This paper reconciles conflicting evidence in prior literature on the relative ability of earnings and cash flows in predicting future cash flows. Further, we investigate the implications of temporal shifts in accrual properties and operating environment for cash flow predictability. Three key insights emerge. First, cash flows consistently outperform earnings in predicting future cash flows. Second, accruals and its components, including those capturing non-articulating events, have incremental (albeit small) predictive ability over cash flows. Third, earnings’ ability to predict future cash flows has increased over the period 1989–2015, due to changes in operating environment rather than accrual properties.

ABSTRACT Earnings growth dispersion contains information about trends in labor reallocation, unemployment change, and, ultimately, aggregate output. We find that initial macroeconomic estimates released by government statistical agencies do not fully incorporate this information. As a consequence, earnings growth dispersion predicts future restatements in nominal and real GDP growth (and unemployment change) both in the in-sample and out-of-sample tests. Further, when we adjust GDP estimates using the out-of-sample restatement predictions, we find statistically and economically significant effects for the monetary policy prescriptions (Taylor rule) and banking regulation (Basel III).

We examine the dynamic nature of the information content of aggregate earnings. Specifically, we examine how changes in supply elasticity affect the relation between aggregate earnings and future inflation. Using total capacity utilization to capture supply elasticity, we find that aggregate earnings are more informative about inflation when the supply is inelastic. This finding aligns with the investment demand hypothesis—higher aggregate earnings drive increased investment, pushing inflation higher, when supply is relatively inelastic. Furthermore, when we consider market outcomes such as three-month Treasury yields and aggregate stock returns, the role of supply elasticity pervades. That is, we find that aggregate earnings are more informative for market outcomes primarily when supply is inelastic. Finally, the relation between aggregate earnings and gross domestic product (GDP) also varies with the economy’s supply elasticity. Our findings highlight supply elasticity as a critical factor in interpreting the macroeconomic signals in aggregate earnings.

Initial Gross Domestic Product (GDP) announcements are important economic signals that convey information on the state of the economy but contain substantial estimation error. We investigate how GDP estimation errors affect firms' real decisions and profitability. We find that GDP estimation errors are positively associated with one-quarter-ahead changes in firms’ capital investments, production, inventory, and profitability. However, we observe long-run profitability reversals, which is consistent with initial over (under) production eventually being met with declines (increases) in future profitability. Furthermore, managerial responses to the estimation errors mimic the response to the true component of the GDP signal, suggesting that managers do not filter estimation errors and overreact to both GDP signal components. Our firm-level findings translate to the macroeconomic level, where we find that a long-run reversal follows a positive short-run aggregate investment response to GDP signal components.

Extant literature documents that aggregate accruals positively predict future market returns and attributes this relation to either changes in discount rates or systematic earnings management. We offer an alternative explanation: aggregate merger and acquisition (M&A) activity drives this relation. M&A activity affects the magnitude of accruals, which in turn drives the market return predictability of aggregate accruals. We find that the ability of both aggregate accruals and discretionary aggregate accruals (a measure of systematic earnings management) to predict market returns disappears after controlling for aggregate M&A activity. Furthermore, aggregate M&A activity predicts future market returns, consistent with a price response to improvements in macroeconomic outcomes due to aggregate M&A activity.

This paper studies the ability of long-run risk models to explain out-of-sample asset returns during 1931–2009. The long-run risk models perform relatively well on the momentum effect. A cointegrated version of the model outperforms the classical, stationary version. Both the long-run and the short-run consumption shocks in the models are empirically important for the models' performance. The models' average pricing errors are especially small in the decades from the 1950s to the 1990s. When we restrict the risk premiums to identify structural parameters, this results in larger average pricing errors but often smaller error variances. The mean squared errors are not substantially better than those of the classical CAPM, except for Momentum.