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 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.