The Impact of Toys Recall Announcements on Market Returns

7/30/2019 The Impact of Toys Recall Announcements on Market Returns

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Journal of Interdisciplinary Business Studies

The Impact of Toys, Page 1

The Impact of Toys Recall Announcements on Market

Returns

Ekanayake, M. EkanayakeBethune-Cookman University

Sengupta, Sunando

Bowie State University

ABSTRACT

The purpose of this study is to examine empirically the impact of product recall announcementson security prices. The study measures the stock price reaction to recent product recall

announcements of some selected firms by examining the abnormal returns associated with theseannouncements. The sample for this study is based on toy recall announcements and respective

stock price reactions between January 1982 and December 2007. Event study methodologycommon to financial research is used to calculate the cumulative abnormal returns over a 10-

day event window that included the 10 days prior to, the day of, and the 10 days following theannouncement. The results of the study show that the market reactions to toy recall

announcements are associated with significant shareholder losses.

Key words: Event study, abnormal returns, market reaction, event window, announcement


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INTRODUCTION

The purpose of this study is to examine empirically the impact of toy recall announcements onsecurity prices of a sample of 84 firms over the years 1982-2007. There has been a significant

increase in toy recalls in recent years (see Figure 1). Similar to prior studies on product recalls,

this paper examines the stock price reaction to recent product recall announcements of someselected firms by examining the abnormal returns associated with these announcements, using anevent-study framework. This paper considers toy recalls between 1982 and 2007 for major toy

manufacturing companies in the United States as reported in the U.S. Consumer Product SafetyCommission ( CPSC ) database.

The recall of a defected product from the market represents the failure of a manufacturer in

delivering the promised level of quality. As Hendricks and Singhal (2003) point out, productsrecalls may affect firms performance and the net cash flows. Product recalls have been

extensively examined in the literature with researchers finding that such actions are typically

damaging for shareholder value. For example, Jarrell and Peltzman (1985), Hoffer et al. (1987)

and Barber and Darrough (1996) all find significant shareholder losses surrounding automotiverecall announcements, while Pruitt and Peterson (1986) and Davidson and Worrell (1992) have

similar findings for non-automotive recall announcements. However, Hoffer et al. (1988) and

Jarrell and Peltzman (1985) find insignificant shareholder returns surrounding automotive recall

announcements.

The findings of this study suggest that the toy recall announcements are found to have a

significant negative effect on stock prices around the event days. The results indicate that themean cumulative abnormal returns are mostly negative and significant on the days the toy recall

announcements are made. These findings are consistent with the findings of previous studies onthe relationship between the stock prices and product recall announcements.

Review of Literature

Although rich literature links security return predictability to variables thought to capture

changing business conditions, relatively few papers to date have attempted to measure the equitymarkets reaction to product recall announcements. In this section, the findings of some of the

studies on product recall announcements are summarized.

In their work using recalls of automobiles and drugs (prescription, over-the-counter, and medicaldevices), Jerrell and Peltzman (1985) concluded that, over the sample period 1974-1982, for

manufacturers of automobiles and ethical drugs capital market penalized shareholders for more

than the direct costs of the recall campaign. They also concluded that in both industries,shareholders of competitor firms to the firm with the recalled product(s) also suffered wealthlosses. Earlier, Crafton, Hoffer, and Reilly (1981) and Reilly and Hoffer (1983) found that severe

automobile recalls have a significant short-term impact on demand, but neither studyinvestigated the effect of recalls on industry shareholders. Pruitt and Peterson (1986) used a

sample of 293 non-automotive product recall and product sales or production-haltannouncements and found that security prices continue to react to product recall announcements

for approximately two months following the initial news release. Hoffer, Pruitt, and Reilly


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(1988), using the automotive industry recall data during the period 1975-1981, found that neithershareholders of the firm recalling automobile nor shareholders of competitor firms are

significantly affected by product recalls. In a study conducted to examine the stock marketreactions to both manufacturer and government-initiated automotive safety recalls using a 26-

year sample period from 1973 to 1998 involving 734 safety recalls, Rupp (2001) found that the

market reaction to government-initiated recalls is not associated with larger shareholder losses.In another study, Rupp (2004), using 19731998 automotive safety recall data, identifies thekinds of recalls that cause significant shareholder losses. After constructing an equally-weighted

automotive market index to control for industry effects and adjusting the abnormal returns toaccount for the degree of surprise in the recall announcement, the study estimates both

percentage and real dollar abnormal returns. The study finds that the indirect costs of automotiverecalls are likely larger than the direct costs.

