The term structure of equity returns is downward-sloping: stocks with high cash flow duration earn 1.10% per month lower returns than short-duration stocks in the cross-section. I create a measure of cash flow duration at the firm level using balance sheet data to show this novel fact. Factor models can explain only 50% of the return differential, and the difference in returns is three times larger after periods of high investor sentiment. Analysts extrapolate from past earnings growth into the future and predict high returns for high-duration stocks following high-sentiment periods, contrary to ex-post realizations. I use institutional ownership as a proxy for short-sale constraints, and find the negative cross-sectional relationship between cash flow duration and returns is only contained within short-sale constrained stocks.

We causally test alternative theories of expectation formation. Using a randomized information experiment we show overreaction is a key feature of individuals’ return expectations, and individuals’ response to the price-earnings ratio is opposite of academic consensus. Our evidence is inconsistent with standard models of expectation formation, but subjective mental models that deviate from objective benchmarks can jointly explain the updating behavior in the experiment, the link between individuals’ prior perceptions and expectations, and the heterogeneity of updating. Conditional on their beliefs, individuals’ sensitivity of equity shares in a hypothetical portfolio choice experiment is consistent with the standard Merton model.

We study the importance of production networks for the transmission of monetary policy using the stock market reaction as laboratory. We attribute 55% to 85% of the overall response to network effects. Large network effects are a robust feature of the data; we document similar patterns in realized fundamentals. Matching sparsity and the first two outdegrees industry-by-industry can explain large network effects. A simple model with intermediate inputs predicts the reaction of stock returns follows a spatial autoregression, which we exploit for our empirical strategy. Our results suggest production networks are an important mechanism for transmitting monetary policy shocks.

We study how investors respond to inflation combining a customized survey experiment with trading data at a time of historically high inflation. Investors’ beliefs about the stock return–inflation relation are very heterogeneous in the cross section and on average too optimistic. Moreover, many investors appear unaware of inflation-hedging strategies despite being otherwise well-informed about prevailing inflation rates and asset returns. Consequently, whereas exogenous shifts in inflation expectations do not impact return expectations, information on past returns during periods of high inflation leads to negative updating about the perceived stock-return impact of inflation, which feeds into return expectations and subsequent actual trading behavior.

The anomaly zoo has caused many to question whether researchers are using the right tests of statistical significance. But even if researchers are using the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors (i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly, the “anomaly base rate”). We propose a way to estimate it by combining two key insights: Empirical Bayes methods capture the implicit process by which researchers form priors about the likelihood that a new variable is a tradable anomaly based on their past experience, and under certain conditions, a one-to-one mapping exists between these prior beliefs and the best-fit tuning parameter in a penalized regression. The anomaly base rate varies substantially over time, and we study trading-strategy performance to verify our estimation results.

The downside risk capital asset pricing model (DR-CAPM) can price the cross section of currency returns. The market-beta differential between high and low interest rate currencies is higher conditional on bad market returns, when the market price of risk is also high, than it is conditional on good market returns. Correctly accounting for this variation is crucial for the empirical performance of the model. The DR-CAPM can jointly rationalize the cross section of equity, equity index options, commodity, sovereign bond and currency returns, thus offering a unified risk view of these asset classes. In contrast, popular models that have been developed for a specific asset class fail to jointly price other asset classes.

We propose a nonparametric method to study which characteristics provide incremental information for the cross-section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don’t provide incremental information for expected returns, and nonlinearities are important. We study our method’s properties in simulations and find large improvements in both model selection and prediction compared to alternative selection methods. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

We show that after monetary policy announcements, the conditional volatility of stock market returns rises more for firms with stickier prices than for firms with more flexible prices. This differential reaction is economically large and strikingly robust to a broad array of checks. These results suggest that menu costs—broadly defined to include physical costs of price adjustment, informational frictions, etc.—are an important factor for nominal price rigidity at the micro level. We also show that our empirical results are qualitatively and, under plausible calibrations, quantitatively consistent with New Keynesian macroeconomic models in which firms have heterogeneous price stickiness. (JEL E12, E31, E43, E44, E52, G12, L11)

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.

We propose a simple and computationally attractive method to deal with missing data in in cross-sectional asset pricing using conditional mean imputations and weighted least squares, cast in a generalized method of moments (GMM) framework. This method allows us to use all observations with observed returns; it results in valid inference; and it can be applied in nonlinear and high-dimensional settings. In simulations, we find it performs almost as well as the efficient but computationally costly GMM estimator. We apply our procedure to a large panel of return predictors and find that it leads to improved out-of-sample predictability.