Published Papers

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.

We study the informational value of trading networks in over-the-counter (OTC) markets. Using detailed transaction-level data from the corporate bond market, we show that investors with larger dealer networks make superior trading decisions before changes in credit fundamentals and yield better risk-adjusted performance. We trace these investors' superior trading decisions to trading connections where dealers are most likely to have access to novel credit-relevant information, supporting the interpretation that these investors obtain private information through their larger trading networks. Collectively, our evidence highlights the importance of trading relationships for investors' private information acquisition.

We review an empirical literature that studies how political polarization affects financial decisions. We first discuss the degree of partisan segregation in finance and corporate America, the mechanisms through which partisanship may influence financial decisions, and available data sources to infer individuals' partisan leanings. We then describe and discuss the empirical evidence. Our review suggests an economically large and often growing partisan gap in the financial decisions of households, corporate executives, and financial intermediaries. Partisan alignment between individuals explains team and financial relationship formation, with initial evidence suggesting that high levels of partisan homogeneity may be associated with economic costs. We conclude by proposing several promising directions for future research.

Using data from The Motley Fool's social prediction platform CAPS we document substantial differences in stock predictions across investment horizons. Short- and long-horizon investors respond differently to macroeconomic events and firm news announcements. At the onset of the Covid-19 pandemic the sentiment of short-horizon predictions became sharply more negative while long-horizon predictions remained optimistic. Short-horizon investors also react more than twice as strongly as long-horizon investors to earnings surprises and technical view events. Around acquisition rumors short- and long-horizon investors update in opposite directions about the target: short-term investors become more optimistic while long-term investors become more pessimistic. Motivated by these findings we develop a firm-day measure of horizon disagreement spanning from 2006 to 2022 and find it relates significantly to abnormal trading. Additionally the disagreement-trading relation strengthens on earnings announcement days providing new evidence on the role of model disagreement.

We study how political beliefs influence institutional investors' portfolio choices and asset prices, using a novel dataset linking fund manager partisanship to holdings. Following Donald Trump's surprise 2016 election, Republican-majority mutual fund teams sharply increased purchases of high beta stocks and raised equity exposure by approximately 2%, partly financed by elevated inflows. These reallocations affected asset prices: high-beta stocks more exposed to Republican mutual funds earned abnormal returns roughly 25 basis points higher per month. These returns are not explained by firm- or industry-specific news. Finally, Republican-majority fund Sharpe ratios declined, consistent with a deterioration in performance.

This paper examines how beliefs and preferences drive identity-conforming consumption or investments. We introduce a theory that explains how identity distorts individuals' beliefs about potential outcomes and imposes psychic costs on benefiting from identity- incongruent sources. We substantiate our theoretical foundation through two lab-in- field experiments on soccer betting in Kenya and the UK, where participants either had established affiliations with the teams involved or assumed a neutral stance. The results indicate that soccer fans have overoptimistic beliefs about match outcomes that align with their identity and bet significantly higher amounts on those than on outcomes of comparable games where they are neutral. After accounting for individuals' beliefs and risk preferences, our structural estimates reveal that participants undervalue gains from identity-incongruent assets by 9% to 27%. Our counterfactual simulations imply that identity-specific beliefs account for 30% to 44% of the investment differences between neutral observers and supporters, with the remainder being due to identity preferences.

We investigate the impact of peer interaction on the quality of financial decision making in a laboratory experiment. Face-to-face communication with a randomly assigned peer significantly improves the quality of subsequent private decisions even though simple mimicry would have the opposite effect. We present evidence that the mechanism involves general conceptual learning (because the benefits of communication extend to previously unseen tasks), and that the most effective learning relationships are horizontal rather than vertical (because people with weak skills benefit most when their partners also have weak skills). The benefits of demonstrably effective financial education do not propagate to peers.

Ideology often shapes belief formation, which is central to asset pricing. However, the role of ideological narratives as a source of asset pricing risk remains largely unexplored. Using cryptocurrencies as a laboratory, I examine the role of two ideological narratives-anarchism and decentralization-in the cross-section of cryptocurrency returns. Leveraging social media data and large language models to measure ideology dynamics, I find that fluctuations in these two ideology dynamics are priced in the cross-section of cryptocurrency returns. A two-factor model based on ideological narratives explains the cross-section of cryptocurrency returns better than a three-factor model of crypto market, size, and momentum. Positive shocks to ideology salience are associated with a significant positive spread between more ideology-aligned and less aligned cryptocurrencies, indicating a relative increase in demand for more aligned cryptocurrencies when collective attention to ideological narratives heightens. Consistent with the view that factors proxy for state variables, ideology factors contain distinct information about future crypto market returns and user network growth. Neither investor sentiment nor attention explains the results of the ideology factors. Moreover, the role of ideological narratives extends beyond cryptocurrencies. Stocks with greater exposure to the anarchism narrative yield abnormally high returns that cannot be explained by common stock factor models. The results highlight how ideological narratives contribute to the emergence and adoption of new assets.

Using novel data of social interactions and individual trading records in the Bitcoin market, we document evidence of social learning which leads to sentiment contagion. Investors significantly update their beliefs about Bitcoin in the same direction of average peer sentiment although it is not informative about future price. Our findings indicate inefficiency in social learning, consistent with echo chamber effect and selective interpretation of signals. Moreover, social learning affects both individuals' trading decisions and aggregate market outcomes. We construct a novel measure for the intensity of sentiment contagion due to social learning. It significantly predicts Bitcoin volatility, volume and crash.

We examine the contagion of investment ideas in a multiperiod setting in which investors are more likely to transmit their ideas to other investors after experiencing higher payoffs in one of two investment styles with different return distributions. We show that heterogeneous investment styles are able to coexist in the long run, implying a greater diversity than predicted by traditional theory. We characterize the survival and popularity of styles in relation to the distribution of security returns. In addition, we demonstrate that psychological effects such as conformist preference can lead to oscillations and bubbles in the choice of style. These results remain robust under a wide class of replication rules and endogenous returns. They offer empirically testable predictions, and provide new insights into the persistence of the wide range of investment strategies used by individual investors, hedge funds, and other professional portfolio managers.

We examine an undercover Securities and Exchange Commission (SEC) investigation into the manipulation of financial news on social media. While fraudulent news had a direct positive impact on retail trading and prices, revelation of the fraud by the SEC announcement resulted in significantly lower retail trading volume on all news, including legitimate news, on these platforms. For small firms, volume declined by 23.5% and price volatility dropped by 1.3%. We find evidence consistent with concerns of fraud causing the decline in trading activity and price volatility, which we interpret through the lens of social capital, and attempt to rule out alternative explanations. The results highlight the indirect consequences of fraud and its spillover effects that reduce the social network's impact on information dissemination, especially for small, opaque firms.

After the run on Silicon Valley Bank (SVB) in March 2023, U.S. regional banks entered a period of significant distress. We quantify social media's role in this distress using comprehensive Twitter data. During the SVB run period, banks with high pre-existing exposure to Twitter lost 4.3 percentage points more stock market value. Moreover, Twitter pre-exposure interacts significantly with classical run risks to predict greater run severity and greater deposit outflows during Q1-2023, effects unexplained by other banking or market characteristics. At the hourly frequency during the run, high Twitter attention over the past four hours predicts stock market losses, especially for banks with high run risks. By contrast, we find that negative Twitter sentiment does not amplify bank run risks. Rather, our evidence points to a distinctive role of Twitter attention, particularly when tweets are retweeted broadly.