In addition to these studies, there are several studies on the effects of food recalls on company

stock prices using the event-study methodology. For example, Salin and Hooker (2001) reportmixed evidence of security price movements in the wake of recalls while Thomsen and

McKenzie (2001) find significant and negative stock price reaction to recalls involving serioushealth hazards.

METHODOLOGY

This study employs a standard event study methodology and a standard market model to measure

normal performance:

2varand0where

titititmtiiit )()E(RR ==++= (1)

The regression coefficientsi

andi

are estimated in an ordinary least squares (OLS ) regression

during the estimation period one year (255 trading days) prior to the event period (event days -300 through -46). The event period consists of 61 trading days centered on the product recall

announcementevent (-30 through +30). Four event windows were defined based on the event

date, [-30,-2], [-1, 0], [+1, +2] and [+3, +30]. As proxy for the return for the market portfoliomt

R

, both the CRSP value weighted index and the CRSP equal weighted index is used.

Under standard assumptions, OLS is a consistent estimation procedure for the market model

parameters. Under the assumption that asset returns are jointly multivariate normal and

independently and identically distributed ( iid),OLS is also efficient. The prediction errors,i

PE,

which represent abnormal returns, are simply the OLS residuals, it

.

( )i i i i i mt

PE R R = + (2)

with46

2 2

299

1 ( )255 2

t

t i i i m

t

R R

=

=

(3)


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The prediction error,it

PE , is used as an estimator of the abnormal return. In other words, the

abnormal return is the residual term of the market model calculated on an out of sample basis.Let , 30, 29,... 29, 30

iAR t t t t

= + + be the sample of 61 abnormal returns for firm i in the

event window. Under the null hypothesis, conditional on the event window market returns, theabnormal returns will be jointly normally distributed with a zero conditional mean and

conditional variance:

))(,0(~ 2

ii

ARNAR (4)

The conditional variance 2 ( )i

AR

has two components. The first component is the disturbance2

t

from (3), and the second component is additional variance due to sampling error in

estimating the market model parametersi

andi

:

=

=

++=

46

299

2

222

255

1where]

)(1[

255

1)(

t

t

mm

m

mmttit RR

RRAR

(5)

Since the estimation window is large (255 trading days), it is assumed that the contribution of the

second component to 2 ( )i

AR

is zero.

To draw inferences about the average price impact of an event, abnormal return observationshave to be aggregated across securities and through time. Average abnormal returns AAR

are

formed by aggregating abnormal returnsi

ARfor each event period 30, 29,... 29, 30t t t t = + + .

Given N events (for our sample, 147N = ),

1

1N

i

iAAR ARN

=

=

(6)

Under the assumption that average abnormal returns are independent across securities, theasymptotic variance equals to

2

21

1( )

N

i

Var AARN

=

= (7)

The average abnormal returns are aggregated through time to give the cumulative average

abnormal return,2

1

1 2( , )

i iCAAR AAR

=

= (8)

Setting the covariance terms to be zero,

1 2

1

var( ( , )) var( )N

i i

i

CAAR AAR

=

= (9)

Hence

))),(var(,0(~),( 2121 ii CAARNCAAR (10)


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This can be used to test the null hypothesis that the abnormal returns are zero.

Because 2

is unknown, it has to be estimated. Since on a single day there are two FFTR

ringing events involving two firms, it is quite likely that abnormal returns are cross-sectionally

correlated across securities. Hence, using2

in (7) to construct test statistics could cause apotential problem. Brown and Werner (1985) suggest a crude dependence adjustment whichuses the variance of portfolio residuals from the estimation period rather than the sum of

variances of residuals for individual securities. Therefore the estimated variance of AAR

is

46 462

2 299 299

( )

where255 2 255

t t

t t

AAR

AAR AAR AAR

AAR

= =

= =

(11)

The portfolio test statistic for day in event time is

2AAR

AARt

= (12)

Assuming time series independence, the test statistic for1 2

( , )i

CAAR is

1 2

2 1

( , )

( 1)

i

AAR

CAARt

=

+

(13)

If clustering is present, this portfolio approach will impound any residual cross-sectionalcorrelation in its estimate of portfolio residuals standard deviation. Nevertheless, besides being

cross-sectionally correlated, the abnormal return estimators often have different variances acrossfirms. A common way of addressing this problem is the standardized residual method (Patell,

1976). Define the standardized abnormal return,i

SARas

i

i

i

MLE

ARSAR

= . (14)

