Previous studies by Beaver et al. [1970] and Eskew [1979] indicate that the inclusion of accounting-based risk measures in models used to predict the systematic risk of equity securities enables better predictions than security-market-based models which exclude accounting risk measures. This study finds that accounting risk measures do not improve upon market-based systematic risk predictions. The earlier findings of superior predictive ability for accounting-based forecasts are reinterpreted as due to the instability over time of systematic risk, coupled with a fortuitous "shrinking effect" of the ordinary least-squares regression model. Replications of the analysis using portfolios, risk levels, and contemporary values of the accounting risk variables fail to reveal any advantage for accounting-based predictions. Additional tests for the stability of the relationship between accounting and market-based risk measures indicate instability over time and across groups of companies.

The ability of accounting risk measures to aid in explanations and predictions of systematic risk (β) has been studied extensively, and successive studies have reached conflicting conclusions. An element of research design that has varied across studies is the selection of a security market index to serve as a proxy for the unobservable "market portfolio" defined by the underlying capital asset pricing theory. This paper demonstrates empirically that the choice of a market index can have a substantial effect upon the research findings, and offers a partial reconcilation of the apparently contradictory results of earlier studies.

Reviews the book "Financial Reporting: An Accounting Revolution," by William H. Beaver.

This paper re-evaluates evidence in Ayers and Freeman [Ayers, F., Freeman, R., 1997. Market assessment of industry and firm earnings information. Journal of Accounting and Economics 24, 205–218] suggesting that investors anticipate industry-wide components of earnings earlier than firm-specific components, and that post-earnings-announcement drift following annual earnings announcements is due primarily to firm-specific components of earnings. Our tests indicate that post-announcement drift is entirely attributable to coefficient bias due to measurement errors in the use of realized earnings changes as proxies for unexpected earnings. Also, coefficient differences in the market's anticipation of subsequent-year industry and firm-specific earnings become insignificant when we introduce suitable controls for non-linearity in the return/earnings relation.

Ray J. Pfeiffer, Jr., Pieter T. Elgers, Controlling for Lagged Stock Price Responses in Pricing Regressions: An Application to the Pricing of Cash Flows and Accruals, Journal of Accounting Research, Vol. 37, No. 1 (Spring, 1999), pp. 239-247

Pieter T. Elgers, May H. Lo, Reductions in Analysts' Annual Earnings Forecast Errors Using Information in Prior Earnings and Security Returns, Journal of Accounting Research, Vol. 32, No. 2 (Autumn, 1994), pp. 290-303

This paper reassesses evidence of anticipatory income smoothing reported in DeFond and Park (DP) (J. Accounting Econom. 23 (1997) 115) in light of knowledge about measurement error in discretionary accrual estimates. We argue that the method DP use to measure un-managed earnings mechanically biases the evidence in a manner consistent with anticipatory income smoothing. Using an approximate randomization approach, we find that DP's results cannot be distinguished from those achieved when discretionary accruals are randomly assigned to firm-years in our sample. Overall, these results show that the ‘backing out’ approach to measuring un-managed earnings is ineffective in testing earnings management hypotheses.