Published Papers

Honest and dishonest behaviors may both diffuse among the members of an organization. Knowing which of the two spreads faster is important because it impacts the extent to which managers will need to resort to other, potentially more costly solutions to curb dishonest behavior. Assessing empirically which of honest behavior or dishonest behavior spreads faster is challenging because this requires field measurements of social relationships and dishonest behavior of individual members, which poses both measurement and inference problems. We examine an original fine-grained data set from a large company that allows for identifying agents likely to be dishonest and interactions among employees while offering a natural experiment that circumvents the inference problems associated with identifying peer-to-peer diffusion. We find (1) that dishonest behavior diffuses, whereas honest behavior does not; (2) that diffusion likely operates through spreading information about opportunities for collusion; and (3) that policies that screen on dishonesty at hiring may be efficient to curb dishonest behavior in environments with high turnover.

We investigate peer effects associated with a user's churn and subsequent retention of other users in the network. We use data from an upscale gym with information on exogenous churns of users that allows us to identify peer effects. We estimate a structural model of users' plan choice and usage decisions that allows for bidirectionality in peer effects, direct/indirect peer effects, heterogeneity in the degree of centrality of users, and dynamics in peer effects. Our main results are as follows. (i) Unlike the churn of a low- or medium-centrality user, the churn of a high-centrality user has a strong negative cascade effect on the usage and subscriptions of other users in the network over time. (ii) To stop this negative cascade effect and increase the retention in the rest of the network, the firm's decision on which of the central churner's peers to target with promotions depends on the tradeoff between two forces: the individual level impact of the targeted promotion versus the positive spillover effect to the rest of the network that can increase over time because of carryover effects of usage and subsequent social multiplier effects (that stem from bidirectionality in peer effects). Thus, retention strategies focused on short-term outcomes should prioritize targeting users with low centrality, whereas strategies aimed at long-term outcomes should prioritize targeting users with high centrality.

We develop a dynamic model of na??ve social learning from consumer reviews. In our model, consumers decide if and what to buy based on both the products' expected quality and their idiosyncratic taste for them. Products' qualities are initially unknown and are (mis)learned from reviews. At the heart of the model lies a dynamic feedback loop between reviews, beliefs, and choices: period t reviews influence t+1 consumers' beliefs and, thus, choices; these determine the average of t+1 reviews, which, in turn, influences t+2 beliefs, choices, and reviews. We show that, in the long run (t=), reviews are systematically biased, leading some consumers astray. In particular, in both monopoly and duopoly, reviews relatively advantage lower quality and more polarizing products because these products induce stronger taste-based consumer self-selection. Thus, in stark contrast with the winner-takes-all dynamics of classic observational learning models in which consumers learn from the choices of their predecessors, social learning from opinions generates excessive choice fragmentation. Our findings have implications for interpreting the variance and number of reviews, pricing in the presence of reviews, and the short- and long-term effectiveness of fake reviews.

We present evidence that loan allocations and loan terms are closely linked to the strength of social connections between bank and borrower regions. Lending increases with social connectedness, particularly in the presence of strong screening incentives. If connectedness is high, banks discriminate between borrowers more, the terms of originated loans are more borrower friendly, and loan performance is better. Furthermore, social connectedness is associated with higher bank profitability and with lending-related increases in borrower-region GDP growth and employment. Our results suggest that lending barriers decrease with social connectedness, which helps our understanding of geographic lending patterns and regional economic disparities.

Media tone constructed from 7,000,000 articles from 2,000 global media and 800 social media sites is found to be a genuine risk factor that cross-sectionally prices currencies. It can predict excess US dollar returns for up to six months and surpasses the no-change benchmark in predicting returns out of sample. Its predicted value contains information beyond those predicted by currency factors and business cycles. Evidence collaborates with the theory that Media tone increases investment returns, has pronounced predictive power for the currencies associated with hard-to-value characteristics, and its predictive power increases with media sources. Trading of rational investors, including banks, is associated with Media tone.

We examine how social norms measured by religiosity influence institutional investors in the stock lending market. We find that firms with higher average local religiosity of their block institutional investors are associated with higher utilization rates, lower supply, but not higher demand of lendable shares. Such firms have higher stock lending fees and likelihoods of becoming special. A higher short interest, when coupled with higher blockholder religiosity, is a stronger predictor of lower stock returns. Our findings suggest that the social norms of institutional investors act as an origin for limits to arbitrage that impede market efficiency through stock lending.

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 investigate whether herding behaviour of bond funds influences corporate bond issuance. Using quarterly data from 1998 to 2018, we find that only buy herding significantly increases bond issuance activity. Further analysis supports an informational channel, suggesting that 'investigative herding' among funds enhances credit information efficiency more significantly than demand-driven price impacts. Our main identification strategy employs an instrumental variable approach, based on the share of  inexperienced managers among fund bondholders. 

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.