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Assessing the Market’s Use of Analyst Estimates and Quarterly Earnings Announcements

Assessing the Market’s Use of Analyst Estimates and Quarterly Earnings Announcements. Sam Lim. To cover. Continue exploring impact of beating/missing/meeting analyst estimates on price. Issues remaining from last presentation Sampling frequency had very significant impact on results

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Assessing the Market’s Use of Analyst Estimates and Quarterly Earnings Announcements

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  1. Assessing the Market’s Use of Analyst Estimates and Quarterly Earnings Announcements Sam Lim

  2. To cover • Continue exploring impact of beating/missing/meeting analyst estimates on price. • Issues remaining from last presentation • Sampling frequency had very significant impact on results • Accounting for dispersion—last time used one interaction term • Left out analysis of overnight returns/intraday returns • Any systematic patterns? • Conclusion

  3. Problem from last time with sampling frequency Previously, saw that sampling at 10 minutes and 15 minutes gives contradictory results

  4. Sub-sampling provides consistency Wal-Mart sub-sampled at 15 minutes Source | SS df MS Number of obs = 2899 -------------+------------------------------ F( 6, 2892) = 712.04 Model | 17691.8423 6 2948.64039 Prob > F = 0.0000 Residual | 11976.0998 2892 4.14111336 R-squared = 0.5963 -------------+------------------------------ Adj R-squared = 0.5955 Total | 29667.9422 2898 10.2373851 Root MSE = 2.035 ------------------------------------------------------------------------------ RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- RV1 | .303276 .0221944 13.66 0.000 .2597576 .3467945 RV5 | .3500639 .0372137 9.41 0.000 .2770959 .4230319 RV22 | .276637 .0324942 8.51 0.000 .2129228 .3403512 pos | .1437678 .0701733 2.05 0.041 .0061731 .2813625 neg | -.1855239 .2597124 -0.71 0.475 -.694764 .3237161 meet | .3114457 .6447161 0.48 0.629 -.9527037 1.575595 _cons | .3975586 .0965612 4.12 0.000 .2082229 .5868943 ------------------------------------------------------------------------------ Note on variables: Pos=positive surprise, neg=negative surprise (magnitude of surprise). Beat, miss, and meet are dummy variables. Wal-Mart data from 4/9/97 to 1/7/09, 28 positive surprises, 14 negative, 2 meets exp.

  5. (Continued) Wal-Mart sub-sampled at 10 minutes Source | SS df MS Number of obs = 2899 -------------+------------------------------ F( 6, 2892) = 819.28 Model | 19070.8513 6 3178.47522 Prob > F = 0.0000 Residual | 11219.8246 2892 3.87960739 R-squared = 0.6296 -------------+------------------------------ Adj R-squared = 0.6288 Total | 30290.6759 2898 10.4522691 Root MSE = 1.9697 ------------------------------------------------------------------------------ RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- RV1 | .327721 .022175 14.78 0.000 .2842406 .3712015 RV5 | .3462127 .0366381 9.45 0.000 .2743734 .4180521 RV22 | .2615723 .0313595 8.34 0.000 .200083 .3230616 pos | .1434613 .0679122 2.11 0.035 .0103002 .2766224 neg | -.241247 .2514104 -0.96 0.337 -.7342087 .2517147 meet | .3386972 .6240845 0.54 0.587 -.8849981 1.562393 _cons | .3759649 .0934038 4.03 0.000 .1928202 .5591096 ------------------------------------------------------------------------------ Now results are consistent in that positive surprises are statistically significant both times.

  6. Dispersion and Returns, Case Study McDonald’s Source | SS df MS Number of obs = 2903 -------------+------------------------------ F( 6, 2896) = 546.06 Model | 12011.5913 6 2001.93188 Prob > F = 0.0000 Residual | 10617.1323 2896 3.66613684 R-squared = 0.5308 -------------+------------------------------ Adj R-squared = 0.5298 Total | 22628.7235 2902 7.79763044 Root MSE = 1.9147 ------------------------------------------------------------------------------ RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- RV1 | .2770757 .0216917 12.77 0.000 .2345429 .3196084 RV5 | .3339961 .0381714 8.75 0.000 .2591502 .408842 RV22 | .308857 .0349661 8.83 0.000 .2402961 .3774179 pos | .1349859 .0503122 2.68 0.007 .0363345 .2336372 neg | -.5912832 .128214 -4.61 0.000 -.8426831 -.3398832 meet | 2.222736 .4408345 5.04 0.000 1.358355 3.087117 _cons | .4453212 .1081768 4.12 0.000 .2332098 .6574325 ------------------------------------------------------------------------------ Note on variables: Pos=positive surprise, neg=negative surprise (magnitude of surprise). Beat, miss, and meet are dummy variables. 4/9/97 to 1/7/09, 14 positive surprises, 10 negative, 19 meets expectations

