the effect of labor strikes on security analysts' forecast superiority and on the association...

The effect of labor strikeson security analysts' forecast superiority

and onthe association between risk-adjusted

stock returns and unexpected earnings*

LAWRENCE D. BROWN University at Buffalo - SUNYMARK E. ZMUEWSKI University of Chicago

Abstract. This paper empirically examines whether labor strikes affect the forecasting andinfonnation content of quarterly earnings numbers. We address two issues regardingfinancial analyst forecast (FAF) superiority: whether FAF superiority increases when astrike occurs and if so, whether the increase in FAF superiority is sustained immediatelyafter the strike ends. We also examine two issues regarding infonnation content: whetherstrikes affect the coefficient mapping unexpected earnings into stock prices and whetherstrikes affect the variance of stock price changes. We suggest that strikes affect both theforecasting and information content of quarterly earnings numbers.

Resume. Cet article examine de fagon empidque si les greves des travailleurs influencent lapr6vision et le contenu informatif des resultats trimestriels. Nous abordons deux questionsrelatives k la superiorite des provisions d'analystes financiers (PAF): a savoir si lasup^dodtd des PAF s'accroit lorsqu'une greve se produit et, dans l'affirmative, siI'accroissement de cette superiorite se maintient immOdiatement apres la fin de la gr^ve.Nous ^tudions egalement deux questions relatives au contenu informatif: a savoir si lesgraves influencent le coefficient incorporant l'impact des b6nefices imprevus sur le coursdes actions et si les greves affectent la vadance des fluctuations du cours des actions. Lesgreves affecteraient done a la fois la prevision et le contenu informatif des resultatstdmestdels.

IntroductionLabor strikes (hereafter strikes) are significant economic events in that firmsexperiencing them have had, on average, a significant decrease in the market valueof their shareholders' equity during the period that they occur (Neumann (1980)and Becker and Olson (1986)).' Given that strikes are significant economic events,

* The authors appreciate the assistance of Benjamin Lee and Pravin Shah, and the comments ofHaim Falk, Bob Holthausen, Kathenne Schipper, an anonymous reviewer, and the participants ofthe Purdue accounting workshop.

1 Recognizing that a peaceful settlement is an alternative to a strike, Becker and Olson (1986)compared labor strikes to a sample of peaceful settlements. Finding the market response to apeaceful settlement (stnke) to be positive (negative), the authors concluded that the bulk oftheeconomic costs are associated with the strike experience per se, rather than with the terms of anew contract. However, as the authors did not present data on the terms ofthe settlement, their re-sults may be attributable to differential terms of stnke and peaceful settlement contracts.

Contemporary Accounting Research Vol. 4 No. 1 pp 61-75

62 L.D. Brown M.E. Zmijewski

it is conceivable that they affect the forecasting and/or the information content ofaccounting earnings numbers. Indeed, Collins and Hopwood (1980) and Beaver(1981, p. 105), respectively, have asserted that strikes are "atypical" events (i.e.,potential interventions in the earnings process) that can affect the forecasting andinformation content of accounting earnings numbers. However, neither Collinsand Hopwood (1980) nor Beaver (1981) empirically examined these hypotheses.The purpose of this paper is to empirically examine whether strikes affect theforecasting and information content of quarterly earnings numbers.

More specifically, we address two issues regarding financial analyst forecast(FAF) superiority and two issues regarding information content. With respect toFAF superiority,^ we examine: (1) whether FAF superiority increases when astrike occurs; and (2) whether the increase in FAF superiority is sustainedimmediately after the strike ends. With respect to information content, weexamine: (1) whether the coefficient mapping unexpected earnings numbers intorisk-adjusted stock returns in strike quarters differs from the coefficient innonstrike quarters; and (2) whether the risk-adjusted stock return variance aroundthe date that earnings are announced in strike quarters differs from the variance innonstrike quarters.

The paper proceeds as follows. The specific hypotheses are developed andformally stated in the next section. The third section discusses the data and definesthe variables used in the tests. The forecast accuracy results are presented in thefourth section, and the last section contains the results of the information contenttests. The paper concludes with a summary.

