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Forecasting risk in earnings

Konstantinidi, Theodosia and Pope, Peter F. (2016) Forecasting risk in earnings. Contemporary Accounting Research, 33 (2). 487 - 525. ISSN 0823-9150

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Identification Number: 10.1111/1911-3846.12158


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.

Item Type: Article
Official URL:
Additional Information: © 2015 CAAA
Divisions: Accounting
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
H Social Sciences > HF Commerce > HF5601 Accounting
Sets: Departments > Accounting
Date Deposited: 15 Jun 2015 10:47
Last Modified: 20 Sep 2021 03:49

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