City Research Online

Abstract

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.

Publication Type:	Article
Additional Information:	This is the peer reviewed version of the following article: Konstantinidi, T. & Pope, P. (2016). Forecasting risk in earnings. Contemporary Accounting Research, 33(2), pp. 487-525, available at http://dx.doi.org/10.1111/1911-3846.12158. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Publisher Keywords:	accruals; fundamentals-based risk forecasts; quantile regression
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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