Earnings Surprises, Growth Expectations, and Stock Returns:

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Our basic sample consists of approximately 103,000 firm-quarters, giving us potentially 2.06 million regression observations as we track each firm-quarter over the subsequent 20 quarters. The actual ‘full’ sample is on the order of 1.4 million observations, primarily because we lose firm-quarters at the end of our sample period as we move forward through the 20 quarters, and because of missing earnings announcement dates. We conduct our regression results both at the annual level, where we include each of the four firm-quarters from each of the five subsequent years, and the five-year level, where we include all 20 firm-quarters. This regression approach results leads to a dependence problem, because each quarterly return observation can be included as the dependent variable up to four times in the annual regressions and up to 20 times in the five-year regressions. To control for this problem, we adjust the t-statistics by dividing by the square root of the maximum number of times each observation can enter the regression. For example, in the annual-level regressions, we divide by √4. Similarly, in the five-year level regressions, we divide by √20. If all observations entered the maximum number of times, then this procedure would be asymptotically equivalent to using generalized least squares with a residual variance-covariance matrix that sets each of the off-diagonal elements relating to the same dependent variable observation equal to the residual variance. However, because not all observations enter the maximum number of times, our procedure will lead to a slight downward bias in our ‘adjusted’ t-statistics. The F-statistics are adjusted in a similar manner, dividing by 4 at the annual level and 20 at the five-year level. 23
Turning to the regression results in table 3, we first estimate a simple regression of return on growth. As expected based on previous research, growth loads with a significantly negative coefficient in each of the five years. The coefficients have a simple interpretation in this regression. The intercept provides an estimate of the expected quarterly abnormal return for the low growth quintile, and the coefficient on growth provides an estimate of the expected quarterly abnormal return differential between adjacent growth quintiles. Focusing on the ‘All 20 Quarters’ regression, the intercept is 0.0065 (t = 5.36) and the coefficient on growth is –0.0032 (t = –6.56). These coefficients indicate an annual abnormal return to the lowest growth quintile of 2.6% (4 x .65%) and an annual abnormal return to the highest growth quintile of –2.5% {4 x [0.65% – 4 x 0.32%]} for an annual average differential of 5.1%. The next regression includes growth, surprise (defined earlier as a +1/0/–1 indicator variable reflecting the sign of the earnings surprise), and a surprise*growth interaction. This regression allows the sensitivity of abnormal returns to earnings surprises to vary as a function of the growth quintile to which the stock belongs. The coefficient on growth remains negative in all regressions and is statistically significant in all regressions except for year 4. As expected, the coefficient on surprise is consistently positive and highly statistically significant, indicating that stock returns are correlated with the sign of earnings surprises. In addition, the coefficient on the surprise*growth interaction is consistently positive and statistically significant, indicating that the stock returns of high growth firms are more responsive to earnings surprises than those of low growth firms. The final regression in table 3 also includes the earnings forecast error (defined earlier) and a forecast error*growth interaction. Surprise, forecast error, and 24
Page 1 and 2: Earnings Surprises, Growth Expectat
Page 3 and 4: Our paper also resolves the inconcl
Page 5 and 6: Our first example is the case of Or
Page 7 and 8: Our first prediction concerns the r
Page 9 and 10: and growth stocks is concentrated i
Page 11 and 12: 3. Sample and research design We ob
Page 13 and 14: constructed by allocating all firm-
Page 15 and 16: on the sign of the earnings surpris
Page 17 and 18: esult if they miss and report a neg
Page 19 and 20: and then track the mean stock retur
Page 21 and 22: to positive and negative surprises.
Page 23: As an alternative specification, we
Page 27 and 28: Consistent with our predictions, th
Page 29 and 30: interest of brevity, we only report
Page 31 and 32: 4.3 Alternative measures of growth
Page 33 and 34: MRET(HML)t = -.010 + .090*MSURP(HML
Page 35 and 36: plausible, since it implies that th
Page 37 and 38: References Barberis, Nicholas, Andr
Page 39 and 40: Figure 1 Summary financial statisti
Page 41 and 42: Figure 3 Illustration of the altern
Page 43 and 44: Abnormal Return 0.2 0.15 0.1 0.05 0
Page 45 and 46: Table 2 Mean Quarterly Abnormal Sto
Page 47 and 48: Quarters from: (number of obs.) Yea
Page 49 and 50: Period (obs.) Intercept Growth Year
Page 51 and 52: Quarters from: (no. of obs.) Year 1
Page 53 and 54: Table 5 Estimated Coefficients (adj
Page 55 and 56: Quarters from: (no. of obs.) Year 5
Page 57 and 58: Return Measurement Interval Fullret
Page 59: Growth Measure MB .0065 (5.36) MB .

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