Simon Benninga. Financial Modelling 3-rd edition

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378 Chapter 14

The regression results indicate the normal behavior of Gillette in the

estimation window:

Gillette,t

= 0.0007 + 0.6364r

NYSE,t

The Steyx function measures the standard error of the regression-

predicted y-values. In the next subsection we show how to use this value

to measure the signiﬁ cance of the event’s abnormal returns.

14.3.2 The Event Window

We deﬁ ne the event window as two days before and three days after the

announcement. To measure the impact of the announcement effect in

the event window we use the market model r

Gillette,t

= 0.0007 + 0.6356r

NYSE,t

The formulas in the event window are in the preceding spreadsheet. As

you can see, the announcement of the acquisition of Gillette by Procter

& Gamble led to several large abnormal Gillette returns in the event

window.

We can use Steyx, the standard error of the regression prediction, to

measure the signiﬁ cance of the abnormal returns. Only two of the abnor-

mal returns—on the event date 28 January and the day following—are

actually signiﬁ cant at the 5 percent level:

258

259

260

261

262

263

264

265

266

267

268

269

ABCDEFGH

Intercept 0.0007 <

=INTERCEPT($C$11:$C$262,$B$11:$B$262)

Slope 0.6364 <

=SLOPE($C$11:$C$262,$B$11:$B$262)

R-squared 0.1315 <

=RSQ($C$11:$C$262,$B$11:$B$262)

Steyx 0.0113 <

=STEYX($C$11:$C$262,$B$11:$B$262)

Days in estimation

window

252 <

=COUNT(A11:A262)

Date NYSE Gillette

Abnormal

return

(

)

t-test

nicant?

27-Jan-04 -0.48% -0.42%

28-Jan-04 -1.26% -1.27%

29-Jan-04 0.00% -0.94%

30-Jan-04 -0.06% -1.39%

2-Feb-04 0.26% -0.74%

19-Jan-05 -0.78% -0.09%

20-Jan-05 -0.69% -0.56%

21-Jan-05 -0.20% -1.50%

24-Jan-05 -0.18% 0.57%

25-Jan-05 0.21% 1.44%

26-Jan-05 0.68% 0.07% -0.44% -0.39 no

27-Jan-05 0.04% 1.89% 1.80% 1.59 no

28-Jan-05 -0.24% 12.94% 13.03% 11.56 yes

31-Jan-05 0.82% -1.71% -2.30% -2.04 yes

1-Feb-05 0.80% -0.83% -1.40% -1.24 no

2-Feb-05 0.32% 0.80% 0.52% 0.46 no

3-Feb-05 -0.29% -0.59%

Cell F263 contains formula

=IF(ABS(E263)<1.96,"no","yes")

EVENT WINDOW

GILLETTE RETURNS: THE SIGNIFICANCE

OF THE ABNORMAL RETURNS

Cell D263 contains formula

=C263-($B$2+$B$3*B263)

Cell E263 contains formula

=D263/$B$5

379 Event Studies

We deﬁ ne the test statistic for the abnormal return AR by dividing AR

by the Steyx in cell B5. Assuming that the regression residuals are nor-

mally distributed, if the absolute value of the test statistic is larger than

1.96, then the abnormal return is signiﬁ cant at the 95 percent level

(meaning that the chances that the abnormal return is random and insig-

niﬁ cant is less than 5 percent). If the test statistic is larger than 2.58, its

signiﬁ cance level is 1 percent. As can be seen from rows 263–268, at the

1 percent level, only the announcement itself has a signiﬁ cant abnormal

return.

14.3.3 What about Procter & Gamble?

Thus far we have concentrated on the event inﬂ uence on the takeover

target, Gillette. Applying the same methodology to Procter & Gamble’s

stock returns shows that the announcement had a negative impact on its

stock returns. There may also have been some leakage of the information

prior to the announcement on 28 January 2005:

6. One limitation of Steyx is that the variance is slightly understated. The true variance

of the market model is the estimation variance from Steyx and the additional variance

due to the sampling error in a

and b

. However, the sampling error approaches zero

as the length of the estimation window increases. Since we suggest using 252 trading

days in the estimation window, the effect of the sampling error is minimal, and it is,

therefore, often disregarded when calculating the variance of the abnormal returns.

