Published Papers

Using IRS tax filing data, I show that social network and word-of-mouth communications play an important role in stock market participation decisions. Using a novel dataset from Facebook, I construct a measure of social network friends' participation for US counties and find that a one-percentage point increase in friends' participation increases the focal county participation by 14 to 25 basis points in the following year. For identification strategy, I employ the revelation of financial misconducts as an exogenous negative shock to local participation rate and show that the instrumented change in friend participation significantly and positively predicts the change in focal county participation rates. The increase in participation rates among the low-income households induced by friends' participation decreases the Gini coefficients in metropolitan counties in the following two years. The evidence suggests that social influences and peer effects contribute to the cross-sectional differences in the stock market participation rates across US counties and may lead to lower income inequality.

This paper examines the causal role of face-to-face (F2F) interactions in providing informational advantages to mutual fund managers. Using COVID-19 lockdowns as an exogenous shock, I show that fund managers' performance on local stocks declined relative to distant stocks when in-person meetings were curtailed, driven by impaired investment timing rather than changes in fundamentals. I investigate two distinct benefits of F2F interaction facilitated by interpersonal cues: trust-building, which enhances the transmission of soft information, and impression management, which facilitates managers' tendency to share favorable information. The results cannot be fully explained by changes in internal information flow or the use of public information, and are more pronounced for stocks in less transparent information environments and in regions with stronger social traits.

We study the nature and effects of cultural biases in choice under risk and uncertainty by comparing peer-to-peer loans the same individuals (lenders) make alone and after observing robo-advised suggestions. When unassisted, lenders are more likely to choose co-ethnic borrowers, facing 8% higher defaults and 7.3pp lower returns. Robo-advising does not affect diversification but reduces lending to high-risk co-ethnic borrowers. Lenders in locations with high inter-ethnic animus drive the results, even when borrowers reside elsewhere. Biased beliefs explain these results better than a conscious taste for discrimination: lenders barely override robo-advised matches to ethnicities they discriminated against when unassisted.

I review the empirical literature on word of mouth (WOM) among investors. I begin with an outline of the empirical challenges that WOM research faces and possible strategies for overcoming those challenges. I then discuss recent studies on WOM among retail and institutional investors. The research to date provides compelling evidence that WOM importantly determines investment decisions. On balance, the information transmitted through WOM does not appear to help investors make better investment decisions. I explore possible reasons. I also discuss potential asset-pricing implications, the emergence of social technologies, and possible avenues for future research.

We study how intercommunity social networks influence demand and supply dynamics in fintech lending platforms. Demand for online loans rises following increases in online borrowing activity in geographically distant but socially connected areas. On the supply side, borrower-area social proximity to deposit-rich regions enhances funding likelihood and is associated with better ex-post loan performance. We establish causality with instrumental variables obtained from natural disasters (demand-side) and financial adviser misconduct (supply-side). We further show that social connectedness is particularly effective in expanding both loan demand and supply in disadvantaged communities, without increasing delinquency rates. These results are consistent with intercommunity social networks raising awareness of alternative lending platforms and transmitting otherwise hard-to-obtain information that mitigates community-level information asymmetry. Although our findings suggest that social networks improve capital allocation and expand credit access for underserved communities, the welfare benefits are muted for low FICO score individuals.

We find evidence of selective exposure to confirmatory information among 400,000 users on the investor social network StockTwits. Self-described bulls are five times more likely to follow a user with a bullish view of the same stock than are self-described bears. Consequently, bulls see 62 more bullish messages and 24 fewer bearish messages than bears do over the same 50-day period. These "echo chambers" exist even among professional investors and are strongest for investors who trade on their beliefs. Finally, beliefs formed in echo chambers are associated with lower ex post returns, more siloing of information, and more trading volume.