Hypotheses

Forecasting hypothesesThe extant literature contains numerous studies that have examined and comparedalternative models for forecasting quarterly earnings.^ This literature has shownthat: (1) three time series (TS) models appear to be viable candidates for generatingquarterly earnings numbers, and (2) on average, analysts' earnings forecasts aremore accurate than TS model forecasts.

Fried and Givoly (1982), Brown et al. (1987a), and Hopwood and McKeown(1986) argue that FAF superiority is due, in part, to analysts' access to a broaderand more timely information set. Brown, Richardson and Schwager (1987)maintain that FAF superiority is due, in part, to the amount of information analystsacquire, the precision of the information and the covariance among the informa-tion signals. As knowledge of strikes is information that is available to analysts(but not to TS models), and as knowledge of strikes is likely to have a lowcovariance with other information available to analysts, it is plausible that FAF

2 FAF superiority is measured relative to three alternative univariate time-series models, discussedbelow.

3 For a review of this evidence see Foster (1986, chapters 7 and 8) and Watts and Zimmerman(1986, chapter 6).

Effect of Labor Strikes on Analysts' Forecast 63

superiority is enhanced when a strike occurs, and possibly shortly after the strikeends. This reasoning gives rise to our first two testable hypotheses.

The first two null hypotheses are:

oi jjs — RFS PS,

//02' RFS PS ^ RFSps,

where RFS indicates relative FAF superiority over a time-series model and PS,DS and FS designate preceding strike, during strike and following strike, respec-tively. Rejection of each of these hypotheses respectively indicates that RFSduring and after a strike is greater than prior to a strike.

Information content hypothesesBeaver (1981) and Ryan (1986) maintain that accounting earnings numbers havetwo components, a transitory (temporary) component and a permanent compon-ent. Fora given firm, adollar of unexpected transitory earnings impacts firm valueless than does a dollar of unexpected permanent earnings."*

Strikes have both a permanent and a transitory effect on earnings. In so much asstrikes result in permanently lost sales and increased labor costs due to settle-ments, they affect firms' future economic earnings. However, during the period inwhich they occur, strikes may also have a transitory effect on earnings - firms mayexperience a decrease in sales and incur additional strike-related expenses only inthose fiscal quarters when the strike occurs.

If (1) unexpected information contained in earnings announcements is a mixtureof transitory and permanent components; (2) unexpected earnings reported duringstrikes have a larger temporary component than do earnings reported during other(nonstdke) periods; and (3) adollar of unexpected transitory earnings impacts firmvalue less than does a dollar of unexpected permanent earnings, the coefficientmapping (CM) unexpected earnings into risk-adjusted stock returns during a strikeperiod (DS) will be less than the coefficient during nonstrike periods (NS).^ Thus,the third hypothesis is:

H03: CMDS ^ CMNS.

Rejection of this hypothesis suggests that the coefficient mapping unexpectedearnings into risk-adjusted stock returns during a strike period is less than duringnonstrike periods.

The process by which markets partition unexpected earnings into transitory andpermanent components is unknown. However, if the transitory component ofearnings represents noise in the interpretation of the earnings information signaluseful for valuing firms, then unexpected earnings numbers with large (small)

4 Beaver (1981, p. 105) uses strikes as an example of an atypical event resulting in an increase mthe transitory component of income.

5 It is assumed that the market's expectation of the reported earnings number does not fully in-corporate the strike's effect. The contention that strikes are not fully anticipated is consistent withNeumann (1980) and Becker and Olson (1986).


transitory components will provide less (more) precise information signals.Holthausen and Verrecchia (1986) argue that the variance of the stock marketreaction to an information signal is an increasing function of the infonnationsignal's precision (or a decreasing function ofthe signal's noise). Consistent withHolthausen and Verrecchia, we hypothesize that the variance (V) of the stockmarket's reaction to an earnings announcement during a strike period(s) is lessthan its variance to an earnings announcement during nonstrike periods. Moreformally we have:

Rejection of this hypothesis suggests that the variance of the stock market'sreaction to an earnings announcement during a strike period(s) is less than thevariance of the stock market's reaction to an earnings announcement duringnonstrike periods.

