Forecasting Risk in Earnings

Conventional 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 flows, 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.