In an efficient capital market, asset prices vary when investors change their expectations about cash flows, discount rates, or both. Using dividends to measure cash flows, previous research shows that the aggregate dividend‐price ratio varies due to changes in expected discount rates (returns) rather than expected cash flows. In contrast, using accounting earnings instead of dividends as a measure of cash flows, this paper shows that as much as 70% of the variation in the dividend‐price ratio can be explained by changes in expected earnings. Moreover, the paper documents a significant negative correlation between expected returns and expected earnings, suggesting that variations in a common factor to both may generate significant price volatility. The results are consistent with the dividend‐policy irrelevance hypothesis.

In an efficient capital market, asset prices vary when investors change their expectations about cash flows, discount rates, or both. Using dividends to measure cash flows, previous research shows that the aggregate dividend-price ratio varies due to changes in expected discount rates (returns) rather than expected cash flows. In contrast, using accounting earnings instead of dividends as a measure of cash flows, this paper shows that as much as 70% of the variation in the dividend-price ratio can be explained by changes in expected earnings. Moreover, the paper documents a significant negative correlation between expected returns and expected earnings, suggesting that variations in a common factor to both may generate significant price volatility. The results are consistent with the dividend-policy irrelevance hypothesis.

This paper studies the effects of predictability on the earnings–returns relation for individual firms and for the aggregate. We demonstrate that prices better anticipate earnings growth at the aggregate level than at the firm level, which implies that random-walk models are inappropriate for gauging aggregate earnings expectations. Moreover, we show that the contemporaneous correlation of earnings growth and stock returns decreases with the ability to predict future earnings. Our results may therefore help explain the apparently conflicting recent evidence that the earnings–returns relation is negative at the aggregate level but positive at the firm level.

This paper examines the consequences of adopting ASC 606, a new revenue recognition standard, on revenue uncertainty and debt contracting, using a quasi-natural experiment surrounding its adoption. We find that affected firms experience an increase in revenue uncertainty, as indicated by both higher analyst forecast dispersion and absolute analyst forecast error. Consequently, the cost of debt rises for affected firms, as covenants are used less in debt contracts reflecting a decreased effectiveness of earnings-based covenants. The effect is mitigated by relationship lending. We also show that the decreased use of earnings-based covenants as well as the increased cost of debt dissipate over time, while the increase in revenue uncertainty persists. Our analyses document a costly transition toward a more principles-based accounting standard but also suggest that some costs are transient.

This paper studies the relation between aggregate stock returns and contemporaneous and future cross-sectional earnings dispersion. We hypothesize that increases in expected earnings dispersion signal increases in uncertainty and increases in unemployment, thereby causing expected returns to rise, which in turn causes prices to decline. We find a positive relation between aggregate stock returns and contemporaneous earnings dispersion because higher earnings dispersion is associated with higher expected returns. Consequently, we also find a negative relation between aggregate stock returns and future (one-year ahead) earnings dispersion, as investors anticipate higher future earnings dispersion and higher expected returns.

ABSTRACT A principal‐components analysis demonstrates that common earnings factors explain a substantial portion of firm‐level earnings variation, implying earnings shocks have substantial systematic components and are not almost fully diversifiable as prior literature has concluded. Furthermore, the principal components of earnings and returns are highly correlated, implying aggregate earnings risks and return risks are related. In contrast to previous studies, the correlation we report between the systematic components of earnings and returns is stable over time. We also show that the earnings factors are priced, in the sense that the sensitivities of securities' returns to the earnings factors explain a significant portion of the cross‐sectional variation in returns, even controlling for return risk. This suggests earnings performance is an underlying source of priced risk. Our evidence that the information sets of returns and earnings are jointly determined implies cash flow risk and return risk are not fully separable, and raises the possibility that it is the common variation of earnings and returns that is priced.

ABSTRACT Despite theoretical and anecdotal evidence highlighting the importance of industry-level analyses to lenders, the empirical literature on debt pricing has focused almost exclusively on firm-level forces that affect expected loss. This paper provides empirical evidence that industry-level characteristics relate to debt pricing through risk premiums. We address the empirical challenges that arise when testing these theories by using a proprietary dataset of time-varying and forward-looking measures of industry characteristics. These characteristics include growth, sensitivity to external shocks, and industry structure, all measured at the six-digit NAICS level. Our results show that lenders demand higher spreads to bear industry-level risk. The relation exists within subsamples with constant credit ratings, and strengthens when lenders' loan portfolios are less diversified and during periods when diversification is difficult. Therefore, our results suggest that industry characteristics relate to debt pricing by informing lenders not only about expected loss, but also about risk premiums. JEL Classifications: G31; G32; G33; M21.