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Macroeconomic forces, systematic risk, and financial variables: an empirical investigation

Author: Young, S. David ; Berry, M. A. ; Harvey, D. W. ; Page, J. R.INSEAD Area: Accounting and ControlIn: Journal of Financial and Quantitative Analysis, no. 26, December 1991 Language: EnglishDescription: p. 559-564.Type of document: INSEAD ArticleNote: Please ask the Library for this articleAbstract: Various researchers argue that financial statement data are useful in predicting betas derived from a single-index market model. Others suggest that such data are no better at beta prediction than Bayesian adjustments of prior-period betas. The development and growing proeminence of multifactor models of asset pricing suggests the importance of examining this beta prediction issue in the context of alternative systematic risk measures. This paper reports the results of an investigation into the usefulness of financial data in predicting factor betas derived from a multifactor, macroeconomic model. Specifically, it tests the hypothesis that financial statement data have incremental forecasting power relative to forecasting techniques that use only historical betas. Factor betas for a large cross-section of firms are regressed on 12 well-known financial variables to develop a predictive model for future-period betas..
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Various researchers argue that financial statement data are useful in predicting betas derived from a single-index market model. Others suggest that such data are no better at beta prediction than Bayesian adjustments of prior-period betas. The development and growing proeminence of multifactor models of asset pricing suggests the importance of examining this beta prediction issue in the context of alternative systematic risk measures. This paper reports the results of an investigation into the usefulness of financial data in predicting factor betas derived from a multifactor, macroeconomic model. Specifically, it tests the hypothesis that financial statement data have incremental forecasting power relative to forecasting techniques that use only historical betas. Factor betas for a large cross-section of firms are regressed on 12 well-known financial variables to develop a predictive model for future-period betas..

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