We propose a new method for constructing the hedge component in Merton's ICAPM that uses a daily summary measure of economic activity to track time-varying investment opportunities. We then use nonparametric projections to compute a robust estimate of the conditional covariance between stock market returns and our daily economic activity index. We find that the new conditional covariance risk measure plays an important role in explaining time variation in the equity risk premium. Specification tests as well as out-of-sample forecasts of aggregate stock returns suggest that the new covariance risk measure performs well compared to alternative covariance measures previously proposed in the literature.

We document a little-known source of information exploited by sophisticated institutional investors: the Freedom of Information Act (FOIA), a law that allows for the disclosure of previously unreleased information by the U.S. Government. Through FOIA requests, we identify several sophisticated institutional investors, chiefly hedge funds, that request information from the FDA. We explore the type of information commonly requested by funds and the types of firms that are targets of FDA-FOIA requests and show that FOIA requests allow these investors to generate abnormal returns. Thus, we illustrate a detailed mechanism through which costly information becomes incorporated into market prices.

This paper shows that Edgeworth expansions for option valuation are equivalent to approximating option payoffs using Hermite polynomials. Consequently, the value of an option is the value of an infinite series of replicating polynomials. The resultant formulas express option values in terms of skewness, kurtosis, and higher moments. Unfortunately, the Hermite series diverges for fat-tailed models, so we provide alternative moment-based formulas. These formulas are a computationally efficient alternative to Fourier transform valuation and can value options even when the characteristic function is unknown. Applications include the first convergent solution for Hull and White’s stochastic volatility model.Received February 1, 2016; accepted June 27, 2016 by Editor Wayne Ferson.

We study the diversification and welfare effects of a large US robo-advisor on the portfolios of previously self-directed investors and document five facts. First, robo-advice reshapes portfolios by increasing indexing and reducing home bias, number of assets held, and fees. Second, these portfolio changes contribute to higher Sharpe ratios. Third, those who benefit most from robo-advice are investors who did not have high exposure to equities or indexing and had poorer diversification levels. Fourth, robo-advice decreases the time investors dedicate to managing their investments. Fifth, those investors who benefit most are more likely to join the service and not quit it.

We employ a novel brokerage account data set to investigate which individual investors are the most attentive, how investors allocate their attention, and the relation between investor attention and performance. Attention is positively related to investment performance, at both the portfolio return level and the individual trades level. We provide evidence that the superior performance of high-attention investors arises because they purchase attentiongrabbing stocks whose positive performance persists for up to six months. Finally, we show that paying attention is particularly profitable when trading stocks with high uncertainty, but for which a lot of public information is available. (

We propose a new method for constructing the hedge component in Merton's ICAPM that uses a daily summary measure of economic activity to track time-varying investment opportunities. We then use nonparametric projections to compute a robust estimate of the conditional covariance between stock market returns and our daily economic activity index. We find that the new conditional covariance risk measure plays an important role in explaining time variation in the equity risk premium. Specification tests as well as out-of-sample forecasts of aggregate stock returns suggest that the new covariance risk measure performs well compared to alternative covariance measures previously proposed in the literature.

We document four secular trends about U.S. mortgage origination by traditional and FinTech lenders after the 2008-2009 financial crisis. First, since 2011, the overall number, size, and approval rate of small and medium-sized loans have been decreasing over time, relative to large loans. Second, the largest lenders redistribute their lending the most. Third, this loan-size redistribution of credit increases in the size of the lender. Fourth, the effects are stronger for mortgages further away from the conforming loan limit(s) in both directions. We argue that the supply of credit drives these secular trends, and we assess several potential economic mechanisms.

We document a little-known source of information exploited by sophisticated institutional investors: the Freedom of Information Act (FOIA), a law that allows for the disclosure of previously unreleased information by the U.S. Government. Through FOIA requests, we identify several sophisticated institutional investors, chiefly hedge funds, that request information from the FDA. We explore the type of information commonly requested by funds and the types of firms that are targets of FDA-FOIA requests and show that FOIA requests allow these investors to generate abnormal returns. Thus, we illustrate a detailed mechanism through which costly information becomes incorporated into market prices. Received April 15, 2015; editorial decision April 16, 2016 by Editor Itay Goldstein.

ABSTRACT 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 isolate the information channel of peer effects in consumption in a setting that excludes a role for common shocks or social pressure—a spending panel paired with crowdsourced information about anonymous “peers” elicited at different times. Consumers converge to peers’ spending, and more so when peer signals are more informative. Convergence is asymmetric: within 12 months of information provision, overspenders close 17% and underspenders 5% of their gap relative to peers. We exploit the quasi-random assignment to peer groups in an instrumental-variable strategy and implement an experiment for external validity. Our results are consistent with information-based theories of overconsumption.