  7. Same Idea using Dummies Source | SS df MS Number of obs = 2903 -------------+------------------------------ F( 6, 2896) = 568.58 Model | 12239.0063 6 2039.83439 Prob > F = 0.0000 Residual | 10389.7172 2896 3.58760954 R-squared = 0.5409 -------------+------------------------------ Adj R-squared = 0.5399 Total | 22628.7235 2902 7.79763044 Root MSE = 1.8941 ------------------------------------------------------------------------------ RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- RV1 | .2746655 .0214609 12.80 0.000 .2325854 .3167456 RV5 | .3353536 .0377718 8.88 0.000 .2612913 .409416 RV22 | .3063925 .0345986 8.86 0.000 .2385521 .374233 beat | 3.130044 .5075778 6.17 0.000 2.134794 4.125294 miss | 4.441939 .6002818 7.40 0.000 3.264916 5.618961 meet | 2.239534 .436092 5.14 0.000 1.384452 3.094616 _cons | .4475078 .1069679 4.18 0.000 .2377669 .6572486 ------------------------------------------------------------------------------ Note on variables: Pos=positive surprise, neg=negative surprise (magnitude of surprise). Beat, miss, and meet are dummy variables.

  8. Accounting for Dispersion Source | SS df MS Number of obs = 2903 -------------+------------------------------ F( 10, 2892) = 339.95 Model | 12227.0842 10 1222.70842 Prob > F = 0.0000 Residual | 10401.6394 2892 3.59669411 R-squared = 0.5403 -------------+------------------------------ Adj R-squared = 0.5387 Total | 22628.7235 2902 7.79763044 Root MSE = 1.8965 ------------------------------------------------------------------------------ RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- RV1 | .2758763 .0215008 12.83 0.000 .2337178 .3180348 RV5 | .3273023 .0378597 8.65 0.000 .2530675 .401537 RV22 | .3158067 .0346752 9.11 0.000 .247816 .3837974 pos | 1.35302 .2714459 4.98 0.000 .8207732 1.885267 neg | -1.331312 .5274456 -2.52 0.012 -2.365519 -.297105 meet | 2.446963 .868413 2.82 0.005 .7441922 4.149734 dispersion | 367.801 48.98837 7.51 0.000 271.7454 463.8567 pos*disp | -142.8803 27.76458 -5.15 0.000 -197.3207 -88.43996 neg*disp | 133.9182 50.89393 2.63 0.009 34.12615 233.7102 meet*disp | -392.6746 101.7286 -3.86 0.000 -592.1425 -193.2067 _cons | .4366835 .1071948 4.07 0.000 .2264976 .6468695 Dispersion is significantly correlated

  9. Overnight Returns – McDonald’s Source | SS df MS Number of obs = 2903 -------------+------------------------------ F( 3, 2899) = 0.91 Model | 1.07107029 3 .357023429 Prob > F = 0.4343 Residual | 1134.81593 2899 .39145082 R-squared = 0.0009 -------------+------------------------------ Adj R-squared = -0.0001 Total | 1135.887 2902 .39141523 Root MSE = .62566 ------------------------------------------------------------------------------ ONreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- beat | -.0660544 .1676235 -0.39 0.694 -.3947276 .2626189 miss | -.2328604 .1981968 -1.17 0.240 -.6214812 .1557603 meet | .1569062 .1440123 1.09 0.276 -.1254706 .4392831 _cons | -.0114143 .0116992 -0.98 0.329 -.0343539 .0115252 ------------------------------------------------------------------------------ Expected there to be significant results…

  10. Intraday Returns – McDonald’s Source | SS df MS Number of obs = 2903 -------------+------------------------------ F( 3, 2899) = 1.22 Model | 3.43575787 3 1.14525262 Prob > F = 0.3014 Residual | 2725.08804 2899 .940009673 R-squared = 0.0013 -------------+------------------------------ Adj R-squared = 0.0002 Total | 2728.5238 2902 .940221847 Root MSE = .96954 ------------------------------------------------------------------------------ IDreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- beat | -.0760549 .2597542 -0.29 0.770 -.5853763 .4332666 miss | -.0602419 .3071313 -0.20 0.845 -.6624596 .5419758 meet | -.4199334 .2231656 -1.88 0.060 -.8575126 .0176458 _cons | .0445392 .0181294 2.46 0.014 .0089914 .080087 ------------------------------------------------------------------------------ Meets expectations significant (?), but F-statistic low so results are expected.

