This paper examines cross-sectional analysis procedures common to many market-based accounting research papers. Both the economic and econometric properties of ‘levels’ and ‘returns’ studies are discussed. Topics covered include the relations between the accounting studies and cash flow valuation models, the role of expectations of accounting variables, deflators, spurious inference, risk adjustment and its relation to growth, size and leverage, residual dependence, dependence among explanatory variables, and the effect of scale differences across firms. Major conclusions are that market value is the correct deflator in returns studies, and that levels and returns studies are economically but not econometrically equivalent.

This paper investigates the extent of intra-industry information transfers associated with the half-yearly earnings announcements of a sample of Australian firms. The ‘omitted factor’ interpretation of Foster's (1981) results is examined using a recursive systems specification of the return generating process to model extra-market return covariation in cross-section. Although some aspects of the results do appear sensitive to the alternative methodologies, the overall conclusion is consistent with Foster (1981) and supports the existence of intra-industry information transfers associated with firms' earnings releases.

This paper provides evidence of security analyst (SA) superiority relative to univariate time-series (TS) models in predicting firms' quarterly earnings numbers and shows that SA forecast superiority in our sample is attributable to: (1) better utilization of information existing on the date that TS model forecasts can be initiated, a contemporaneous advantage; and (2) use of information acquired between the date of initiation of TS model forecasts and the date when SA forecasts are published, a timing advantage.

This study examines the association between abnormal returns and five alternative proxies for the market's assessment of unexpected quarterly earnings. We examine the role that measurement error potentially has in multiple regression tests of abnormal returns (occuring around the time of earnings announcements) on an unexpected earnings proxy and other non-earnings variables. The results indicate a potential measurement error interpretation of such multiple regression tests. We examine three procedures which reduce, to an unknown degree, the measurement error problem. Our procedures appear to be more (less) effective at reducing measurement error for small (large) firms and recent (non-recent) forecasts.

Beaver, Lambert and Morse (1980) employ a regression of percentage change in prices on percentage change in earnings in which data are grouped by the dependent variable. Reverse regression offers a more intuitive and direct way to assess the information content of security prices, the objective of Beaver et al. While grouping is asymptotically equivalent, reverse regression is a more efficient way of examining the incremental explanatory power of lagged values of percentage change in price with respect to accounting earnings.