Data and variable definitions

Data:The firms studied satisfy three criteria:1 1960 through 1980 quarterly earnings per share available in Moody's Handbook

of Common Stocks;2 No change in fiscal year between 1960 and 1980; and3 Covered by The Value Line Investment Survey (hereafter Value Line) from 1975

through 1980.The first criterion is imposed because a source of data is required to generate TS

model forecasts of quarterly earnings numbers. As the univariate TS modelsgenerating quarterly earnings contain a seasonal component (see, for example,Foster (1977) and Griffin (1977)), criterion two must be imposed to preventchanges in the seasonality factor. The Value Line criterion is imposed because werequired a source of analysts' quarterly earnings forecasts. The Value LineInvestment Survey was the only publicly available source of these data during thistime period. A total of 233 (212) firms satisfy the three criteria during the1975-1979 (1980) time period. For each of the 233 firms, strike information wascollected for all strikes that occurred between 1975 and 1980 for these firms.^-^

In order to conduct empirical tests of hypotheses //oi and H02, three additionalsample selection criteria are imposed for an observation to be included in the finalstrike sample:1 Only one strike occurs for the firm in the quarter;2 The firm does not experience another strike in either the immediately succeed-

ing or preceding fiscal quarter; and

6 Brown et al. (1987a) is the source ofthe earnings data and Becker and Olson (1986) is the sourceof the stnke data used in this study.

7 The reason for the loss of 21 firms in 1980 is that the 1980 data are an addition to data collectedthrough 1979; some firms experienced bankruptcies, mergers, etc


3 Required forecast data are available in Value Line.The first two criteria are necessary in order to ensure that, for each strikeobservation, data are available for the quarter before and the quarter after the strikequarter(s). (Data are treated as unavailable if either of the first two criteria areviolated.) The third criterion requires that forecasts for the quarter prior to thestrike, the strike quarter(s), and the quarter after the strike are included in the ValueLine Investment Survey.

In order to conduct empirical tests of hypotheses H03 and H04, the followingadditional sample selection criteria are imposed:1 A firm must have at least one strike occurring between 1975 and 1980;2 Required forecast data are available in Value Line,3 The earnings announcement date is available in the Wall Street Journal; and4 Sufficient stock return data are available on the CRSP Daily Return File.

The first criterion is obvious. Criteria two through four are imposed becausemeasures of security analysts' unexpected earnings and stock market reaction toearnings announcements are required to conduct the tests. These sample selectioncriteria result in a sample of 64 firms for the information content tests. The 64 firmshave a total of 1,435 quarterly earnings announcements.* A total of 155 (10.8percent) strikes occur during the 1,435 firm fiscal quarters.

Forecasts by analysts and modelsThe empirical tests in this study are based on a one-quarter ahead forecast ofquarterly earnings per share. Forecasts made by one security analyst firm areexamined relative to forecasts generated by three Box-Jenkins (1976) univariateTS models. The source of analysts' forecasts is The Value Line Investment Survey.The specification, in Box-Jenkins notation (P, D, Q) (p, d, q), of the three TSmodels is:1 (1,0,0)(0,1,1), initiated by Brown and Rozeff (1979), hereafter Brown;2 (l ,0,0)(0,1,0), plus a constant, introduced by Foster (1977), hereafter Fos-

ter; and3 (0, l , l ) (0,1,1) , suggested by Watts (1975) and Griffin (1977), hereafter

Griffin.All available quarterly earnings data are used to generate the TS model

forecasts. More specifically, the first forecast (i.e., the first quarter of 1975) isbased on the 60 quarterly earnings numbers, first quarter of 1960 through fourthquarter of 1974; the last forecast (i.e., the fourth quarter of 1980) is based on 83quarters (first quarter of 1960 through third quarter of 1980).

Forecast accuracy and information content metricsAll hypotheses tests described below compare accounting earnings forecast errorsreported during strike quarter(s) with accounting earnings forecast errors reportedduring the quarter immediately before or immediately after strike quarter(s). For

8 Sixty-four firms x 6 years x 4 quarters = 1536 less 101 missmg observations


the purpose of conducting the statistical tests, each firm is used as its own control,and it is assumed that earnings forecast errors are not serially or cross-sectionallycorrelated.'

