI indices in 56 markets worldwide from 2006 to 2023, we discover arbitrage opportunities arising from index-tracking investors' rigidity to minimize tracking errors around the dates when index reconstitution changes become effective (i.e., effective dates). We document pronounced abnormal returns and trading volume on the last trading day before the effective date. Arbitrageurs can exploit this predictable pattern of stock price changes and earn sizable abnormal returns if they long the added and short the deleted stocks on the announcement date and close their positions at the end of the day before the effective date. Additional analysis reveals how index-tracking investors and arbitragers interact to shape stock prices and equity-lending activities around MSCI index reconstitutions.

Fraud indicators in the Paycheck Protection Program (PPP) COVID relief program are highly geographically concentrated. Zip codes and counties with high rates of suspicious PPP loans exhibit strong social connections to one another with evidence of fraud spreading spatially over time through social connections. Individuals in suspicious social media groups have higher rates of PPP fraud, and socially connected zip codes frequently use the same specific FinTech lenders, consistent with social connections influencing particular loan decisions. Our findings suggest that more proactive data analysis in fraud prevention, detection, and prosecution is needed to prevent the social spread of fraudulent schemes.

Can social norms give rise to distorted information in China? We observe that China's leading social norm related to alcohol consumption and social drinking enhance earnings management. An analysis of toxic alcohol scandals supports a causal interpretation. Further evidence suggests that the influence of alcohol may come from the negative externality that it creates, which is propagated by corporate leaders and cannot be attenuated by market-oriented institutions. Our results reveal a social norm externality that may have important normative implications.

As emerging FinTech platforms face pressure in efficiently managing credit risk, the human emotional spectrum of FinTech platform borrowers within social media becomes a potential source for gaining insight into and evaluating their financial behaviors. Collaborating with an Asian FinTech platform, we investigate the impact of social media emotions on a platform's loan-approval decisions and repayment-reminder interventions before due dates. We demonstrate that anger at the pre-approval stage has a U-shaped relationship with platform borrowers' default probability. We reveal what we call "a bright side of anger" with respect to curbing financial credit risk: moderate intensity of anger at the pre-approval stage suggests a lower loan default probability. We also find that the average happiness tendency of platform delinquent borrowers' at the pre-maturity stage becomes informative and valuable, as it shows a U-shaped relationship with loan default; as for anger, it does not work therein. Furthermore, our field experiment indicates that a positive-expectation reminder is useful for prompting repayment when delinquent borrowers are in strong emotional intensities, regardless of anger or happiness. However, a negative-consequence reminder results in a higher default probability for delinquent borrowers who maintain high immediate happiness before the loan maturity dates. We draw on the classical appraisal theory of emotions and the feelings-as-information theory to interpret our findings. We offer non-trivial theoretical and practical implications to support FinTech platform credit risk decision-making by investigating the value of social media emotions and advocating for cross-functional coordination between debt approval and debt collection departments.

We measure popular sentiment toward finance by applying a large language model to millions of books published in eight countries over hundreds of years. We extensively validate this measure both internally and externally. We document persistent differences in finance sentiment across countries despite ample time-series variation. Books written in the languages of more capitalist countries discuss finance in a more positive context. Finance sentiment is correlated with survey-based measures of financial market participation and income inequality. Finance sentiment declines one year before rather than after financial crises. Positive shocks to finance sentiment are followed by higher output and credit growth.

We educate investors about the benefits of dividend reinvestment and costs of misperceiving dividends as free income. The intervention increases planned dividend reinvestment in survey responses. Using trading records, we observe a causal increase in dividend reinvestment in the field of roughly 50 cents for every euro received. This holds relative to investors' prior behavior and various control samples. Investors who learned the most from the intervention update their trading the most. The results suggest the free dividends fallacy is a significant source of dividend demand. Our study demonstrates that simple, targeted, and focused educational interventions can affect investment behavior.

Using a semisupervised topic model on 7 million New York Times articles spanning 160 years, we test whether topics of media discourse predict future stock market excess returns to test rational and behavioral hypotheses about market valuation of disaster risk. Media discourse data address the challenge of sample size even when disasters are rare. Our methodology avoids look-ahead bias and addresses semantic shifts. Our discourse topics positively predicts market excess returns, with War having an out-of-sample R^2 of 1.35%. We call this effect the war return premium. The war return premium has increased in more recent time periods.

We identify the crucial role social networks play in crowdfunding markets. Investors are 50% more likely to fund projects that their peers support and are 11.2% more likely to fund projects from regions where they share strong social ties, given a one-standard-deviation change in the variables. More influential peers exert a greater influence, especially in the case of riskier projects, and the peer effects are amplified in crowdfunding platforms that prioritize transparency and accountability. Social ties transmit information about economic conditions in project locations, and they complement the influence of peer effects. Furthermore, the social network effects affect project funding outcomes and can be particularly valuable in mitigating the adverse effects of natural disasters. Our findings suggest that social networks play a significant role in crowdfunding markets by increasing investor awareness, disseminating information, and ultimately influencing capital allocations.