Where:

22

462

299

( )1

1( )

i

m m

MLE t

m m

t

R R

TR R

=

= + +

(15)

is the maximum likelihood estimate of the variance. Under the null hypothesis eachi

SARfollows

a Students t distribution with T-2 degrees of freedom. Summing thei

SARacross the sample

yields


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=

=

N

i

ititit QNASARSARASAR1

),0(~where

(16)

The Z-test statistic for the null hypothesis that1 2

( , ) 0i

CAAR = is

2

1

1 2 1 2 1 2

1

2 1

1 1( , ) ( , ) where ( , )

2( 1)

4

N

i i i

i

Z Z Z SAR

N T

T

= =

= =

+

(17)

The two test statistics so far discussed use the variance estimate from the market model duringthe estimation period to estimate the variance of the abnormal return estimator. But frequently,

events increase the variance of returns, so that the event period variance is greater than theestimation period variance. Two common proposals for coping with event-induced variance are

the cross-sectional standard deviation method proposed by Brown and Warner (1985) and thestandardized cross-sectional test developed by Boehmer, Musumeci and Poulson (1991). The

cross-sectional standard deviation method substitutes a daily cross-sectional standard deviationfor the portfolio time-series standard deviation. The portfolio test statistic for day t in event time

is

2

1 1

1 1where ( )

1 /

N N

AAR i i

i iAAR

AARt AR AR

N NN

= =

= =

(18)

We use the above equation to calculate theAdjusted-t.

Thestandardized cross-sectional methodis a hybrid of the standardized-residual and the cross-sectional approach:

2

1 1

1 1where ( )

1 /

N N

t AAR i i

i iSAR

ASARZ SAR SAR

N NN

= =

= =

(19)

We use the above equation to calculate theAdjusted-Z.

DATA

The data sets to be analyzed are the daily stock prices of 84 companies. The data cover a period

of twenty one years from January 1982 to December 2007. During the 26-year period from 1982to 2007, the U.S. Consumer Product Safety Commission ( CPSC ) published 675 toy recalls. To

isolate the equity responses to the recall announcements, 580 companies were eliminated since

they were not publicly traded companies. Of the 95 companies selected, another 11 companieswere eliminated due to unusable data giving us a sample of 84 companies.

Daily stock price and number of shares outstanding data come from the Center for Research in

Security Prices (CRSP ) at the University of Chicago. The study used the CRSP daily returns

data from EVENTUS software in Wharton Research Database Service.


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The event dates correspond to the press release days for the toy recalls. These voluntary dates

follow significant company analysis and review. In either case the stock market may be betterinformed than consumers prior to the press release resulting in reductions in share prices prior to

the event days. Since the information contained in the CPSC database may have released before

the close of trading on the previous day, the two-day event window includes the prior tradingday. The cumulative abnormal return is the two-day sum of daily average abnormal returns fromequation (8).

Let t index the time in trading days relative to the recall announcement at event day zero. We

estimate the market model parameters (i

andi

) for the 255 days of trading prior to the

announcement from 300-t = to 46-t = . The event period consists of 61 trading days centered on

the product recall announcementevent (-30 through +30).

ANALYSIS AND FINDINGS

A sample of eighty-four companies was used and fitted with a standard market model andcalculated the abnormal returns using the following four event windows: [-30, 2], [-1, 0], [+1,+2] and [+3, +30]. The event date is defined as the date zero.

Using CRSP data and the market model, the mean abnormal returns for three event windows

was estimated utilizing the software. The estimated results for the four event

windows and their associated test statistics are presented in Table 1. The mean abnormal returnswere estimated in two ways: (a) using the market model with equally weighted index; and (b)

using the market adjusted returns with equally weighted index. The results indicate that there is astatistically insignificant -0.08 percent mean cumulative abnormal return for the window [-1, 0]

but a statistically significant -0.61 percent mean cumulative abnormal return for the window [+1,

+2], using equal weighted index as proxy for market portfolio return. There is also a statisticallysignificant -3.84 percent mean cumulative abnormal return for the window [+3, +30], usingequal weighted index as proxy for market portfolio return. However, the return increases to 0.05

percent, -0.52 percent, and -3.71 percent for the three event windows, respectively, when marketadjusted returns with equally weighted index was used. The results suggest that markets on

average tend to react negatively when toy recall announcements are made public.

Table 2 reports the estimated mean abnormal returns observed for the 84 companies and theirassociated test statistics, for 10 days before and 10 days after the toy recall announcement date.