380 Chapter 14

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

ABCDEFGH

Intercept 0.0004 <

=INTERCEPT($C$11:$C$262,$B$11:$B$262)

Slope 0.5877 <

=SLOPE($C$11:$C$262,$B$11:$B$262)

R-squared 0.1872 <

=RSQ($C$11:$C$262,$B$11:$B$262)

Steyx 0.0084 <

=STEYX($C$11:$C$262,$B$11:$B$262)

Days in estimation

window

252 <

=COUNT(A11:A262)

Date NYSE Procter-Gamble

Abnormal

return (AR)

t-test

signicant?

Cumulative

abnormal

return

27-Jan-04 -0.48% -0.65%

28-Jan-04 -1.26% -0.56%

29-Jan-04 0.00% 2.41%

30-Jan-04 -0.06% 0.08%

2-Feb-04 0.26% 0.59%

19-Jan-05 -0.78% 1.69%

20-Jan-05 -0.69% 0.04%

21-Jan-05 -0.20% -1.85%

24-Jan-05 -0.18% -0.79%

25-Jan-05 0.21% 0.94%

26-Jan-05 0.68% -0.52% -0.96% -1.14 no -0.96% <

=D263

27-Jan-05 0.04% -0.22% -0.28% -0.33 no -1.25% <

=G263+D264

28-Jan-05 -0.24% -2.12% -2.02% -2.39 yes -3.26% <

=G264+D265

31-Jan-05 0.82% -1.70% -2.23% -2.64 yes -5.49%

1-Feb-05 0.80% -0.68% -1.19% -1.41 no -6.67%

2-Feb-05 0.32% 1.00% 0.77% 0.91 no -5.90%

3-Feb-05 -0.29% -0.30%

4-Feb-05 0.97% -1.16%

7-Feb-05 -0.23% -0.17%

8-Feb-05 0.09% -1.05%

9-Feb-05 -0.63% -0.90%

10-Feb-05 0.68% 0.21%

11-Feb-05 0.71% 1.03%

14-Feb-05 0.26% 1.05%

15-Feb-05 0.32% 1.48%

16-Feb-05 0.04% -0.32%

17-Feb-05 -0.47% 0.04%

18-Feb-05 0.21% 0.32%

22-Feb-05 -1.05% -2.34%

23-Feb-05 0.45% 2.37%

24-Feb-05 0.55% 0.64%

25-Feb-05 1.08% 0.67%

28-Feb-05 -0.55% -2.03%

1-Mar-05 0.41% 0.79%

Cell F263 contains formula

=IF(ABS(E263)<1.96,"no","yes")

PROCTER & GAMBLE RETURNS: ESTIMATION WINDOW

AND EVENT WINDOW

EVENT WINDOW

Cell D263 contains formula

=C263-($B$2+$B$3*B263)

Cell E263 contains formula

=D263/$B$5

-7%

-6%

-5%

-4%

-3%

-2%

-1%

26-Jan 27-Jan 28-Jan 31-Jan 01-Feb 02-Feb

Abnormal return (AR)

Cumulative abnormal return

381 Event Studies

14.3.4 Summary: What Happened on the Announcement Date?

On 28 January 2005, Procter & Gamble announced the purchase of Gil-

lette. Each share of Gillette was purchased for 0.975 shares of Procter &

Gamble. At the 5 percent signiﬁ cance level, the acquisition announce-

ment had signiﬁ cant effects on the stock prices of Gillette and Procter

& Gamble only on the announcement date and the day after. After an

initial positive impact on Gillette (a 13.03 percent increase in the nor-

mally anticipated stock return on the event date 28 January and a further

−2.30 percent on 31 January) and an initial negative impact on Procter

& Gamble (−2.02 percent on 28 January and −2.23 percent on 31 January),

there were no additional signiﬁ cant effects on the stock prices. The cumu-

lative effects are as follows:

382 Chapter 14

The preceding table attempts to measure the short-term synergies of

the announcement by multiplying the CAR for Gillette and P&G times

their market value on the day before the event window. In the short

period of time measured by this event window, the cumulative surgery

appears to be negative, with the positive value creation for Gillette

shareholders outweighted by the negative impact on P&G.

14.4 A Fuller Event Study: Impact of Earnings Announcements on Stock Prices

In the previous section we used the event-study methodology to explore

the impact of a merger announcement on the returns of both the take-

over target (Gillette) and acquirer (Procter & Gamble). In this section

we show how to aggregate the returns of an event in order to evaluate

the market response to a particular type of event. We consider the effect

of earning announcements on a set of stores in the grocery industry.

