differential association of forecast error and earnings variability with systematic risk

by Russell M. Barefield

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• 14 Currently reading

Published 1978 by Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University in West Lafayette, Ind .
Written in English

Subjects:

• Stock price forecasting.

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Edition Notes

Bibliography: leaf 10.

Statement	by Russell M. Barefield and Eugene E. Comiskey.
Series	Paper - Institute for Research in the Behavioral, Economic, and Management Sciences, Purdue University ; no. 661
Contributions	Comiskey, Eugene E., joint author.
Classifications
LC Classifications	HD6483 .P8 no. 661, HG4636 .P8 no. 661
The Physical Object
Pagination	10 leaves ;
Number of Pages	10
ID Numbers
Open Library	OL4378505M
LC Control Number	78622635

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Moreover, there was an association among the systematic risk and earnings variability and growth. Mensah () argues that expressing exogenous variables in accounting terms is likely to be useful as accounting reports provide an overview of the financial and operational status of a particular entity. You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.

into the firm or the same tools to analyze the differential earnings streams with respect to variability and persistence. As we show later, variance contri-bution is a function of both the persistence of the earnings component and its variability.3 Although researchers recently show considerable interest in. Learning to forecast, risk aversion, and microstructural aspects of financial stability. Alessio Emanuele Biondo. Abstract. This paper presents a simulative model of a financial market, based on a fully operating order book with limit and market orders. The heterogeneity of traders is characterized not. The volatility forecast is based on a multi-factor model that determines a company’s potential to be have either stable or unstable prices, based on the company’s factor profile. Factors such as sensitivity to credit spreads, EBIT variability and overall market size, among others, are combined in an APT type model to forecast inherent risk.

Sur is the earnings surprise, measured as the difference between actual earnings and consensus forecast, standardized by share price. The consensus forecast is computed as the mean of all quarterly forecasts issued by analysts within ninety days before the earnings announcement day. The LAD high earnings forecast portfolio return is significantly higher than that of the low earnings forecast portfolio, even after adjusting for risk. The LAD high minus low portfolio has the highest monthly abnormal return of basis points ( basis points) based on the CAPM model (the Fama‐French model) (Panel A of Table 7). To provide further evidence on the relation between analysts’ forecast dispersion and firm risk, we explore how firms’ systematic risk and idiosyncratic risk explains the variation in dispersion. We use market beta as a proxy for systematic risk and mean squared errors (MSE) - from the estimation of the market model as a proxy for.