The mean cumulative abnormal returns around the event day and the three days following theevent day are negative and statistically significant, when using equal weighted index as proxy for

market portfolio return. The mean cumulative abnormal returns from days five through eight arealso negative but they are not statistically significant. The results are somewhat similar when

market adjusted returns with equally weighted index was used.

In conclusion, the toy product recall announcements are found to have a significantly negativeeffect on stock prices on and around the event days. This finding is consistent with the findings

of previous studies on the relationship between the stock prices and the product recallannouncements.

EVENTUS


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SUMMARY AND CONCLUSIONS

The purpose of this study is to examine empirically the impact of toy recall announcements on

security prices of a sample of 84 firms over the years 1982-2007. Similar to prior studies on

product recalls, this paper examines the stock price reaction to recent product recallannouncements of some selected firms by examining the abnormal returns associated with theseannouncements. Using an event-study framework, this study examines how the stock prices

respond to toy recall announcements. The mean cumulative abnormal returns were calculated forthree event windows using the market model and CRSP data in combination with the event-

study methodology utilizing the software.

The results indicate that there is a statistically insignificant -0.08 percent mean cumulative

abnormal return for the window [-1, 0] but a statistically significant -0.61 percent meancumulative abnormal return for the window [+1, +2], using equal weighted index as proxy for

market portfolio return. There is also a statistically significant -3.84 percent mean cumulative

abnormal return for the window [+3, +30], using equal weighted index as proxy for marketportfolio return. However, the return increases to 0.05 percent, -0.52 percent, and -3.71 percentfor the three event windows, respectively, when market adjusted returns with equally weighted

index was used. The results suggest that markets on average tend to react negatively when toyrecall announcements are made public.

When all the companies are taken together, our results indicate that the mean cumulative

abnormal returns around the event day and the three days following the event day are negativeand statistically significant, when using equal weighted index as proxy for market portfolio

return. The mean cumulative abnormal returns from days five through eight are also negative butthey are not statistically significant.

In conclusion, the toy product recall announcements are found to have a significantly negative

effect on stock prices on and around the event days. This finding is consistent with the findingsof previous studies on the relationship between the stock prices and the product recall

announcements.

EVENTUS


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REFERENCES

Barber, B. M. and M. N. Darrough (1996), Product Reliability and Firm Value: The Experienceof American and Japanese Automakers, 1973-1992, Journal of Political Economy, 104(5),

1084-1099.

Boehmer, E., J. Musumeci, and A. Poulsen (1991), Event-Study Methodology under Conditionsof Event-Induced Variance,Journal of Financial Economics, 30(2), 253-272.

Brown, S. and J. Warner (1985), Using Daily Stock Returns: The Case of Event Studies,

Journal of Financial Economics, 14(1), 3-31.

Campbell, J. Y., A. W. Lo, and A. C. MacKinlay (1997), Event Study Analysis, in TheEconometrics of Financial Markets (Princeton University Press: Princeton, NJ), 149-180.

Crafton, S. M., G. E. Hoffer, and R. J. Reilly (1981), "Testing the Impact of Recalls on the

Demand for Automobiles,"Economic Inquiry, 19(3), 694-703.

Davidson, W. N. and D. L. Worrell (1992), Research Notes and Communications: The Effects

of Product Recall Announcements on Shareholder Wealth, Strategic Management Journal,

3(3), 467473.

Fama, E. F., L. Fisher, M. C. Jensen, and R. Roll (1969), The Adjustment of Stock Prices toNew Information,International Economic Review, 10(1), 1-21.

Hendricks, K. B. and V. R. Singhal (2003), The Effects of Supply Chain Glitches on

Shareholder Wealth, Journal of Operations Management, 51(5), 695-711.

Hoffer, G. E., S. W. Pruitt, and R. J. Reilly (1987), Automotive Recalls and InformationalEfficiency, The Financial Review, 22(4), 433-442.

Hoffer, G. E., S. W. Pruitt, and R. J. Reilly (1988), The Impact of Product Recalls on the

Wealth of Sellers: A Reexamination, Journal of Political Economy, 96(2), 113-122.

Hoffer, G. E., S. W. Pruitt, and R. J. Reilly (1994), When Recalls Matter: Factors Affecting

Owner Response to Automotive Recalls, The Journal of Consumer Affairs, 28(1), 96106.Jarrell, G. and S. Peltzman (1985), The Impact of Product Recalls on the Wealth of Sellers,

Journal of Political Economy, 93(3), 512-536.

