assessing the market’s use of analyst estimates and quarterly earnings announcements
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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 - PowerPoint PPT PresentationTRANSCRIPT
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 Accounting for dispersion—last time used one
interaction term Left out analysis of overnight returns/intraday
returns Any systematic patterns? Conclusion
Problem from last time with sampling frequency
Previously, saw that sampling at 10 minutes and 15 minutes gives contradictory results
Sub-sampling provides consistencyWal-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.
(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.
Dispersion and Returns, Case StudyMcDonald’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
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.
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
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…
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.
“Expected” Returns – Wal-Mart againOvernight 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
Wal-MartIntraday 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
------------------------------------------------------------------------------
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
------------------------------------------------------------------------------
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).
Breakdown of Estimate Days Dummies Dispersion Overnight Return Intraday Return
Positive Negative Meet Beat Miss Meet Dispersion Beat Miss Meet Beat Miss Meet
XOM 30 12 2 0.67* 1.51** X no 0.48** -0.96** X -.32 X X
PG 31 2 10 2.00** X 1.62** no X X X .46** 1.24 -.6*
GE 10 6 27 X X 1.12** no .54** X X -.76* X -.35
T 35 6 2 2.33** 5.76** X pos only .34* -2.38** X -.42* X X
JNJ 30 6 7 1.63** 1.31* 1.58** pos only .57** X 0.41 X X X
CVX 12 13 1 X 1.76** X no .64** -.6** X X X X
MSFT 32 5 6 X X X meet only X X X X X X
AMZN 14 23 5 3.41** 2.65** X neg only 1.92** -1.93** -.933* .66* -.38 X
WMT 28 6 10 0.7 X X no .63** -2.16** X X X X
JPM 26 14 2 2.14** 2.39** X neg only .46** X -0.79 X X X
IBM 30 5 8 0.56 X 2.82** meet only .80** -.83* -1.96** -.31 -.76 X
HPQ 17 2 4 2.28** X 2.12 no 1.84** -9.54** 1.28** -.54* X -1.06*
WFC 13 17 13 X 1.65** X neg only .38* -.71** X 0.47 0.43 X
VZ 16 3 12 1.03* X 2.10** no X X -.31 X X 0.48
CSCO 35 1 8 X X 2.09* no .57** -3.57** -2.07 X X -0.73*
Breakdown of Estimate Days Dummies Dispersion Overnight Return Intraday Return
Positive Negative Meet Beat Miss Meet Dispersion Beat Miss Meet Beat Miss Meet
KO 27 6 10 1.23** 1.56** 1.56** no .78** X -.48* -0.36 1.10** -.58
PEP 24 8 11 1.81** 1.54** 2.87** yes .91** .63** X -.52** .73* X
ABT 10 3 30 2.23** X 1.86** no X X X X X X
INTC 28 10 5 1.43** X 4.39** no .37* -2.83** X -.42* X X
AAPL 37 4 2 1.6** X X no .83** X X X X X
BAC 31 7 4 X 5.30** X no .30* -1.32** -.93** X X X
MRK 17 7 19 1.06* 2.58** 1.24* neg only .73** -2.20** X 1.00** -.93* .54*
AMGN 31 7 5 X X 4.37** no .33* -1.12** X X X X
QCOM 34 5 3 2.13** X X no .63** X X X 1.16** X
MCD 14 10 19 3.13** 4.44** 2.24** yes X X X X X -.42
UPS 19 5 9 2.00** 2.96** 3.29**neg and meet .65** -3.19** X X 1.25** X
UTX 40 1 2 2.39** 4.09** X pos only .62** X X X X X
GS 32 3 1 2.26** X X no X .93** 1.63** X X -1.81
SLB 24 13 6 2.03** 3.04** X no 0.2 -.73** X X X X
WYE 16 6 1 1.96** 3.11** X no .64** -3.24** X X X X
Breakdown of data – number of significant results
Magnitude
Dummies Disp
Overnight Return
Intraday Return
Pos Neg Beat
Miss
Meet
NA Beat
Miss Meet
Beat
Miss Meet
# sig.
19 14 23 16 15 NA 24 18 10 12 9 9
Magnitude
Dummies Disp
Overnight Return
Intraday Return
Pos Neg Beat
Miss
Meet
NA Beat
Miss Meet
Beat
Miss Meet
# sig.
9 6 11 7 12 NA 10 7 5 7 7 7
Magnitude
Dummies Disp
Overnight Return
Intraday Return
Pos Neg Beat
Miss
Meet
NA Beat
Miss Meet
Beat
Miss Meet
# sig. 6 7 8 10 5 NA 11 10 3 6 4 2
Firms with 7 or more quarterly earnings misses: 12 firms
Firms with 7 or more quarterly earnings meeting of expectations: 14 firms
All firms: 30 firms
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.