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 consistency Wal-Mart sub-sampled at 15 minutes Source | SS df MS Number of obs = F( 6, 2892) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] RV1 | RV5 | RV22 | pos | neg | meet | _cons | 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 = F( 6, 2892) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] RV1 | RV5 | RV22 | pos | neg | meet | _cons | Now results are consistent in that positive surprises are statistically significant both times.

Dispersion and Returns, Case Study McDonald’s Source | SS df MS Number of obs = F( 6, 2896) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] RV1 | RV5 | RV22 | pos | neg | meet | _cons | 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 = F( 6, 2896) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] RV1 | RV5 | RV22 | beat | miss | meet | _cons | 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 = F( 10, 2892) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] RV1 | RV5 | RV22 | pos | neg | meet | dispersion | pos*disp | neg*disp | meet*disp | _cons | Dispersion is significantly correlated

Overnight Returns – McDonald’s Source | SS df MS Number of obs = F( 3, 2899) = 0.91 Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = ONreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] beat | miss | meet | _cons | Expected there to be significant results…

Intraday Returns – McDonald’s Source | SS df MS Number of obs = F( 3, 2899) = 1.22 Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = IDreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] beat | miss | meet | _cons | Meets expectations significant (?), but F-statistic low so results are expected.

“Expected” Returns – Wal-Mart again Overnight Returns Source | SS df MS Number of obs = F( 3, 2895) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = ONreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] beat | miss | meet | _cons | /9/97 to 1/7/09, 28 positive surprises, 6 negative, 10 meets expectations

Wal-Mart Intraday Returns Source | SS df MS Number of obs = F( 3, 2895) = 1.00 Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = IDreturn | Coef. Std. Err. t P>|t| [95% Conf. Interval] beat | miss | meet | _cons |

But with Dispersion, breaks down (Wal-Mart) Source | SS df MS Number of obs = F( 9, 2889) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = RVt | Coef. Std. Err. t P>|t| [95% Conf. Interval] RV1 | RV5 | RV22 | pos | neg | meet | disp | pos*disp | neg*disp | meet*disp |

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 DaysDummies DispersionOvernight Return Intraday Return PositiveNegativeMeetBeatMissMeetDispersionBeatMissMeetBeatMissMeet XOM *1.51**Xno0.48**-0.96**X-.32XX PG **X1.62**noXXX.46** * GE10627XX1.12**no.54**XX-.76*X-.35 T **5.76**Xpos only.34*-2.38**X-.42*XX JNJ **1.31*1.58**pos only.57**X0.41XXX CVX12131X1.76**Xno.64**-.6**XXXX MSFT3256XXXmeet onlyXXXXXX AMZN **2.65**Xneg only1.92**-1.93**-.933*.66*-.38X WMT XXno.63**-2.16**XXXX JPM **2.39**Xneg only.46**X-0.79XXX IBM X2.82**meet only.80**-.83*-1.96** X HPQ **X2.12no1.84**-9.54**1.28**-.54*X-1.06* WFC131713X1.65**Xneg only.38*-.71**X X VZ *X2.10**noXX-.31XX0.48 CSCO3518XX2.09*no.57**-3.57**-2.07XX-0.73*

Breakdown of Estimate DaysDummies DispersionOvernight Return Intraday Return PositiveNegativeMeetBeatMissMeetDispersionBeatMissMeetBeatMissMeet KO **1.56** no.78**X-.48* **-.58 PEP **1.54**2.87**yes.91**.63**X-.52**.73*X ABT **X1.86**noXXXXXX INTC **X4.39**no.37*-2.83**X-.42*XX AAPL **XXno.83**XXXXX BAC3174X5.30**Xno.30*-1.32**-.93**XXX MRK *2.58**1.24*neg only.73**-2.20**X1.00**-.93*.54* AMGN3175XX4.37**no.33*-1.12**XXXX QCOM **XXno.63**XXX1.16**X MCD **4.44**2.24**yesXXXXX-.42 UPS **2.96**3.29** neg and meet.65**-3.19**XX1.25**X UTX **4.09**Xpos only.62**XXXXX GS **XXnoX.93**1.63**XX-1.81 SLB **3.04**Xno **XXXX WYE **3.11**Xno.64**-3.24**XXXX

Breakdown of data – number of significant results MagnitudeDummiesDispOvernight ReturnIntraday Return PosNegBeatMissMeetNABeatMissMeetBeatMissMeet # sig NA MagnitudeDummiesDispOvernight ReturnIntraday Return PosNegBeatMissMeetNABeatMissMeetBeatMissMeet # sig NA MagnitudeDummiesDispOvernight ReturnIntraday Return PosNegBeatMissMeetNABeatMissMeetBeatMissMeet # sig NA 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.