Knowledge that Transforms

How do people record information about the outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are hampered in their perception of outcomes that they expect to seldom encounter. We provide experimental evidence for such “outlier blindness” and discuss how it provides a microfoundation for neglected tail risk by investors in financial markets.

Using individual credit bureau data matched with cash windfalls from fracking, we estimate that windfall recipients reduce debt-to-income by 2.4 percentage points relative to no-windfall controls. Debt repayment effects are 3 times stronger for subprime individuals than for prime individuals. Based on the timing of upfront versus continuing cash payments, debt repayment coincides with the timing of payments but not with news about future payments. These findings present a challenge for purely forward-looking models of debt. Indeed, when we incorporate a windfall shock into a forward-looking model, the model predicts an increase in debt that runs counter to our evidence of debt repayment.

This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of multiple systematic factors, is priced in the cross-section of expected stock returns. We derive an extended linear model with a positive premium for MCRASH, and we empirically confirm that stocks with high MCRASH earn significantly higher future returns than stocks with low MCRASH. The premium is not explained by linear factor exposures, alternative downside risk measures, or stock characteristics. Extending market-based definitions of crash risk to other well-established factors helps to determine the cross-section of expected stock returns without further expanding the factor zoo.

We investigate the effect of the “7% solution”—the fact that underwriters in the U.S. charge a 7% spread to most IPOs between 20 million and 100 million in size—on the ensuing pricing of the offerings. Our identification exploits the variation in spreads that is due to distinct kinks in the relation between spread and offer size at these two thresholds. We find the spread positively influences underpricing but also the offer-price adjustment from the filing range's midpoint. Our evidence indicates the spread influences the aftermarket price, suggesting underwriters can shape, not merely discover, investor valuations.

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.

We study the effects of social media on the informativeness of retail trading. Our identification strategy exploits the editorial delay between report submission and publication on Seeking Alpha, a popular crowdsourced investment research platform. We find the ability of retail order imbalances to predict the cross-section of stock returns and cash-flow news increases sharply in the intraday post-publication window relative to the pre-publication window. The findings are robust to controlling for report tone and stronger for reports authored by more capable contributors. The evidence suggests that recent technology-enabled innovations in how individuals share information help retail investors become better informed.

We study the rapidly growing literature on the causal effects of financial education programs in a meta-analysis of 76 randomized experiments with a total sample size of over 160,000 individuals. Many of these experiments are published in top economics and finance journals. The evidence shows that financial education programs have, on average, positive causal treatment effects on financial knowledge and downstream financial behaviors. Treatment effects are economically meaningful in size, similar to those realized by educational interventions in other domains, and robust to accounting for publication bias in the literature. We also discuss the cost-effectiveness of financial education interventions.

This paper attempts to study the causal effect of examiner busyness on patent quality and firm value. Using a broad set of patent quality measures, we find strong evidence that patents allowed by busy examiners exhibit significantly lower quality. Further, examiner busyness of firms’ patents negatively predicts the firms’ future stock returns, which is consistent with investor underreaction to examiner busyness. Examiners’ experience helps attenuate the negative effect of examiner busyness.

We present a model of media coverage of corporate announcements. Firms strategically use the media to communicate corporate announcements to a group of traders who observe announcements not directly but through media reports. Journalists strategically select which announcements to report to readers. Media coverage inadvertently incentivizes firms to manipulate the underlying announcements. In equilibrium, media coverage is tilted towards less manipulated negative news. The presence of financial journalists leads to more manipulation but makes stock prices more informative on average. We provide additional predictions regarding the media’s impact on the quality of firm announcements and stock prices.

This paper assesses different econometric approaches to working with count-based outcome variables and other outcomes with similar distributions, which are increasingly common in corporate finance applications. We demonstrate that the common practice of estimating linear regressions of the log of 1 plus the outcome produces estimates with no natural interpretation that can have the wrong sign in expectation. In contrast, a simple fixed-effects Poisson model produces consistent and reasonably efficient estimates under more general conditions than commonly assumed. We also show through replication of existing papers that economic conclusions can be highly sensitive to the regression model employed.