AB C DEF G H I

outstanding

(thousands)

price,

25jan05

Market

value,

25jan05

(

billion $)

Gillette 1,000,000

44.53 44.53

P&G 2,741,000

53.49 146.62

Date

Gillette P&G Sum Gillette P&G Sum

26-Jan-05 -0.44% -0.96% -1.40% -0.19 -1.41 -1.61 <-- =F12+G12

27-Jan-05 1.80% -0.28% 1.52% 0.61 -1.83 -1.22

28-Jan-05 13.03% -2.02% 11.01% 6.41 -4.78 1.63

31-Jan-05 -2.30% -2.23% -4.52% 5.38 -8.04 -2.66

1-Feb-05 -1.40% -1.19% -2.59% 4.76 -9.78 -5.02

2-Feb-05 0.52% 0.77% 1.29% 4.99 -8.66 -3.66

GILLETTE PURCHASED BY PROCTER & GAMBLE

Measuring the synergies in event window

Abnormal returns (AR)

Cumulative abnormal valuations

(billion $)

Cell G12 contains formula

=$D$4*SUM($C$12:C12)/1000

Cell F12 contains formula

=$D$3*SUM($B$12:B12)/1000

7. A study commissioned by the Massachusetts Secretary of the Commonwealth William

F. Galvin after the merger offer suggests that the synergies of the merger, between

$22–$28 billion, were largely captured by Procter & Gamble. See http://www.sec.state

.ma.us/sct/sctpdf/Gillettefairnessreport.pdf (on the disk with this book) and http://

www.businessweek.com/bwdaily/dnﬂ ash/jun2005/nf20050616_1121_db016.htm.

383 Event Studies

14.4.1 An Initial Example: Safeway’s Positive Earnings Surprise on 20 July 2006

To set the stage, consider the earnings announcement made by Safeway

on 20 July 2006. On this date Safeway announced earnings per share

(EPS) of $0.42, a number which exceeded by 6 cents the market consen-

sus estimate of $0.36.

On the same day, the S&P 500 declined by 0.85

percent, and Safeway stock rose by 8.39 percent. The following spread-

sheet shows the example of Safeway’s earnings announcement on 20 July

2006. We use the event-study methodology to gauge the market reaction

to this earnings surprise:

Announcement date 20-Jul-06

Earnings per share $0.42

Consensus earnings estimate $0.36

Earnings surprise (forecast error) $0.06

Safeway 8.39%

S&P 500 -0.85%

Intercept 0.0001

9829.0 epolS

Steyx 0.0118

Expected return -0.78% <

=B13*B9+B12

Residual return 9.17% <

=B8-B17

t-statistic 7.75 <

=B18/B14

The residual return:

Expected stock returns versus actual returns

How did the market interpret the earnings surprise?

Using the market model

Regressing Safeway returns on the S&P returns:

Safeway = 0.0001 + 0.9289*SP

THE MARKET REACTION TO A POSITIVE EARNINGS

SURPRISE BY SAFEWAY, 20 July 2006

In cells B12 : B14 we have regressed Safeway daily returns on those of

the S&P 500 for the 252 trading days preceding the announcement. The

regression shows that r

Safeway

= 0.0001 + 0.9289*r

SP500

and that the standard

error of the estimate is 0.0118.

Given these data, we can see that on the day of the announcement the

anticipated return of Safeway, given the negative 0.85 percent return of

8. Yahoo is the source of our earnings surprise data (see p. 384).

384 Chapter 14

the S&P and absent the earnings surprise, should have been −0.78 percent.

This means that the abnormal return, measuring the impact of the earn-

ings announcement, was 9.17 percent (cell B18). The t-statistic for the

return was 7.75, showing that it is highly signiﬁ cant.

14.4.2 The Earnings Surprise Numbers

Our earnings surprise numbers are drawn from Yahoo, as shown in the

following picture. While Yahoo is an admirable source of data, it does

not provide a database of historical analyst estimates and actual earnings

numbers; such data are available from Bloomberg’s Best Consensus

Earnings Estimates and other commercial sources.

385 Event Studies

14.4.3 An Event Study: The Grocery Industry

We extend our Safeway study by considering 16 quarterly earnings

announcements by four grocery companies for ﬁ scal year 2006.

