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Understanding Accounting Changes in an Efficient Market: Analysis of Variance Issues.

James C. McKeown
The Accounting Review 1987 62(3), 597-600
An examination of analysis of variance, Anova design, which includes a blocking factor, must start with an understanding of the effect of the blocking factor on the dependent variable the cumulative abnormal return or CAR. The design includes a separate mean for each block. The effective value of each observation's CAR is thus transformed into the deviation of the original CAR from the mean CAR of the two firms control and switch in the pair of firms. The ANOVA then estimates the main effect of switch vs. control, factor A as the difference between the CAR deviations from the pair means. The other main effects and their interact modify these estimations to allow for the possible effect of signs of the forecasted change in earnings. A test of this main effect then constitutes a test of the market effect of the decision to switch to lifo. An ANOVA, of course, has only one ESS. ESS is defined to be the error term in an ANOVA. An examination of a standard statistics book would have revealed this statement to be inaccurate. Any ANOVA design that has a blocking factor combined with one more crossed factors will necessarily have more than one error term. Error term is actually ambiguous in an ANOVA. Specifically one error term excluding the between-block sum of squares will be used for analysis of any effect that includes the blocking factor while a second error term including the between-block sum of squares will be needed for analyzing effects that do not include the blocking factor. When a blocking factor is used, the observations from a given block must be placed in cells whose factor levels differ only for the blocking factor.

Cue Usage and Self-Insight of Financial Analysts.

Ross Mear; Michael Firth
The Accounting Review 1987 62(1), 176-182
An experiment Was conducted where practicing financial analysts provided risk and return judgments on 30 equity securities. The ability of financial analysts to subjectively express the relative emphasis they place on the available cues when generating their judgment evaluations was assessed by three alternative measurement methods. The results indicate the analysts exhibited a relatively high degree of self-insight since their subjective indications of cue importance were consistent with the models and outputs of their judgment policies.