[We propose a decomposition for time series in components classified by levels of persistence. Employing this decomposition, we provide empirical evidence that consumption growth contains predictable components highly correlated with well-known proxies of consumption variability. These components generate a term-structure of sizable risk premia. At low frequencies we identify a component correlated with long-run productivity growth and commanding a yearly premium of approximately 2%. At high frequencies we identify a component with yearly half-life, which contributes to the equity premium for another 2%. Accounting for persistence heterogeneity, we obtain an estimate of the IES strictly above one and robust across subsamples.]

We construct tradable risk factors using combinations of large and liquid mutual funds (long leg) and ETFs (exchange-traded funds) (long and short legs), based on their holdings, for both retail and institutional investors. Exploiting a novel dataset, our tradable factors take into account ETF shorting costs. Assessing the performance of our tradable factors against standard “on-paper” factors, we uncover an implementation shortfall of 2–4 percent annually. Shorting fees and transaction costs contribute to 58 percent of the performance differential between tradable and “on-paper” factors, assigning a non-trivial role to the opportunity cost of not trading the exact “on-paper” portfolio.

We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. In all these asset classes, expected value returns vary by at least as much as their unconditional level. A single common component of the value spreads captures about two-thirds of value return predictability and the remainder is asset class specific. We argue that common variation in value premia is consistent with rationally time-varying expected returns, because (i) common value is closely associated with standard proxies for risk premia, such as the dividend yield, intermediary leverage, and illiquidity, and (ii) value premia are globally high in bad times.

Using the first reported case of COVID-19 in a given U.S. county as the event day, we find that firms headquartered in an affected county experience, on average, a 27-bps lower return in the 10-day post-event window. This negative effect nearly doubles in magnitude for firms in counties with a higher infection rate (−50 bps). We test a number of transmission channels. Firms belonging to labor-intensive industries and those located in counties with a large mobility decline have worse stock performance. Firms sensitive to COVID-19-induced uncertainty also exhibit more negative returns. Finally, more negative stock returns are associated with downward revisions in earnings forecasts.

We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.

We propose a decomposition for time series in components classified by levels of persistence. Employing this decomposition, we provide empirical evidence that consumption growth contains predictable components highly correlated with well-known proxies of consumption variability. These components generate a term-structure of sizable risk premia. At low frequencies we identify a component correlated with long-run productivity growth and commanding a yearly premium of approximately 2%. At high frequencies we identify a component with yearly half-life, which contributes to the equity premium for another 2%. Accounting for persistence heterogeneity, we obtain an estimate of the IES strictly above one and robust across subsamples.

We study the drift and cyclical components in U.S. Treasury bonds. We find that bond yields are drifting because they reflect the drift in monetary policy rates. Empirically, modeling the monetary policy drift using demographics and productivity trends, plus long-term inflation expectations, leads to cyclical deviations of bond prices from their drift that predict bond returns in- and out-of-sample. These bond cycles can be interpreted as term premia or/and temporary deviations from rational expectations in a behavioral framework. Through the lens of our model, we detect a significant role of the latter in determining the cyclical properties of yields with short maturities.

We study the horizon dimension of cross-sectional return predictability using a model where characteristics contain both persistent and transitory components. We test the implications of this model for the average returns of popular characteristic-based trading strategies at short versus long horizons after portfolio formation. Our evidence supports the claim that the relative compensation for persistent and transitory components varies across characteristics, in both magnitude and sign. Benchmark factor models cannot explain the returns of portfolios sorted on characteristics where either the persistent or transitory component is dominant. Finally, we discuss implications for the long-term discount rates of firms.

Fiscal policy matters for bond risk premia. Empirically, government spending level and uncertainty predict bond excess returns, as well as term structure level and slope movements. Shocks to government spending level and uncertainty are also priced in the cross-section of bond and stock portfolios. Theoretically, government spending level shocks raise inflation when marginal utility is high, thus generating positive inflation risk premia (term structure level effect). Uncertainty shocks steepen the yield curve (slope effect), producing positive term premia. These effects are consistent with evidence from a structural vector autoregression. Asset pricing tests using model simulated data corroborate our empirical findings.