ABCDEFGHIJKL M N OP Q

Calendar Date Ticker Consensus Actual

Surprise:

=D3-C3

Starting

Point

Intercept Slope STEXY

Actual

Return

S&P 500

Return

Expected

Return:

=H3+I3*M3

Abnormal

Return:

=L3-N3

T-stat

7-Mar-06 KR 0.36 0.39 0.03 44 0.0003 0.6662 0.0123 1.36% -0.19% -0.09% 1.46% 1.1808 <

=O3/J3

20-Jun-06 KR 0.42 0.42 0.00 117 0.0004 0.6063 0.0113 5.05% 0.00% 0.04% 5.01% 4.4355 <

=O4/J4

12-Sep-06 KR 0.29 0.29 0.00 175 0.0006 0.5628 0.0108 -5.67% 1.03% 0.64% -6.31% -5.8201 <

=O5/J5

5-Dec-06 KR 0.28 0.30 0.02 234 0.0003 0.4483 0.0118 5.08% 0.40% 0.21% 4.87% 4.1297

18-Apr-06 SVU 0.56 0.55 -0.01 73 -0.0008 0.7049 0.0123 -0.28% 1.69% 1.11% -1.39% -1.1313

26-Jul-06 SVU 0.57 0.53 -0.04 142 -0.0003 0.5416 0.0122 -7.09% -0.04% -0.05% -7.03% -5.7632

10-Oct-06 SVU 0.53 0.61 0.08 195 -0.0002 0.6014 0.0128 4.36% 0.20% 0.10% 4.26%

3.3170

9-Jan-07 SVU 0.56 0.54 -0.02 256 0.0003 0.5238 0.0130 -1.70% -0.05% 0.00% -1.70% -1.3061

27-Apr-06 SWY 0.30 0.32 0.02 80 0.0001 1.0139 0.0130 2.88% 0.33% 0.34% 2.54% 1.9473

20-Jul-06 SW Y 0.36 0.42 0.06 138 0.0001 0.9289 0.0118 8.39% -0.85% -0.78% 9.17% 7.7519

12-Oct-06 SWY 0.39 0.39 0.00 197 0.0003 0.7533 0.0134 -1.43% 0.95% 0.75% -2.18% -1.6265

27-Feb-07 SWY 0.60 0.61 0.01 289 0.0012 0.7505 0.0131 -3.95% -3.53% -2.54% -1.41% -1.0814

4-May-06 WFMI 0.35 0.36 0.01 85 0.0006 0.8345 0.0175 12.50% 0.32% 0.33% 12.17% 6.9434

1-Aug-06 WFMI 0.34 0.35 0.01 146 -0.0007 1.2329 0.0164 -12.51% -0.45% -0.63% -11.88% -7.2345

3-Nov-06 WFMI 0.29 0.29 0.00 213 -0.0014 1.3199 0.0195 -26.21% -0.22% -0.43% -25.78%

-13.2186

22-Feb-07 WFMI 0.40 0.38 -0.02 286 -0.0020 1.5321 0.0243 13.13% -0.09% -0.33% 13.46% 5.5465

Positive

surprise

2.65%

None-positive

surprise

-3.24%

EARNINGS ANNOUNCEMENT IN 2006-07 FOR KROGER (KR), SUPERVALU (SVU),

SAFEWAY (SWY), AND WHOLE FOODS (WFMI)

Cell G18 contains formula:

=COUNTIF('Stock Prices'!$A$3:$A$551,"<="&TEXT(A18,"0"))-252

Cell H18 contains formula:

=INTERCEPT(OFFSET('Stock Prices'!$A$2,$G18,10,252,1),OFFSET('Stock

Prices'!$A$2,$G18,2,252,1))

Cell J18 contains formula:

=STEYX(OFFSET('Stock Prices'!$A$2,$G18,10,252,1),OFFSET('Stock

Prices'!$A$2,$G18,2,252,1))

Cell I18 contains formula:

=SLOPE(OFFSET('Stock Prices'!$A$2,$G18,10,252,1),OFFSET('Stock

Prices'!$A$2,$G18,2,252,1))

=SUMIF($E$3:$E$18,">0",$O$3:$O$

18)/COUNTIF(E3:E18,">0")

=SUMIF($E$3:$E$18,"<=0",$O$3:$O

$18

)

/COUNTIF

(

E3:E18,"<=0"

)

For each announcement we have determined the intercept and slope

of the market-response regression for the 252 days preceding the

announcement.

Following is a speciﬁ c example from the spreadsheet.

9. We only included Kroger, Supervalu, Safeway, and Whole Foods in the sample. This

is obviously an incomplete sample, both in terms of the ﬁ rms covered and the number

of announcements. However, this extended example is meant to impart the ﬂ avor of

a full-bodied event study.