In this article, we examine whether comovement in the stock prices of pairs of Chinese firms connected to the same political network are systematically shaped by the prevailing coordination versus competition incentives of that network's politicians. We find strong evidence from 2000 to 2012 (Jiang's and Hu's regimes) that stock price comovement is affected by the embeddedness of the firm-politician ties within the network. Among pairs of firms connected to a network through a common politician, we document an increase in stock price comovement. For those pairs of firms connected to a common network via separate politicians (rather than a common politician), we document a relative decrease in stock price comovement. This negative effect suggests that politicians' relationships within these political networks are generally adversarial rather than cooperative in nature. These results become significantly weaker during Xi's regime from 2013 to 2017, suggesting that Xi's anti-corruption campaign and state-owned enterprise reforms may have attenuated these political network effects on the firms. Our additional tests also show that stock price comovement becomes even more positive (negative) in settings which are expected to increase the coordination or decrease the competition (decrease the coordination or increase the competition) of the politicians.

Using hand-collected data spanning more than a decade on European banks' sovereign debt portfolios, we show that the trust of residents of a bank's countries of operation in the residents of a potential target country of investment has a positive, statistically significant, and economically important association with its cross-border exposures. In identifying cultural stereotypes at the bank level, we show that corporate culture at bank headquarters is influenced by foreign subsidiaries for several reasons, including banks' tendency to hire internally across borders for high-level managerial positions. We therefore leverage the geography of multinational bank branch networks to construct a bank-specific measure of culture that differs across banks headquartered in the same country, at the same point in time, with regard to the same target country. This allows us to compare how sovereign exposures are affected by cultural stereotypes while ruling out confounding factors at country and country-pair levels. The effect of stereotypes is persistent overtime, stronger for less diversified banks, and weaker for target countries whose bonds appear more frequently in bank portfolios. Cultural stereotypes are particularly salient when governments are hit by sovereign debt crises.

Using machine learning-based algorithms, we measure key impressions about sell-side analysts using their LinkedIn photos. We find that impressions of analysts' trustworthiness (TRUST) and dominance (DOM) are positively associated with forecast accuracy, especially after recent in-person meetings between analysts and firm managers. High TRUST also enhances stock return sensitivity to forecast revisions, especially for stocks with high institutional ownership. In contrast, the impression of analysts' attractiveness (ATTRACT) is only positively associated with accuracy for new analysts or when a firm has a new CEO or CFO. Furthermore, while high DOM helps male analysts' chances of attaining All-Star status, it reduces female analysts' accuracy and the likelihood of winning the All-Star award. In addition, the relation between TRUST and accuracy is modulated by the disclosure environment and is attenuated by Regulation Fair Disclosure. Our results suggest that face impressions influence analysts' access to information and the perceived credibility of their reports.

We analyze the partisanship of Commissioners at the SEC and Governors at the Federal Reserve Board. Using recent advances in machine learning, we identify partisan phrases in Congress, such as "red tape" and "climate change," and observe their usage among regulators. Although the Fed has remained relatively nonpartisan throughout our sample period (1930-2019), we find that partisanship among SEC Commissioners rose to an all-time high during the 2010-2019 period, driven by more-partisan Commissioners replacing less-partisan ones. Partisanship at the SEC appears in both the language of new SEC rules and the voting behavior of SEC Commissioners.

Maintaining economic output during the COVID-19 pandemic results in benefits for firm shareholders but comes at a potential cost to public health. Using store-level data, we examine how a CEO's political leaning impacts this trade-off. We document that firms with a Republican-leaning CEO experience a relative increase in store visits compared to firms with a Democratic-leaning CEO. The increase in store visits is associated with higher sales and positive abnormal stock returns. However, we also document higher COVID-19 transmission rates and more employee safety complaints in communities where establishments with higher store traffic are managed by a Republican-leaning CEO.

Epidemiological models of belief formation put social interactions at their core; such models are widely used by scholars who are not economists to study the dynamics of beliefs in populations. We survey the literature in which economists attempting to model the consequences of beliefs about the future - expectations - have employed a full-fledged epidemiological approach to explore an economic question. We draw connections to related work on contagion, narrative economics, news/rumor spreading, and the spread of internet memes. A main theme of the paper is that a number of independent developments have recently converged to make epidemiological expectations (EE) modeling more feasible and appealing than in the past.