Published Papers

We propose a new measure of private information in decentralized markets-connections-which exploits the time variation in the number of dealers with whom a client trades in a time period. Using trade-level data for the U.K. government bond market, we show that clients perform better when having more connections as their trades predict future price movements. Time variation in market-wide connections also helps explain yield dynamics. Given our novel measure, we present two applications suggesting that (i) dealers pass on information, acquired from their informed clients, to their affiliates, and (ii) informed clients better predict the orderflow intermediated by their dealers.

This study examines whether sell-side equity analysts engage in "social learning" in which their earnings forecasts for certain firms are influenced by the forecasts and outcomes of "peer" analysts associated with other firms in their respective portfolios. We find that analyst optimism is negatively correlated with recent forecast errors, by peers, on other firms in the analyst's portfolio. An analyst is also more likely to issue "bold" forecasts when peers recently issued similar forecasts for other portfolio firms. Analysts learn more from peers with similar personal characteristics. Overall, social learning benefits analysts and improves their forecast accuracy.

Flooding is the most costly natural disaster faced by US households, yet policymakers are puzzled by the low take-up rates for flood insurance. Leveraging novel transaction-level data, this paper studies the influence of social interactions on households' insurance decisions. I show that households increase flood insurance purchases by 1-5 percent when their geographically distant friends are exposed to flooding events or to campaigns for flood insurance. These exogenous shocks to far-away friends should not affect local households' own insurance decisions except through peer effects. I provide evidence suggesting that social interactions facilitate learning through information dissemination and attention triggering.

I present closed-form solutions for prices, portfolios, and beliefs in a model where four types of investors trade assets over time: naive investors who learn via a social network, "fanatics" possibly spreading fake news, and rational short- and long-term investors. I show that fanatic and rational views dominate over time, and their relative importance depends on their following by influencers. Securities markets exhibit social network spillovers, large effects of influencers and thought leaders, bubbles, bursts of high volume, price momentum, fundamental momentum, and reversal. The model sheds new light on the GameStop event, historical bubbles, and asset markets more generally.

Do social networks help firms recruit talented managers? In our setting, firms are randomly connected to prospective young managers through former employees. Under a discrete choice model, we find networks increase the likelihood firms hire high-ability managers, while having no effect on the hiring rate of low-ability managers. Effects are greatest for nonlocal firms, strong ties, and peers living in the same neighborhood. Survey evidence suggests social networks promote recruitment by providing information about firm fundamentals to potential applicants. Our results help rationalize why the majority of managers hold prior connections to the firm.

We show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties but find no evidence that these investments earn a differential return. Firms in regions with stronger social ties to locations with many institutional investors have higher valuations and liquidity. These effects are largest for small firms with little analyst coverage, suggesting that the investors' behavior is explained by their increased awareness of firms in socially proximate locations. Our results highlight that the social structure of regions affects firms' access to capital and contributes to geographic differences in economic outcomes.

Explosive growth in the number of users on various social media platforms has transformed the way firms strategize their marketing activities. To take advantage of the vast size of social networks, firms have now turned their attention to influencer marketing wherein they employ independent influencers to promote their products on social media platforms. Despite the recent growth in influencer marketing, the problem of network seeding (i.e., identification of influencers to optimally post a firm's message or advertisement) neither has been rigorously studied in the academic literature nor has been carefully addressed in practice. We develop a data-driven optimization framework to help a firm successfully conduct (i) short-horizon and (ii) long-horizon influencer marketing campaigns, for which two models are developed, respectively, to maximize the firm's benefit. The models are based on the interactions with marketers, observation of firms' message placements on social media, and model parameters estimated via empirical analysis performed on data from Twitter. Our empirical analysis discovers the effects of collective influence of multiple influencers and finds two important parameters to be included in the models, namely, multiple exposure effect and forgetting effect. For the short-horizon campaign, we develop an optimization model to select influencers and present structural properties for the model. Using these properties, we develop a mathematical programming based polynomial time procedure to provide near-optimal solutions. For the long-horizon problem, we develop an efficient solution procedure to simultaneously select influencers and schedule their message postings over a planning horizon. We demonstrate the superiority of our solution strategies for both short- and long-horizon problems against multiple benchmark methods used in practice. Finally, we present several managerially relevant insights for firms in the influencer marketing context.

Modern communication technologies enable efficient exchange of information but often sacrifice direct human interaction inherent in more traditional forms of communication. This raises the question of whether the lack of personal interaction induces individuals to exploit informational asymmetries. We conducted two experiments with a total of 848 subjects to examine how human versus machine interaction influences cheating for financial gain. We find that individuals cheat about three times more when they interact with a machine rather than a person, regardless of whether the machine is equipped with human features. When interacting with a human, individuals are particularly reluctant to report unlikely and therefore, suspicious outcomes, which is consistent with social image concerns. The second experiment shows that dishonest individuals prefer to interact with a machine when facing an opportunity to cheat. Our results suggest that human presence is key to mitigating dishonest behavior and that self-selection into communication channels can be used to screen for dishonest people.