10. The event window is deﬁ ned in column G by the “Starting Point,” which uses Countif

to locate a date 252 business days before the event date in the database of stock

returns. Notice that the “Starting Point” is used in the Intercept, Slope, Rsq formulas

in columns H, I, and J.

386 Chapter 14

Row 3 tracks the market model of Kroger stock to the S&P 500

in the 252 trading days before the earnings announcement on 7 March

2006. The market model is r

Kroger,t

= 0.003 + 0.662*r

SP,t

. Kroger’s

actual return on the announcement date, 1.36 percent, is 1.46

percent higher than the return that would have been predicted by its

market model. However, dividing this 1.46 percent by the standard

deviation of the abnormal returns (the regression residuals) (Steyx =

0.0123) gives a t-statistic of 1.1808, which is not signiﬁ cant at the 5 percent

level.

Row 18 tracks the market model of Whole Foods stock to the S&P

500 in the year before the earnings announcement after market close on

21 February 2007. The market model is r

Whole food,t

= −0.0020 + 1.5321*r

SP,t

with Steyx = 0.0243. The abnormal return on the day of the announce-

ment, 13.46 percent, is signiﬁ cant at the 1 percent level (hence the bold-

face in cell P18). To temper this interpretation, note that on the day of

the earnings announcement, Whole Foods announced the merger with

Wild Oats Markets; this second announcement makes it difﬁ cult to inter-

pret the true market reaction to the earnings release. In general, we have

to be careful interpreting abnormal returns when an event is announced

with confounding information.

14.4.4 Cumulative Abnormal Returns

We restate the data in slightly different form in the next spreadsheet—

using the OFFSET function—and then compute the abnormal returns

in an event window which goes from −10 to +10 days around the earnings

announcements:

ABCDEFGHIJKL M N OP

Calendar Date Ticker Consensus Actual

Surprise:

=D3-C3

Starting

Point

Intercept Slope STEXY

Actual

Return

S&P 500

Return

Expected

Return:

=H3+I3*M3

Abnormal

Return:

=L3-N3

T-stat

7-Mar-06 KR 0.36 0.39 0.03 44 0.0003 0.6662 0.0123 1.36% -0.19% -0.09% 1.46% 1.1808

22-Feb-07 WFMI 0.40 0.38 -0.02 286 -0.0020 1.5321 0.0243 13.13% -0.09% -0.33% 13.46% 5.5465

387 Event Studies

We can compute the average abnormal return (AAR) and cumulative

abnormal return (CAR) for each of the days in the event window. In the

following table we perform this computation separately for positive

versus nonpositive earnings announcements:

ABCDEFGHIJKLMNOPQ

Calendar Date 7-Mar-06 20-Jun-06 12-Sep-06 5-Dec-06 18-Apr-06 26-Jul-06 10-Oct-06 9-Jan-07 27-Apr-06 20-Jul-06 12-Oct-06 27-Feb-07 4-May-06 1-Aug-06 3-Nov-06 22-Feb-07

Ticker KR KR KR KR SVU SVU SVU SVU SWY SWY SWY SWY WFMI WFMI WFMI WFMI

Consensus 0.36 0.42 0.29 0.28 0.56 0.57 0.53 0.56 0.3 0.36 0.39 0.6 0.35 0.34 0.29 0.4

Actual 0.39 0.42 0.29 0.3 0.55 0.53 0.61 0.54 0.32 0.42 0.39 0.61 0.36 0.35 0.29 0.38

Surprise 0.03 0.00 0.00 0.02 -0.01 -0.04 0.08 -0.02 0.02 0.06 0.00 0.01 0.01 0.01 0.00 -0.02

Starting Point 44 117 175 234 73 142 195 256 80 138 197 289 85 146 213 286

Intercept 0.0003 0.0004 0.0006 0.0003 -0.0008 -0.0003 -0.0002 0.0003 0.0001 0.0001 0.0003 0.0012 0.0006 -0.0007 -0.0014 -0.0020

Slope 0.6662 0.6063 0.5628 0.4483 0.7049 0.5416 0.6014 0.5238 1.0139 0.9289 0.7533 0.7505 0.8345 1.2329 1.3199 1.5321

STEXY 0.0123 0.0113 0.0108 0.0118 0.0123 0.0122 0.0128 0.0130 0.0130 0.0118 0.0134 0.0131 0.0175 0.0164 0.0195 0.0243

Day relative

to event

-10

0.59% -0.07% 0.72% -0.46% -0.68% -1.81% -1.04% 0.47% 0.32% -0.01% 0.36% 0.46% -0.52% -3.87% 0.05% 0.26%

