Working Papers

Tesla is an American electric vehicle and clean energy company. They are based in Palo Alto, California and their product base consists of electric cars, battery energy storage, solar panels, and solar roof tiles. On an average day in 2021, Tesla stock sells for $700/share. We will review historical Tesla data and examine whether this particular stock is worth the investment? In addition to historical data, this research reviews the effect of traditional news and social media on human behavior. Exploring social media analytics, investor sentiment and behavior in hopes to gauge how these factors can impact Tesla and whether this should be taken into consideration prior to the investment.

This paper examines the extent to which individual politicians affect asset prices using a high-frequency identification approach. We exploit the regular flow of viewpoints contained in a large volume of tweets from members of US Congress. Congressional tweets targeting individual firms are collected and classified based on their tone. Supportive (critical) tweets increase (decrease) stock prices of the targeted firm in minutes around the tweet. The price response persists for several days, during which analysts revise their forecasts about the firm cash flows. Selected politician tweets linked to legislation affect the stock prices of firms in the same industry as the targeted firm. The timeline of politician viewpoints within a particular bill exhibits surges in relevant news that predict roll call votes months before the signing of the bill. We highlight how the social media accounts of politicians are a valuable source of political news.

We develop an empirical proxy for the differential treatment of local and foreign investors using translation differences in public disclosure as observable indicators that reflect non-public interactions. After confirming the validity of our proxy, we show that differential treatment results in significant information asymmetry between local and foreign investors as measured through stock illiquidity, and analysis of analyst forecast errors suggests that this information asymmetry is created by firms providing foreign market participants with lower quality information. Firms respond to strategic incentives for differential treatment relating to government subsidies and capital raising, and thus differential treatment is not just a byproduct of resource constraints. Our results highlight the role of differential treatment as one driver of local information advantage.

We study the psychological bias underlying the decision to become an entrepreneur in the online business context. Using entrepreneurs affiliated with Taobao Marketplace, the world’s largest online shopping platform, as our sample, we find that people who observe the emergence of successful stores in their neighborhood are more likely to become online entrepreneurs. Relying on the Taobao store rating system and detailed geographical information for identification, we find that in rural areas of China, an increase in the online rating (upgrade event) of a store leads to a significant increase in the number of new stores within a 0.5-km radius. This effect increases with the magnitude of the upgrade event, decreases with physical distance from the focal store and is robust to a wide range of rigorous model specifications. However, such decisions to enter the market may be suboptimal, as entrants whose entrepreneurs are motivated by these upgrade events underperform relative to their peers in terms of sales and have a higher probability of market exit. Overall, our results are most consistent with salience theories of choice and cannot be explained by regional development or rational learning.

This paper studies the role of social stigma in debt repayment decisions, using a randomized field experiment with the borrowers of a retail bank. In our experiment, borrowers are randomly chosen to have their repayment status shared with an observer who is also randomly selected from a pre-existing list of the borrower's social connections. First, we find that receiving the social disclosure treatment significantly reduces delinquency, by 20% of the base rate. Second, estimates from the benchmarking treatments indicate that borrowers are willing to pay 9% of their monthly income to preserve their social image, not significantly less than they would pay to maintain a good credit report. Third, we combine the random variation in the assigned social contexts with heterogeneity in subject characteristics to examine why borrowers respond to reputational incentives. We find that borrowers are concerned that the revelation of delinquency can make them a less attractive match in social interactions such as in the labor market or the marriage market, i.e., the instrumental role of reputation. Our findings highlight the role of social social collateral as an alternative mechanism to enforce lending contracts and expand credit provision.

The ESG (environmental, social, and governance) practice has become very important in contemporary business, and it is believed to have a significant impact on firm value. However, how investors react to firms' ESG performance is still unknown. Exploiting user-generated content from a popular online investment community (Seeking Alpha) and ESG performance scores from a professional database (Sustainalytics), we first run a fixed-effect panel regression and find an overall positive relationship between ESG and investor attention but no relationship between ESG and investor sentiment. We then conduct an event-study analysis, in which we classify changes in ESG performance as upgrade and downgrade events and find that the significant relationship between ESG and investor attention holds for the downgrade events but not for the upgrade events. We also conduct various robustness checks, on both ESG and investor attention, to rule out potential effects of other factors, such as firm size, debt, intangible assets, and profitability. Our further mechanism analysis reveals that the effect of ESG on investor attention is driven by the social and governance factors rather than the environmental factors. Our work makes both theoretical and practical contributions by identifying the nuanced effect of ESG on investors' reactions in the social media era.