  11. “Expected” Returns – Wal-Mart again Overnight Returns Source | SS df MS Number of obs = 2899 -------------+------------------------------ F( 3, 2895) = 34.63 Model | 39.8397927 3 13.2799309 Prob > F = 0.0000 Residual | 1110.31725 2895 .383529275 R-squared = 0.0346 -------------+------------------------------ Adj R-squared = 0.0336 Total | 1150.15704 2898 .396879588 Root MSE = .6193 ------------------------------------------------------------------------------ ONreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- beat | .6305918 .1176087 5.36 0.000 .3999865 .861197 miss | -2.158172 .2530926 -8.53 0.000 -2.654432 -1.661912 meet | -.2758238 .1961817 -1.41 0.160 -.6604937 .1088461 _cons | .0509484 .0115903 4.40 0.000 .0282223 .0736746 ------------------------------------------------------------------------------ 4/9/97 to 1/7/09, 28 positive surprises, 6 negative, 10 meets expectations

  12. Wal-Mart Intraday Returns Source | SS df MS Number of obs = 2899 -------------+------------------------------ F( 3, 2895) = 1.00 Model | 2.72609705 3 .908699017 Prob > F = 0.3935 Residual | 2640.4452 2895 .91207088 R-squared = 0.0010 -------------+------------------------------ Adj R-squared = -0.0000 Total | 2643.1713 2898 .91206739 Root MSE = .95502 ------------------------------------------------------------------------------ IDreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- beat | .2708877 .1813654 1.49 0.135 -.0847307 .6265061 miss | -.1457403 .3902964 -0.37 0.709 -.9110271 .6195466 meet | .2392901 .3025336 0.79 0.429 -.3539128 .832493 _cons | -.0398925 .0178736 -2.23 0.026 -.0749387 -.0048463 ------------------------------------------------------------------------------

  13. But with Dispersion, breaks down (Wal-Mart) Source | SS df MS Number of obs = 2899 -------------+------------------------------ F( 9, 2889) = 547.59 Model | 19096.3423 9 2121.81581 Prob > F = 0.0000 Residual | 11194.3336 2889 3.87481259 R-squared = 0.6304 -------------+------------------------------ Adj R-squared = 0.6293 Total | 30290.6759 2898 10.4522691 Root MSE = 1.9685 ------------------------------------------------------------------------------ RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- RV1 | .3279283 .0221629 14.80 0.000 .2844716 .3713849 RV5 | .3464192 .0366327 9.46 0.000 .2745903 .4182482 RV22 | .262611 .0313497 8.38 0.000 .2011409 .324081 pos | -.0306947 .0994789 -0.31 0.758 -.2257515 .1643621 neg | -.6918537 .7089188 -0.98 0.329 -2.081891 .6981838 meet | .0714671 .7601663 0.09 0.925 -1.419056 1.56199 disp | 33.68857 54.54979 0.62 0.537 -73.27186 140.649 pos*disp | 26.74131 16.43577 1.63 0.104 -5.485717 58.96834 neg*disp | 59.65674 76.95299 0.78 0.438 -91.23157 210.545 meet*disp | .3647323 .0935536 3.90 0.000 .1812938 .5481708 ------------------------------------------------------------------------------

  14. Any Systematic Pattern? • Run the 5 different tests (using magnitudes, using dummies, accounting for dispersion, overnight returns, intraday returns) for various firms in S&P 100. • Ran tests for 30 firms, chose the largest in the S&P 100 by market cap (excluding Phillip Morris, Google, and Oracle).

  15. Breakdown of data – number of significant results All firms: 30 firms Firms with 7 or more quarterly earnings misses: 12 firms Firms with 7 or more quarterly earnings meeting of expectations: 14 firms

  16. Conclusion • Unfortunately, no nice systematic pattern, but can make some rough generalizations. • Sub-sampling helps to bring more consistent results. • At very least, can back up using a different method (HAR-RV) the research done by Beaver (1968) and Landsman and Maydew (2002). • An earnings surprise in general is strongly correlated with overnight returns in the same direction, not so much intraday returns. • Market adjusts fairly quickly to news. • Research corroborates idea that negative news has larger impact than positive news. Of the 13 firms where both beat and miss days were significantly correlated with volatility, 10 of them had larger coefficients on miss days. Of the 17 firms where both days were sig. correlated with overnight returns, 15 had larger coefficients on miss days. • The results seem to indicate that market responds more to fact that there is a negative or positive surprise than the actual magnitude (corroborates research by Kinney et al. in 2002). • Dispersion – got lucky with McDonald’s, is significant with some firms but not all.

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