MacKinlay, A. C. (1997), Event Studies in Economics and Finance, Journal of EconomicLiterature, 35(1), 1339.

Patell, J. (1976), Corporate Forecasts of Earnings Per Share and Stock Price Behavior:

Empirical Tests,Journal of Accounting Research, 246-276.


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Pruitt, S. W. and D. Peterson (1986), Security Price Reactions Around Product RecallAnnouncements, Journal of Financial Research, 9(2), 113-122.

Salin, V. and N. H. Hooker (2001), Stock Market Reactions to Food Recalls, Review of

Agricultural Economics, 23(2), 33-46.

Reilly, R. J. and G. E. Hoffer (1983), Will Retarding the Information Flow on Automobile

Recalls Affect Consumer Demand?,Economic Inquiry, 21(3), 444447.

Rupp, N. G. (2001), Are Government Initiated Recalls More Damaging for Shareholders?

Evidence from Automotive Recalls,Economic Letters, 71(2), 265-270.

Rupp, N. G. (2004), The Attributes of a Costly Recall: Evidence from the AutomotiveIndustry,Review of Industrial Organization, 25(1), 21-44.

Rupp, N. G. and C. R. Taylor (2002), Who Initiates Recalls and Who Cares? Evidence from the

Automobile Industry,Journal of Industrial Economics, 50(2), 123149.

Thomsen, M. R. and A. M. McKenzie (2001), Market Incentives for Safe Foods: AnExamination of Shareholder Losses from Meat and Poultry Recalls, American Journal of

Agricultural Economics, 82(3), 526-538.


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Note:Figures for2008 arefromJ anuarythroughAugust2008.

Source: The U.S. Consumer Product Safety Commission (CPSC), Product Recall database.

Figure 1. Number of Toy Recalls, 1982-2008

0

10

20

30

40

50

60

70

80

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008


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Table 1: Mean Abnormal Returns of Event Period [-30, +30]

(a) Market Model, Equally Weighted Index

Days N

Mean

Cumulative

AbnormalReturns

Precision

Weighted

CAAR

Positive:

Negative

Patell

Z

Portfolio

Time-Series

(CDA) t

Generalized

Sign Z

(-30, -2) 84 -1.77% -1.22% 32:52< -1.35 -1.26 -1.86*

(-1, 0) 84 -0.08% -0.10% 46:38 -0.41 -0.21 1.19(+1, +2) 84 -0.61% -0.66% 30:54< -2.77** -1.66* -2.30*(+3,+30) 84 -3.84% -3.02% 29:55


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Table 2: Mean Abnormal Returns Around the Event Day [-10, +10]

Market Model, Equally Weighted Index Market Adjusted Returns, Equally Weighted Index

Day N

Mean

AbnormalReturn

Positive:Negative

Patell Z

Portfolio

Time-Series

(CDA)t

GeneralizedSign Z

Mean

AbnormalReturn

Positive:Negative

Patell Z

Portfolio

Time-Series

(CDA)t

GeneralizedSign Z

-10 84 0.07% 37:47 -0.26 0.28 -0.77 0.10% 38:46 -0.09 0.39 -0.44-9 84 -0.05% 42:42 0.29 -0.19 0.32 -0.08% 43:41 0.37 -0.28 0.65

-8 84 -0.08% 45:39 -0.29 -0.31 0.98 -0.18% 39:45 -0.91 -0.66 -0.23

-7 84 -0.08% 37:47 -0.02 -0.31 -0.77 -0.10% 45:39 -0.18 -0.37 1.09

-6 84 0.29% 45:39 1.97* 1.12 0.98 0.34% 48:36>> 2.16* 1.28 1.74*

-5 84 0.28% 44:40 0.43 1.05 0.76 0.29% 38:46 0.50 1.08 -0.44

-4 84 -0.40% 38:46 -1.54 -1.52 -0.55 -0.48% 35:49 -1.96* -1.79* -1.10

-3 84 0.05% 39:45 -0.25 0.19 -0.34 0.02% 39:45 -0.24 0.06 -0.22

-2 84 0.02% 44:40 1.61 0.07 0.76 0.02% 42:42 1.54 0.06 0.43

-1 84 0.19% 43:41 1.60 0.74 0.54 0.21% 40:44 1.50 0.77 -0.01

0 84 -0.27% 40:44 -2.17* -1.03 -0.12 -0.16% 42:42 -1.76* -0.58 0.43

1 84 -0.21% 32:52

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The Impact of Toys Recall Announcements on Market Returns

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