Knowledge that Transforms

Existing research suggests that alternative data are mainly informative about short‐term future outcomes. We show theoretically that the availability of short‐term‐oriented data can induce forecasters to optimally shift their attention from the long term to the short term because it reduces the cost of obtaining short‐term information. Consequently, the informativeness of their long‐term forecasts decreases, even though the informativeness of their short‐term forecasts increases. We test and confirm this prediction by considering how the informativeness of equity analysts' forecasts at various horizons varies over the long run and with their exposure to social media data.

We show that “zombie credit”—subsidized credit to nonviable firms—has a disinflationary effect. By keeping these firms afloat, zombie credit creates excess aggregate supply, thereby putting downward pressure on prices. Granular European data on inflation, firms, and banks confirm this mechanism. Markets affected by a rise in zombie credit experience lower firm entry and exit, capacity utilization, markups, and inflation, as well as a misallocation of capital and labor, which results in lower productivity, investment, and value added. If weakly capitalized banks were recapitalized in 2009, inflation in Europe would have been up to 0.21 percentage points higher post‐2012.

We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984 to 2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and can forecast aggregate stock market returns. A text‐augmented vector autoregression demonstrates the large incremental role of news text in forecasting macroeconomic dynamics. We retrieve the narratives that underlie these improvements in market and business cycle forecasts.

We develop a theory in which financial (and other professional services) firms design career structures to “sell” prestigious jobs to qualified candidates. Firms create less prestigious entry‐level jobs, which serve as currency for employees to pay for the right to compete for the more prestigious jobs. In optimal career structures, entry‐level employees (“associates”) compete for better‐paid and more prestigious positions (“managing directors” or “partners”). The model provides new implications relating job prestige to compensation, employment, competition, and the size of the financial sector.

We show theoretically that a critical determinant of the attractiveness of venture capital (VC)‐backed entrepreneurship for high‐earning potential founders is the expected time to develop a startup's initial product. This is because founder‐CEOs' cash compensation increases substantially after product development, alleviating the nondiversifiable risk that founders face at startup birth. Consistent with the model's predictions of where the supply of entrepreneurial talent is likely to be most constrained, we find that technological shocks differentially altering the expected time to product across industries can explain changes in both the rate of entry and characteristics of individuals selecting into VC‐backed entrepreneurship.

We analyze a setting in which a board must hire a chief executive officer (CEO) after exerting effort to learn about the quality of each candidate. Optimal effort is asymmetric, implying asymmetric likelihoods of each candidate being chosen. If the board has an infinitesimal bias in favor of one candidate, it allocates effort to maximize the likelihood of that candidate being chosen. Even when the board's prior is that its preferred candidate is inferior, she may still be chosen most often. A glass ceiling can also arise whereby the tendency to hire favored candidates increases as the importance of the position increases.

We propose a model of due diligence and analyze its effect on prices, payoffs, and deal completion. In our model, if the seller accepts an offer, the winning bidder (or “acquirer”) can gather information and chooses when to complete the transaction. In equilibrium, the acquirer engages in “too much” due diligence. Our quantitative results suggest that the magnitude of the distortion is economically significant. Nevertheless, allowing for due diligence can improve both total surplus and the seller's payoff compared to a setting without due diligence. We use our framework to explore the timing of due diligence, bidder heterogeneity, and breakup fees.

We study the effectiveness of saving goals in increasing individuals' savings using data from a Fintech app. Using a difference‐in‐differences identification strategy that randomly assigns users into a group of beta testers who can set goals and a group of users who cannot, we find that setting goals increases individuals' savings rate. The increased savings within the app do not reduce savings outside the app. Moreover, goal setting helps those individuals previously identified as having the lowest propensity to save. Matching App user survey responses to their behavior highlights the relative merits of monitoring and concreteness channels in explaining our findings.

We study noisy aggregation of dispersed information in financial markets without imposing parametric restrictions on preferences, information, and return distributions. We provide a general characterization of asset returns by means of a risk‐neutral probability measure that features excess weight on tail risks. Moreover, we link excess weight on tail risks to observable moments such as forecast dispersion and accuracy, and argue that it provides a unified explanation for several prominent cross‐sectional return anomalies. Simple calibrations suggest the model can account for a significant fraction of empirical returns to skewness, returns to disagreement, and interaction effects between the two.

Using Swedish administrative panel data, we document that workers facing higher left‐tail income risk when equity markets perform poorly have lower portfolio equity share. In line with theory, the relationship between cyclical skewness and stock holdings increases with the share of human capital in a worker's total wealth and vanishes as workers get closer to retirement. Cyclical skewness also predicts portfolio differences within pairs of identical twins. Our findings show that households hedge against correlated tail risks, an important mechanism in asset pricing and portfolio choice models.