Working Papers

We examine how participation in a microfinance program diffuses through social networks. We collected detailed demographic and social network data in 43 villages in South India before microfinance was introduced in those villages and then tracked eventual participation. We exploit exogenous variation in the importance (in a network sense) of the people who were first informed about the program, "the injection points". Microfinance participation is higher when the injection points have higher eigenvector centrality. We estimate structural models of diffusion that allow us to (i) determine the relative roles of basic information transmission versus other forms of peer influence, and (ii) distinguish information passing by participants and non-participants. We find that participants are significantly more likely to pass information on to friends and acquaintances than informed non-participants, but that information passing by non-participants is still substantial and significant, accounting for roughly a third of informedness and participation. We also find that, conditioned on being informed, an individual's decision is not significantly affected by the participation of her acquaintances.

This paper develops an empirical and theoretical case for how `hype' among retail investors can drive large asset price fluctuations. We use text data from discussions on WallStreetBets (WSB), an online investor forum with over eleven million followers as of February 2022, as a case study to demonstrate how retail investors influence each other, and how social behaviors impact financial markets. We document that WSB users adopt price predictions about assets (bullish or bearish) in part due to the sentiments expressed by their peers. Discussions about stocks are also self-perpetuating: narratives about specific assets spread at an increasing rate before peaking, and eventually diminishing in importance -- a pattern reminiscent of an epidemiological setting. To consolidate these findings, we develop a model for the impact of social dynamics among retail investors on asset prices. We find that the interplay between 'trend following' and 'consensus formation' determines the stability of price returns, with socially-driven investing potentially causing oscillations and cycles. Our framework helps identify components of asset demand stemming from social dynamics, which we predict using WSB data. Our predictions explain significant variation in stock market activity. These findings emphasize the role that social dynamics play in financial markets, amplified by online social media.

We provide causal evidence of the peer effect on investment in a large-scale natural experiment. We show that retail investors respond to the investment decisions of their military peers who were randomly assigned in compulsory military drafts: retail investors participate more in the stock market, invest more in stocks that peers hold, and perform better. Our investigation indicates that retail investors learn valuable information from their peers to make profitable investment decisions. These effects are more pronounced among peers who are more sophisticated and among stocks entailing less behavioral bias. Stocks with more peer clientele outperform stocks with less clientele.

A key development in social media has been the remarkable growth of influencers and their increasing use by firms to manage their online presence and image, and to promote their products. Despite the huge growth of influencers and their use by firms, there is a lack of analysis of social media influencers and their impact on the financial market performance of firms. Anecdotal evidence suggests mega influencers are able to affect the stock prices of firms via social media. We ask whether the effect on stock prices identified in anecdotal evidence is generalizable to all mega influencers and other financial market characteristics of firms. After developing hypotheses from the Noise Trader model and using a hand collected dataset of more than 11,000,000 mega influencer posts on Instagram (2012-2019), we find that mega influencers affect investors' attention, volatility, trading volume and, through extreme sentiment posts, stock returns. The effect on returns is, however, very short lived. Companies need to be aware of these stock market consequences if they intend to use influencers for external image purposes and/or product promotion.

The capacity to aggregate information from diverse perspectives has positioned social finance forums as a potent source of signals that shape investors' beliefs. We study the Seeking Alpha forum to determine if female contributors face a more hostile environment than males and the consequences for effective information aggregation. We find that although male and female contributors display similar abilities, female-authored perspectives receive significantly lower engagement and trust from platform users. Females also face more heightened disagreement and attract more online trolls. This combative environment results in more female contributors quitting the platform, eroding the informativeness of the platform consensus, and implies relatively lower financial compensation for female contributors.

We present a TriSNAR modeling framework for understanding the dynamic interactions of multiple markets for Bitcoin trading, including market efficiency, and for identifying influential exchanges in the global trading network. We are particularly interested in identifying exchanges that are market leaders. Out of 339 weeks (6.5 years of data), we identify 104 weeks in which TriSNAR provides the best MSFE out of 6 contestants and significantly outperforms all other models. Among 194 Bitcoin exchanges, we find that exchange Kraken was the leading exchange prior to the market frenzy of 2017, in particular in 2016. In addition, price discovery shows that the Bitcoin exchange networks efficiency decreased from 2015 to 2017, and increased since 2018. We analyse the relation between blockchain fund flows and influential exchanges, and observe that wealthy holders of Bitcoin transact funds to exchanges when influential exchanges arise. We investigate the finite sample and asymptotic properties of TriSNAR. Compared to alternative methods, TriSNAR outperforms in terms of accuracy and ability to discover multi-market network structures.