Companies donating in the aftermath of large-scale disasters often suffer public backlash and managers systematically fail to understand what corresponds to a donation that stakeholders perceive as contextually appropriate. We attribute this to the level of uncertainty that obscures the relative social value of a donation because accurate information about impacts is not available for months. We argue that stakeholders rely on a company's pre-disaster reputation as a heuristic to make judgments of its philanthropy. Thus, regardless of the amount of aid given, well-regarded firms obtain rents from responding first to a disaster, and this spills over to companies in the same industry that match their donations; the opposite applies to firms with an unfavorable reputation, and to those that imitate their gifts. Analyses of donations by the largest 2,000 companies worldwide to every major epidemic, natural disaster, and terrorist attack from 2007 to 2019 support this argument and show that this heuristic effect does not transfer to firms donating different amounts. The estimates survive a battery of time-varying and joint fixed effects and tests of confounders. They confirm that reputation is a stronger rent determinant than donation amount. We discuss ways to improve managerial philanthropic decisions in similar settings.

This study constructs a novel measure that aims to capture face-to-face private communications between firm managers and sell-side analysts by mapping detailed, large-volume taxi trip records from New York City to the GPS coordinates of companies and brokerages. Consistent with earnings releases prompting needs for private communications, we observe that daily taxi ride volumes between companies and brokerages increase significantly around earnings announcement dates (EAD) and reach their peak on EAD. After controlling for an extensive set of fixed effects (firm, analyst, year, and firm-broker) and other potential confounding factors, we find that increases in ride volumes around EAD are negatively associated with analysts' earnings forecast errors in periods after EAD and positively associated with the profitability of recommendations issued after EAD (but these effects dissipate over longer horizons). Taken together, our results suggest that analysts may obtain a private source of information orthogonal to their pre-existing information from these in-person meetings, which may help them better understand the implications of current earnings signals for future earnings.

Prior research finds that online social media usage may lower self-control and encourage indulgent behavior in laboratory subjects. We find that corporate CEOs show similar tendencies: CEOs with online social media presence are more likely to succumb to lower self-control and abuse their information advantage to profit from unethical insider trades. Specifically, CEOs' social media presence strongly predicts their insider trading activity in terms of incidence, intensity (amount and frequency), and profitability. We further find that the effect is driven by insider buys (not by sells) and is more pronounced for opportunistic buys which tend to contain more material non-public information.

This paper examines how labor frictions affect investment rate and new firm entry. Using matched employee-employer data from LinkedIn, I first show that increases in the enforceability of non-compete agreements lead to widespread declines in employee departures across seniority levels, driven by workers in knowledge-intensive occupations. Investment rates at existing firms increase, especially for firms that employ more skilled workers. This comes at the expense of new firm entry, which declines substantially in knowledge-intensive sectors. The results suggest that labor frictions play an important role in investment decisions, and that NCs may factor into slowing business dynamism.

We analyze worker reactions to firms' credit deterioration. Using weekly anonymized networking activity on LinkedIn, we show workers initiate more connections immediately following a negative credit event, even at firms far from bankruptcy. Our results suggest that workers are driven by concerns about both unemployment and future prospects at their firm. Heightened networking activity is associated with contemporaneous and future departures, especially at highly-rated firms. Other negative events like missed earnings and equity sell recommendations do not trigger similar reactions. Overall, our results indicate that the latent build-up of connections triggered by credit deterioration represents a source of fragility for firms.

We provide the first analysis of the risk exposure and risk-adjusted performance of impact investing funds, private market funds with dual financial and social goals. We introduce a dataset of impact fund cash flows and exploit distortions in VC performance measures to characterize risk profiles. Impact funds have a lower market beta than comparable private market strategies. Accounting for market risk exposure, impact funds underperform the public market, though not more so than comparable strategies. Adding a public sustainability factor to our pricing model helps explain impact returns, though the correlation of fund cash flows with this factor is not necessarily positive.