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Shocks to Shocks: A Theoretical Foundation for the Information Content of Earnings *

Shocks to Shocks: A Theoretical Foundation for the Information Content of Earnings * Contemporary Accounting Research Vol. 26 No. 1 (Spring 2009) pp. 135–66 © CAAA doi:10.1506/car.26.1.5 Contemporary Accounting Research the volatility of unexpected returns generated by shocks to earnings? Can the generalized Ball-Brown methodology developed in this paper be used to improve extant empirical methodologies and to suggest further lines of research? Our initial analysis addresses these and other issues using a simple time-series model — earnings, “other information”, and expected future discount rates are assumed to be loglinear stationary AR(1) processes — that lends itself to reasonably straightforward intuition. Our further analysis generalizes these results by obtaining closed-form solutions for a broad class of (log) linear time-series models. It is remarkable that with elementary matrix manipulation, the results obtained for the simple loglinear stationary AR(1) time-series model extend to loglinear stationary ARMA(p, q), ARIMA(p, d, q) and vector autoregressive (VAR) processes. These generalizations are important because there is an extensive literature on the timeseries properties of accounting earnings showing that, for many firms, the time series of both annual and quarterly accounting earnings are best described by higher-order time series, such as the ARIMA(p, d, q) type. In what follows, section 2 develops the Ball-Brown metric of information content http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Contemporary Accounting Research Wiley

Shocks to Shocks: A Theoretical Foundation for the Information Content of Earnings *

Contemporary Accounting Research , Volume 26 (1) – Mar 1, 2009

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References (67)

Publisher
Wiley
Copyright
2009 Canadian Academic Accounting Association
ISSN
0823-9150
eISSN
1911-3846
DOI
10.1506/car.26.1.5
Publisher site
See Article on Publisher Site

Abstract

Contemporary Accounting Research Vol. 26 No. 1 (Spring 2009) pp. 135–66 © CAAA doi:10.1506/car.26.1.5 Contemporary Accounting Research the volatility of unexpected returns generated by shocks to earnings? Can the generalized Ball-Brown methodology developed in this paper be used to improve extant empirical methodologies and to suggest further lines of research? Our initial analysis addresses these and other issues using a simple time-series model — earnings, “other information”, and expected future discount rates are assumed to be loglinear stationary AR(1) processes — that lends itself to reasonably straightforward intuition. Our further analysis generalizes these results by obtaining closed-form solutions for a broad class of (log) linear time-series models. It is remarkable that with elementary matrix manipulation, the results obtained for the simple loglinear stationary AR(1) time-series model extend to loglinear stationary ARMA(p, q), ARIMA(p, d, q) and vector autoregressive (VAR) processes. These generalizations are important because there is an extensive literature on the timeseries properties of accounting earnings showing that, for many firms, the time series of both annual and quarterly accounting earnings are best described by higher-order time series, such as the ARIMA(p, d, q) type. In what follows, section 2 develops the Ball-Brown metric of information content

Journal

Contemporary Accounting ResearchWiley

Published: Mar 1, 2009

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