Research

Social Media as a Bank Run Catalyst

Revise & Resubmit at Journal of Financial Economics

w/ Anthony Cookson, Javier Gil-Bazo, Juan Imbet, and Christoph Schiller

Social media fueled a bank run on Silicon Valley Bank (SVB), and the effects were felt broadly in the U.S. banking industry. We employ comprehensive Twitter data to show that preexisting exposure to social media predicts bank stock market losses in the run period even after controlling for bank characteristics related to run risk (i.e., mark-to-market losses and uninsured deposits). Moreover, we show that social media amplifies these bank run risk factors. During the run period, we find the intensity of Twitter conversation about a bank predicts stock market losses at the hourly frequency. This effect is stronger for banks with bank run risk factors.  At even higher frequency, tweets in the run period with negative sentiment translate into immediate stock market losses. These high frequency effects are stronger when tweets are authored by members of the Twitter startup community (who are likely depositors) and contain keywords related to contagion. These results are consistent with depositors using Twitter to communicate in real time during the bank run.

How Many Words is  a Picture Worth? Using Emojis From Social media to Predict Future Stock Returns

w/ Eric Kelley and Roman Paolucci

Using a new and comprehensive sample of more than 67 million Twitter posts referencing Russell 3000 firms between 2012 and 2021, we introduce a novel, unsupervised method of scoring the sentiment of emojis. Our method generates point-in-time dictionaries that map individual emojis to the contextual sentiment of recent tweets that contain them. In out-of-sample tests, we find that even controlling for the sentiment extracted from words, emoji sentiment correctly predicts future firm-level stock returns. Understanding the sentiment of emojis has become increasingly important as individuals continue to adopt these new forms of communication.

Equity Borrowing Constraints and the Informed Trading Strategies of Short Sellers

w/ Benjamin Blau, Joshua Della Vedova, and Jason Smith

Informed investors have been shown to break up their larger trades into smaller trades in order to disguise their information. This study considers informed trading strategies when investors face borrowing constraints. Borrowing constraints may induce more intense trading and increase the use of unusually large trade sizes. Using data consisting of short sales, we test this assertion empirically. Following prior work that documents that short sales contain information about future stock prices, we show that the most informed (return predictive) short sales are driven primarily by large short sales in stocks with higher equity borrowing constraints. 

To Own or Not to Own: Stock Loans Around Dividend Payments

w/ Peter Dixon and Eric Kelley

Journal of Financial Economics (2021)

In a standard stock loan, the borrower reimburses the lender any dividends paid while the loan is outstanding. Since these substitute dividends may be taxed differently than dividend payments themselves, some investors have incentives to either remove their shares from lend-able supply – if they pay high taxes on substitute dividends – or lend out their shares to arbitrageurs – if they pay high taxes on dividends. Consistent with these incentives, we find a significant tightening of the equity lending market on dividend record days driven by both a contraction of supply and an expansion of demand – although the demand effect appears to dominate. We then exploit the plausibly exogenous nature of these shifts to causally link tightness in the lending market to wider effective spreads in the stock market.