Valuation Weights, Linear Dynamics and Accounting Conservatism: An Empirical Analysis


  • David Ashton,

    1. School of Economics, Finance and Management, Bristol University, UK
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  • Pengguo Wang

    Corresponding author
    • Xfi Centre for Finance and Investment, University of Exeter Business School, UK
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    • The authors acknowledge many helpful comments received from participants from 2009 AAA annual meetings, Manchester University, Bristol University, Strathclyde University, Exeter University, Loughborough University and Imperial College London on earlier versions of the paper. They would like to thank James Ohlson, Ken Peasnell, Peter Pope, Martin Walker, Colin Clubb, Alan Gregory, Kevin McMeeking and Jo Horton for helpful suggestions. Particular thanks are due to the editor, Andy Stark and an anonymous referee for his/her very insightful and constructive comments.

Address for correspondence: Pengguo Wang, Xfi Centre for Finance and Investment, University of Exeter Business School, Exeter EX4 4ST, UK. e-mail:


Residual income models provide an important theoretical link between equity valuation and financial statement variables. While various researchers have developed models of how accounting policy impacts on the structure of these models, empirical support for these models is at best weak and frequently contradictory. In this paper, we develop an analytical model, which identifies the dependency between valuation weights in residual income models and the associated structure of earnings information dynamics and accounting conservatism. In contrast to many earlier studies, we find strong evidence of conservatism in our reformulation of the linear dynamics. We proceed to test our predictions of the dependency of the weights on two measures of conservatism, the conventional measure of price-to-book ratio and the recent measure of a C-Score index developed by Khan and Watts (2009) and find that the empirical results accord well with our theoretical predictions in the case of the former but not the latter measure.