-9

-1.19% 0.89% 0.38% -1.20% -1.06% 0.33% -0.43% 0.36% 0.65% -0.96% -0.94% 0.29% -1.94% 0.93% -0.06% 0.94%

-8

1.08% 0.58% -0.32% 0.00% 0.25% -0.35% 0.29% -0.92% -0.65% 0.21% 0.22% 0.60% -1.20% -0.33% -0.98% 1.82%

-7

-0.67% -1.34% 0.85% -0.23% -0.86% 1.30% 0.05% 0.66% 0.90% -0.78% -0.16% -0.96% -1.27% -0.18% 0.53% -0.87%

-6

0.98% -0.35% -0.59% -0.67% -0.55% 1.24% -0.66% -0.48% -2.40% 0.20% -3.61% 0.33% -0.59% -1.79% 0.10% -0.11%

-5

-0.34% 1.41% -0.84% 0.42% 0.29% 0.13% 2.44% -1.05% -0.66% 0.19% -4.99% 0.68% -1.35% -0.97% 0.25% 0.35%

-4

-0.23% 0.11% 0.49% 0.62% -0.71% 0.79% -0.63% 0.71% -0.57% 0.93% 0.88% 0.21% 0.09% -1.88% 0.31% 0.35%

-3

-1.55% -1.16% -0.39% -1.92% -1.01% -0.64% -0.11% -0.46% -0.80% 0.92% 1.21% -3.80% 0.41% 0.85% -1.52% 1.22%

-2

0.93% -0.59% 1.97% 0.98% 0.34% 0.49% -0.05% 0.68% 0.61% 1.79% 2.36% -1.12% -0.28% 1.38% 0.03% -1.01%

-1

0.03% -0.26% 0.29% 2.63% 0.29% -0.60% 2.95% -0.79% 0.82% -1.33% 0.30% 0.60% 1.08% 0.01% -4.74% -0.39%

1.46% 5.01% -6.31% 4.87% -1.39% -7.03% 4.26% -1.70% 2.54% 9.17% -2.18% -1.41% 12.17% -11.88% -25.78% 13.46%

1.43% -0.63% 0.38% -1.35% -1.12% -4.37% 0.58% -1.57% -0.12% 0.83% -2.34% 1.01% 1.42% -0.14% -0.94% -2.45%

-1.15% -0.56% 0.93% -0.15% 1.43% -2.64% 0.99% 1.59% 1.33% -1.44% 0.34% 0.37% 0.37% -0.19% 2.02% 0.06%

1.08% 1.15% -2.90% -0.24% -0.82% 0.87% 0.20% -0.91% -3.28% -0.24% 0.35% -1.44% -1.78% 0.67% 3.11% 0.94%

0.03% 1.37% 0.27% 2.61% 0.36% 0.20% -0.71% -0.56% -1.34% -0.23% 1.26% -0.06% -0.40% 0.24% 0.14% -1.13%

0.11% -0.31% -0.31% 1.19% -0.99% 0.62% -0.47% -0.53% 0.58% 0.76% -1.94% -0.59% 0.04% -4.00% 0.44% -1.13%

-0.12% 2.70% 0.50% -0.42% -0.23% -0.76% 0.83% 0.04% 1.02% -1.13% 0.93% -0.40% -0.76% -1.24% 0.84% 0.41%

-0.20% 0.35% -0.96% 0.75% 0.63% 0.11% 0.27% 1.26% -0.45% -0.09% 0.13% 1.20% 1.25% -0.02% 0.25% 1.97%

-0.18% 0.73% 1.90% 0.33% 0.46% -0.41% -0.02% 1.75% -1.43% 0.26% -2.28% -0.12% -0.46% -1.25% -0.34% -0.63%

-1.34% -0.52% 0.14% -2.86% 0.23% -1.15% -0.23% 0.04% -0.82% 1.04% 0.13% -0.12% -1.59% 7.14% -0.99% -1.50%

0.86% 0.08% -0.14% -1.19% -0.88% -1.27% 0.00% 0.48% 0.46% -0.33% 0.92% -0.12% -0.85% 2.10% -0.12% -0.63%

CUMULATIVE ABNORMAL RETURNS IN THE WINDOW -10 TO +10

ABNORMAL RETURN