We study the impacts of social interactions on competing firms' quality differentiation, pricing decisions, and profit performance. Two forms of social interactions are identified and analyzed: (1) market-expansion effect (MEE)-the total market expands as a result of both firms' sales-and (2) value-enhancement effect (VEE)-a consumer gains additional utility of purchasing from one firm based on this firm's previous and/or current sales volume. We consider a two-stage duopoly competition framework, in which both firms select quality levels in the first stage simultaneously and engage in a two-period price competition in the second stage. In the main model, we assume that each firm sets a single price and commits to it across two selling periods. We find that both forms of social interactions tend to lower prices and intensify price competition for given quality levels. However, MEE weakens the product-quality differentiation and is benign to both high-quality and low-quality firms. It also benefits consumers and improves social welfare. By contrast, VEE enlarges the quality differentiation and only benefits the high-quality firm, but is particularly malignant to the low-quality firm. It further reduces the consumers' monetary surplus. Such impact is consistent, regardless of whether the VEE interactions involve previous or current consumers. We further discuss several model extensions, including dynamic pricing, combined social effects, and various cost structures, and verify that the aforementioned impacts of MEE and VEE are qualitatively robust to those extensions. Our results provide important managerial insights for firms in competitive markets and suggest that they need to not only be aware of the consumers' social interactions, but also, more importantly, distinguish the predominant form of the interactions so as to apply proper marketing strategies.

An important challenge for many firms is to identify the life transitions of its customers, such as job searching, expecting a child, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firms opportunities to be more relevant to their customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely to be job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms. Our proposed approach builds on the hidden Markov model (HMM) framework to recover the latent state of job search from noisy signals obtained from social network activity data. Specifically, we use the latent states of the HMM to fuse cross-sectional survey responses to a job-seeking status question with longitudinal user activity data, resulting in a partially HMM. Thus, in some time periods, and for some users, we observe a direct measure of the true job-seeking status. We demonstrate that the proposed model can predict not only which users are likely to be job seeking at any point in time but also what activities on the platform are associated with job search and how long the users have been job seeking. Furthermore, we find that targeting job seekers based on our proposed approach can lead to a 29% increase in profits of a targeting campaign relative to the approach that was used by the social network platform.

Companies often post user-generated reviews online so that potential buyers in different clusters (age, geographic region, occupation, etc.) can learn from existing customers about the quality of an experience good and cluster preferences before purchasing. In this paper, we evaluate two common user-generated review provision policies for selling experience goods to customers in different clusters with heterogeneous preferences. The first policy is called the association-based policy (AP) under which a customer in a cluster can only observe the aggregate review (i.e., average rating) generated by users within the same cluster. The second policy is called the global-based policy (GP) under which each customer is presented with the aggregate review generated by all users across clusters. We find that, in general, the firm benefits from a policy that provides a larger number of "relevant reviews" to customers. When customers are more certain about the product quality and when clusters are more diverse, AP is more profitable than GP because it provides cluster-specific reviews to customers. Otherwise, GP is more profitable as it provides a larger number of less relevant reviews. Moreover, we propose a third provision policy that imparts the union of the information by AP and GP and show that it is more profitable for the firm. Although the third policy always renders a higher consumer welfare than GP, it may generate a lower consumer welfare than AP.

In social trading, less experienced investors (followers) are allowed to copy the trades of experts (traders) in real time after paying a fee. Such a copy-trading mechanism often runs into a transparency-revenue tension. On the one hand, social trading platforms need to release traders' trades as transparently as possible to allow followers to evaluate traders accurately. On the other hand, complete transparency may undercut the platform's revenue because followers can free ride. That is, followers can manually copy the trades of a trader to avoid paying the following fees. This study addresses this critical tension by optimizing the level of transparency through delaying the release of trading information pertaining to the trades executed by traders. We capture the economic impact of the delay using the notions of profit-gap and delayed-profit. We propose a mechanism that elucidates the economic effects of the profit-gap and delayed-profit on followers and, consequently, the amount of money following a trader: protection effect and evaluation effect. Empirical investigations find support for these two effects. We then develop a stochastic control formulation that optimizes platform revenue, where the control is the optimal delay customized at the trader level and calculated as a function of the current amount of money following a trader and the number of views on the trader's profile page. The optimized revenue can be incorporated into an algorithm to provide a systematic way to infuse the platform's goals into the ranking of the traders. A counterfactual study is conducted to demonstrate the performance of the optimal delay policy (versus a constant-delay policy) using data from a leading social trading platform operating in the foreign exchange market.