We study whether information design influences consumer behavior in a randomized field experiment with users of an online account aggregation app. Participants received a personalized index representing their net worth as a lifetime monthly cash flow. The presentation of this index varied across treatments in its framing and the salience of its display. Consumers exposed to a consumption-oriented frame and a salient comparison of the index with their past spending reduced discretionary spending. These findings show that minor variations in information presentation can significantly affect financial behavior, highlighting the power of design in promoting saving and informing policy and regulation.

Cost-of-capital assessments with factor models require quantitative forward-looking estimates. We recommend estimating Vasicek-shrunk betas with 1–4 years of daily stock returns and then shrinking betas a second time (and more for smaller stocks and longer-term projects), because the underlying betas are themselves time-varying. Such estimators also work well in other developed countries and for small-minus-big (SMB) and high-minus-low (HML) exposures. If own historical stock returns are not available, peer betas based on market cap should be used. Historical industry averages have almost no predictive power and should never be used.

Our paper explores whether a symmetric plain or an asymmetric down-beta is a better hedging measure (Roy 1952; Markowitz 1959). Unlike Ang, Chen, and Xing (2006) and Lettau, Maggiori, and Weber (2014), we find that the prevailing plain market beta is the better predictor, even for crashes. It also predicts the subsequent down-beta (i.e., beta measured only on days when the stock market had declined) better than down-beta itself. Stocks with higher down-betas ex ante also do not earn higher average rates of return ex post. Thus, down-betas are useful for neither hedging nor risk-pricing purposes.

We study whether easier monitoring of personal finances affects consumer spending. We use transaction data from an account aggregation company to study consumers who installed the mobile app after using the same service on a personal computer for several months. We utilize the staggered release of the apps on different devices (iPhone, iPad, and Android) to identify a causal effect conditional on adoption of the mobile app. Consumers decrease their discretionary spending following the installation of the mobile app. The decrease is larger during evening hours and among individuals with lower proxies of self‐control, patterns consistent with a monitoring/self‐regulation channel and with benchmark models of costly self‐control.

Using high‐frequency spending data, we show that household consumption displays excess sensitivity to salient macroeconomic news, even when the news is not real. When the announced local unemployment rate reaches a 12‐month maximum, local news coverage of unemployment increases and local consumers reduce their discretionary spending by 1.5% relative to consumers in areas with the same macroeconomic conditions. Low‐income households display greater excess sensitivity to salience. The decrease in spending is not later reversed. Households in treated areas act as if they are more financially constrained than those in untreated areas with the same fundamentals.

Psychological evidence indicates that decision quality declines after an extensive session of decision-making, a phenomenon known as decision fatigue. We study whether decision fatigue affects analysts’ judgments. Analysts cover multiple firms and often issue several forecasts in a single day. We find that forecast accuracy declines over the course of a day as the number of forecasts the analyst has already issued increases. Also consistent with decision fatigue, we find that the more forecasts an analyst issues, the higher the likelihood the analyst resorts to more heuristic decisions by herding more closely with the consensus forecast, self-herding (i.e., reissuing their own previous outstanding forecasts), and issuing a rounded forecast. Finally, we find that the stock market understands these effects and discounts for analyst decision fatigue.