Tweet-level data from a social media platform reveals low average accuracy and high dispersion in the quality of advice by financial influencers, or "finfluencers": 28% of finfluencers are skilled, generating 2.6% monthly abnormal returns, 16% are unskilled, and 56% have negative skill ("antiskill") generating -2.3% monthly abnormal returns. Consistent with homophily shaping finfluencers' social networks, antiskilled finfluencers have more followers and more influence on retail trading than skilled finfluencers. The advice by antiskilled finfluencers creates overly optimistic beliefs most times and persistent swings in followers' beliefs. Consequently, finfluencers cause excessive trading and inefficient prices such that a contrarian strategy yields 1.2% monthly out-of-sample performance

In today's meme-riddled stock market, how viable do traditional theories of information exchange and price discovery remain? The conventional understanding of stock market price discovery focuses on the exchange of information, typically tied to the present value of an issuer's future cash flows, between traders. This paper explores the impact of "finfluencers"-those who wield outsize influence on investing decisions through social media-on this understanding. Finfluencers increasingly broker stock market information. Social media makes doing so easier than ever before. This paper explores two implications of the rise of finfluencers. First, finfluencers are not solely motivated to seek out fundamental value information and trade to profit off of it. Instead, they try to maximize popularity, be entertaining, and "grow their brand," among other motivations. Because they mediate the information that reaches retail investors and provide powerful coordination mechanisms across those investors, finfluencers' influence shapes the types of "information" and motivations that are reflected in stock price movements. Second, the more influence finfluencers wield, the more they can predict and even control trading patterns among their followers. From a finfluencer's perspective, stock price movements can become more predictable, which can weaken finfluencers' incentives to provide valuable information to their followers and make profiting at the expense of their followers more tempting.

We document widespread use of personal financial advice among retail investors. Individuals seek competent and trusted sources for financial advice among their family and friends. Investors who provide advice to family and friends are positively selected and emphasize the reputational costs of giving risky financial advice. While previous studies have shown that advice shared on social media promotes active trading, we show that personal financial advice encourages investing in funds over single stocks. Our evidence complements the existing literature on financial advice in online social networks by highlighting differences in incentives and outcomes of advice to close personal connections.

What are the aggregate and distributional consequences of the relationship between an individual?�?s social network and financial decisions? Motivated by several well-documented facts about the influence of social connections on financial decisions, we build and calibrate a model of stock market participation with a social network that emphasizes the interplay between connectivity and network structure. Since connections to informed agents help spreading information, there is a pivotal role for homophily. An increase in the average number of connections raises the average participation rate, mostly due to richer agents. Higher homophily benefits richer agents by creating clusters where information spreads more efficiently. We show empirical evidence consistent with the importance of connectivity and sorting. We discuss several new avenues for future research into the aggregate impact of peer effects in finance.

We examine the simultaneous peer effects of co-workers, family, and neighbors in financial behavior using Danish registry data. We find that neighbors exert the strongest influence, followed by co-workers and family members. Peer effects are stronger for stocks than for mutual funds, and among experienced investors. While co-workers primarily influence buying decisions, neighbors affect both buying and selling, suggesting distinct channels of influence across peer groups. A multi-layer network model formalizes our empirical results, showing that an investor?�?s trading activity depends on her centrality within and across network layers. Our findings provide new insights into the drivers and implications of peer effects in financial markets.

This paper studies how investors use generative AI in discussions across two major investing social media platforms with distinct governance and user bases: Seeking Alpha and Reddit's r/WallStreetBets. We document sharp cross-platform differences in adoption and market outcomes. On Seeking Alpha, AI adoption arises when information is scarce or contributors cover unfamiliar stocks; it is associated with more informative retail order flows, reduced user disagreement, and narrower bid-ask spreads. On WallStreetBets, AI adoption rises following surges in retail buying and is linked to sentiment contagion. Adoption is also followed by higher abnormal trading volume and volatility, wider spreads, and lottery-like return distributions. These results indicate that the adoption of AI and its relation to market outcomes are shaped by the institutional and behavioral context in which it is deployed.