I estimate the welfare benefits of eliminating idiosyncratic consumption shocks in the United States related (unrelated) to the business cycle as 36%–39% (lower than 1%) of household utility. Estimates of the former exceed earlier ones because I distinguish between idiosyncratic shocks related/unrelated to the business cycle, estimate the negative skewness of shocks, target moments of idiosyncratic shocks from household-level CEX data, and target market moments. Benefits of eliminating aggregate shocks are lower than 1% of utility. Policy should facilitate the insurance of idiosyncratic shocks related to the business cycle, such as job layoffs, with proof that individuals diligently seek suitable employment during periods of unemployment.

Asset pricing models predict a strong connection between the real risk-free interest rate and the macroeconomy, but prior research finds little empirical support for the connection when examining expected growth. This paper documents a robust relation between the interest rate and macroeconomic uncertainty (i.e., conditional variance). Consistent with precautionary savings, high uncertainty is associated with a low interest rate using numerous data sources, time periods, and measures. A relation between habit and the interest rate disappears after including uncertainty, and the relation is stronger using long-run uncertainty. The results imply that analyses of the interest rate without uncertainty are seriously incomplete.

Contrary to the predictions of traditional life-cycle models, household consumption is excessively sensitive to current income. Similarly, weak evidence of income hedging runs against standard portfolio theory. We link these two puzzles by modifying the theoretical framework of Viceira (2001) to study how consumption-income sensitivities generated by income in the utility function affect households' portfolio choices. Empirically, we find that consumption-income sensitivities affect asset allocation through the income hedging channel. In particular, we show that the interaction between consumption-income sensitivity and the correlation of income growth to stock market returns is an important explanatory variable for households' stock market holdings. Received October 20, 2016; editorial decision April 25, 2018 by Editor Wayne Ferson.

We contribute to identifying proxies for the information set of investors in financial markets. We show that the marketwide price-dividend ratio highly correlates with inflation and labor market variables that also forecast consumption, dividend, and GDP growth, but not with aggregate consumption or GDP growth. Our model with learning from inflation and wage earnings rationalizes the moments of consumption and dividend growth, market return, the price-dividend ratio, real and nominal term structures, the low predictive power of the price-dividend ratio for consumption and dividends, and the dynamics of the price-dividend ratio, unlike a nested model with learning from consumption alone.

We present a novel methodology for estimating/testing the Bansal and Yaron (2004) and related long-run risks (LRR) models based on the observation that the latent state variables are known functions of observables. The large standard error of the estimated elasticity of intertemporal substitution explains the controversy on its magnitude. The model requires higher persistence of consumption and dividend growth to explain the cross-section of returns than that observed in the data. The model matches the unconditional moments of consumption and dividend growth, but implies a higher risk-free rate and lower volatility of the price/dividend ratio, risk-free rate, and market return than those observed in the data. Contrary to the model implications, the conditional variance of the LRR variable fails to capture the large time variation in the equity premium.

An extensive literature studies interactions of stock market anomalies using double-sorted portfolios. But given hundreds of known candidate anomalies, examining selected interactions is subject to a data mining critique. In this paper, we conduct a comprehensive analysis of all possible double-sorted portfolios constructed from 102 underlying anomalies. We find hundreds of statistically significant anomaly interactions, even after accounting for multiple hypothesis testing. An out-of-sample trading strategy that invests in the top backward-looking double-sort strategy generates equal-weighted (value-weighted) monthly average returns of 4% (2.7%) at an annualized Sharpe ratio of 2 (1.38), on par with state-of-the-art anomaly-based machine learning strategies.

We construct a panel of S&P 500 Index call and put option portfolios, daily adjusted to maintain targeted maturity, moneyness, and unit market beta, and test multi-factor pricing models. The standard linear factor methodology is applicable because the monthly portfolio returns have low skewness and are close to normal. We hypothesize that any one of crisis-related factors incorporating price jumps, volatility jumps, and liquidity (along with the market) explains the cross-sectional variation in returns. Our hypothesis is not rejected, even when the factor premia are constrained to equal the corresponding premia in the cross-section of equities. The alphas of short-maturity out-of-the-money puts become economically and statistically insignificant.

We present a comprehensive analysis of the performance and flows of U.S. actively managed equity mutual funds during the 2020 COVID-19 crisis. We find that most active funds underperform passive benchmarks during the crisis, contradicting a popular hypothesis. Funds with high sustainability ratings perform well, as do funds with high star ratings. Fund outflows surpass precrisis trends, but not dramatically. Investors favor funds that apply exclusion criteria and funds with high sustainability ratings, especially environmental ones. Our finding that investors remain focused on sustainability during this major crisis suggests they view sustainability as a necessity rather than a luxury good.

Predicted stock issuers (PSIs) are firms with expected high-investment and low-profit profiles that earn extremely low returns. We evaluate alternative explanations for this empirical phenomenon. Our results show top-PSI firms are cash-strapped, have lottery-like payoffs, high volatility, high beta, low liquidity, and high shorting costs. Over the next 2 years, top-PSI firms earn return on assets of −30% per year, report disappointing earnings, and experience strongly negative forecast revisions. They perform poorly in down markets and are six times more likely to delist for performance-related reasons. Overall, we find substantial support for mispricing, some support for nonstandard preferences, and virtually no support for the risk explanation.

We examine the empirical implications of learning under ambiguity for the cross-section of stock returns. We introduce a theoretically-motivated ambiguity measure and find that ambiguity is priced in the cross-section of average stock returns. Ambiguity is not subsumed by state variables known to predict stock returns, nor by value, size, and momentum factors. In R-squared comparative tests, a model that takes ambiguity into account performs better than empirical implementations of the Bayesian learning model, the intertemporal CAPM, and the four-factor model of Fama and French (1993) and Carhart (1997).