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AbstractConventional measures of risk in earnings based on historical standard deviation require long time series data and are inadequate when the distribution of earnings deviates from normality. We introduce a methodology based on current fundamentals and quantile regression to forecast risk reflected in the shape of the distribution of future earnings. We derive measures of dispersion, asymmetry and tail risk in future earnings using quantile forecasts as inputs. Our analysis shows that a parsimonious model based on accruals, cash flow, special items and a loss indicator can predict the shape of the distribution of earnings with reasonable power. We provide evidence that out-of-sample quantile-based risk forecasts explain incrementally analysts' equity and credit risk ratings, future return volatility, corporate bond spreads and analyst-based measures of future earnings uncertainty. Our study provides insights into the relations between earnings components and risk in future earnings. It also introduces risk measures that will be useful for participants in both the equity and credit markets.
We study the value premium using the multiples‐based market‐to‐book decomposition of Rhodes‐Kropf, Robinson, and Viswanathan (2005). The market‐to‐value component drives all of the value strategy return, while the value‐to‐book component exhibits no return predictability in either portfolio sorts or firm‐level regressions. Existing results linking market‐to‐book to operating leverage, duration, exposure to investment‐specific technology shocks, and analysts’ risk ratings derive from the unpriced value‐to‐book component. In contrast, results on expectation errors, limits to arbitrage, and certain types of cash flow risk and consumption risk exposure are due to the market‐to‐value component. Overall, our evidence casts doubt on several value premium theories.
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