Working Papers

The well-documented rise in political polarization among the U.S. electorate over the past 20 years has been accompanied by a substantial increase in the effect of partisan bias on survey-based measures of economic expectations. Individuals have a more optimistic view on future economic conditions when they are more closely affiliated with the party that controls the White House, and this tendency has increased significantly over time. Individuals report a large shift in economic expectations based on partisan affiliation after the 2008 and 2016 elections, but administrative data on spending shows no effect of these shifts on actual household spending.

We investigate whether there are temporary valuation impacts on stocks that media outlets list as involved in a major sporting event (the summer Olympics). We examine five summer Olympics and identify stocks that media outlets hype as benefiting from the Olympics (Olympic stocks). We find that Olympic stocks exhibit increases in comovement of returns after the announcement of the winning bid and declines in comovements after the games are played, consistent with the Olympics being used by investors as a category for investment. Furthermore, Olympic stock returns outperform their matched counterparts over this time period. If the comovement and valuation benefits are due to changes in underlying economics then we expect to observe corresponding increases in comovements of fundamentals and improvements in profitability. However, we find no observable changes in fundamental comovements or profitability. Consistent with investor sentiment driving the categorization, we find that Olympic firms with a greater retail investor presence have stronger comovements effects; and trading volume and volatility are abnormally high for Olympic firms on days where media outlets have stories linking the firm to the Olympic games. To clarify event-based categorization occurs in other settings where media outlets classify stocks for investment, we show comovement increases for stocks classified as "Stay-at-Home" by analysts and the media and "Meme" by retail investors on the Reddit social media platform.

We show that firms anticipate information spillover from peers' investment decisions and delay project exercise to learn from them. While this information improves project selection, the cost of waiting erodes these gains. To establish causality, we exploit local exogenous variation from the 1800s that shapes the number of peers that a firm can learn from today. The effect is most salient when information is scarce, costs of waiting are low, projects have low expected profitability, and the source information is more relevant. Finally, the anticipation of spillovers dampens aggregate investment, suggesting a role for this mechanism in macro-investment models.

We document that the recent house price experiences within an individual's social network affect her perceptions of the attractiveness of property investments, and through this channel have large effects on her housing market activity. Our data combine anonymized social network information from Facebook with housing transaction data and a survey. We first show that in the survey, individuals whose geographically-distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends. Based on these findings, we introduce a new methodology to document large effects of housing market expectations on individual housing investment decisions and aggregate housing market outcomes. Our approach exploits plausibly-exogenous variation in the recent house price experiences of individuals' geographically-distant friends as shifters of those individuals' local housing market expectations. Individuals whose friends experienced a 5 percentage points larger house price increase over the previous 24 months (i) are 3.1 percentage points more likely to transition from renting to owning over a two-year period, (ii) buy a 1.7 percent larger house, (iii) pay 3.3 percent more for a given house, and (iv) make a 7% larger downpayment. Similarly, when homeowners' friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We also find that when individuals observe a higher dispersion of house price experiences across their friends, this has a negative effect on their housing investments. Finally, we show that these individual-level responses aggregate up to affect county-level house prices and trading volume. Our findings suggest that the house price experiences of geographically-distant friends might provide a valid instrument for local house price growth.

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.