Forecasting Risk in Earnings

58 Pages Posted: 2 Aug 2011 Last revised: 16 Jun 2015

See all articles by Theodosia Konstantinidi

Theodosia Konstantinidi

City University London - Sir John Cass Business School

Peter F. Pope

Bocconi University; London School of Economics and Political Science

Date Written: October 12, 2014


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 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.

Keywords: Earnings, accruals, fundamentals-based risk forecasts, quantile regression

JEL Classification: M41, C13

Suggested Citation

Konstantinidi, Theodosia and Pope, Peter F., Forecasting Risk in Earnings (October 12, 2014). Contemporary Accounting Research, Forthcoming, Available at SSRN: or

Theodosia Konstantinidi (Contact Author)

City University London - Sir John Cass Business School ( email )

106 Bunhill Row
London, EC1Y 8TZ
United Kingdom

Peter F. Pope

Bocconi University ( email )

Dept of Accounting
Milan, 20136

London School of Economics and Political Science ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

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