The portfolio approach controls for the magnitude of

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the portfolio approach controls for the magnitude of forecast error, the results for ST Revisions demonstrate that for each forecast error interval, the effect on price can differ significantly based on the permanence of earnings or the noise that is contained in earnings. Our regression approach allows us to control for these additional factors. Table 5 reports the results from estimating regression equation (2), which attempts to simultaneously control for several factors that can influence announce- ment period returns. In addition, we attempt to model nonlinearities in the returns- earnings relation. The first set of results in Table 5 reports results for our full sample. The last two columns report results for the zero-surprise sample. The coefficient on D Dispersion is the focus of our empirical test. We find a statistically significant coefficient for D Dispersion in a direction consistent with the cost of capital hypothesis for our full sample and the zero- surprise sample. The change in forecast dispersion of next quarter’s earnings relates negatively to the current quarter’s announcement period returns. These results suggest that earnings announcements can provide earnings-related information that induces a stock price response incremental to the magnitude of the earnings surprise. The magnitude of the coefficient on D Dispersion ranges from - 1.1 to - 1.4%, indicating a difference in returns for observations that fall in the bottom and top ranks. Thus, considering that returns are measured over a 3-day window, this result is also economically significant. Most of our control variables are significant in the predicted direction. The coefficients for FE , ST Revision , and LT Revision are positive and reflect the market response to the intervals closest to zero (i.e., small forecast errors and revisions). The magnitudes are similar to what Kinney et al. ( 2002 ) document for small levels of unexpected earnings. The controls for nonlinearity ( FE*FELin , Revision*STRev- Lin , and Revision*LTRevLin ) indicate a reduction in the response to FE and 22 L. Rees, W. Thomas 123
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Table 5 Mean coefficients (and t -statistics) from quarterly cross-sectional regressions Variable a Predicted Sign Full sample b Zero-surprise sample b Coefficient t -stat. Coefficient t -stat. Intercept 0.009 1.160 - 0.012 - 0.750 Rank of D Dispersion c - - 0.011*** - 7.000 - 0.015*** - 2.710 FE + 28.478*** 7.800 FE*FELin - - 1.535*** - 5.490 FE*Loss - - 1.016 - 1.280 FE*Market Value - - 0.238** - 2.340 FE* Rank of Expected Growth d + 0.311*** 3.420 FE* Rank of Dispersion e - - 9.643*** - 9.400 ST Revision + 53.446*** 15.290 49.516*** 3.820 ST Revision*STRevLin - - 5.209*** - 13.040 - 4.574* - 1.960 LT Revision + 0.012*** 2.630 0.004 0.330 LT Revision*LTRevLin - - 0.001** - 2.050 0.000 - 0.180 Market Value - - 0.001** - 2.190 0.001 0.910 Rank of Expected Growth d + 0.001** 2.210 0.000 0.290 Rank of Dispersion e + 0.009*** 5.830 0.010*** 2.810 Rank of Book-to-Market d + 0.001*** 3.890 0.001*** 2.920 Beat + 0.007*** 4.310 Beat*PastBeat - 0.000* - 1.880 PastBeat - - 0.001*** - 2.970 Loss - - 0.001 - 0.330 0.008 0.410 Loss*PastLoss + 0.001 0.670 - 0.001 - 0.320 Decr - 0.004*** 3.440 0.002 0.780 Decr*PastDecr + 0.000 - 0.030 0.000 - 0.070 PreReturn - - 0.078*** - 5.980 - 0.115*** - 8.290 N 62,706 8,056 Adjusted R 2 0.145 0.073 a See Table 1 for variable definitions b The full sample consists of all available observations. The zero-surprise sample consists of firms that have current forecast errors ( FE
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