A strike quarter is defined as any fiscal quarter in which the firm experiences astrike. The forecast error metric is defined as actual quarterly earnings less itspredicted value, where both are deflated by the absolute value of the actualearnings number.'° This metric is used to test the hypothesis H03, the mapping ofunexpected earnings into stock price.

Hypotheses //oi and H02 evaluate relative FAF superiority. Relative FAFsuperiority over a TS model for observation / in quarter t, /?F5,,,7-s, is defined asthe unsigned percentage forecast error of the TS model minus the unsignedpercentage forecast error of the analyst.

The information content tests (//03 and H04) require market model predictionerrors for firm i on day f. The model is estimated over days -260 to — 11, whereday 0 is the Wall Street Journal earnings announcement date (a minimum of 100days is required to estimate the parameters). Tests examining the mapping ofunexpected earnings into stock price (H03) employ a cumulative prediction error,CPE(-1,0),,, which is the sum of the market model prediction errors on the daybefore and the day of The Wall Street Journal earnings announcement.''

Tests examining the variance of the stock market reaction to an earnings announce-ment (H04) employ the squared standardized cumulative residual, SCPE(-1,0),,,developed by Patell (1976); the cumulation period is the day before and the day ofthe Wall Street Journal earnings announcement.

Tests of comparative FAF superiorityTest results relating to the FAF superiority hypotheses, //oi and H02, are presentedin Tables 1 and 2, respectively. All tests are nonparametric (Wilcoxon matched-pairs signed-ranks tests). The test results are segmented as follows: all strikesoccurring within one quarter (Panel A); strikes occurring within one quarter withduration of at least five days (Panel B); and strikes occurring within one quarterwith duration of at least ten days (Panel C). It is expected that the effect of a strikeis positively related to its length. Thus, the Panel C results are stronger tests of ourhypotheses than the Panel B results, and the Panel B and C results are stronger testsof our hypotheses than the Panel A results.

The sign of the test statistic in Table 1 is consistent with the altemativehypothesis in seven of nine cases, but only one case of significance is obtained (see

9 This assumption appears plausible because the extant literature indicates that earnings forecasterrors are serially independent. Thus, the dispersion of our final sample across time periods (seethird section) should result in earnings forecast errors that are cross-sectionally independent.

10 Brown etal. (1987) examined altemative deflators in similar tests and found that the choice ofdeflator did not affect the predictive ability results.

11 We use the two-day holding period because prior literature has shown that capital markets in-corporate the information content of earnings announcements dunng this time frame (Morse(1981)).


TABLE 1Wilcoxon matched-pairs signed-ranks test of Hypothesis 1

tio\'. RFS us ~ RFS PS

ModelNo. of Z-cases Statistic

Panel A. Strikes occurring within one quarter

Brown-VLFoster-VLGnffin-VL

565656

-1 .220.850.26

1-Tailedprob.

0.1120.1520.397

Panel B. Strikes occurring within one quarter with duration a

Brown-VLFoster-VLGriffin-VL

464646

Panel C. Strikes occurring


353535

-0 .750.520.39

0.2270.2980.349

; within one quarter with duration ^

0 261.440.47

0.3960.0750.317

Mean RFSduring strike

4.847.588.59

5 days

3.065.837.38

10 days

2.976.676.36

Mean RFSprior to strike

4.763.383.04

3.323.582.70

2.011.290.15

* Test statistic = Relative Forecast Superiority (RFS) during strike — Relative Forecast Superionty(RFS) pnor to strike.

Expected sign of the Z-statistic is positive if the null hypothesis is rejected.

TABLE 2Wilcoxon matched-pairs signed-ranks test of Hypothesis 2 (A/02)*

" 0

ModelNo. ofcases

Z-Statistic

1-Tailedprob.

Panel A. All strikes occurring within one quarter


565656

1.961.651.23

0.0250.0490.109

Panel B. Strikes occurring within one quarter with duration s


464646

2.281.581.29

0.0150.0580.098

Panel C. Strikes occumng within one quarter with duration a


353535

2.422.281.79

0.0080.0110.037

Mean RFSfollowing strike

13.7712.029.85

: 5 days

15.2213.9211.21

: 10 days

16.4014.8911.65

Mean RFSprior to strike

4.763.383.04

3.323.582.70

2.011.290.15

* Test statistic = Relative Forecast Superiority (RFS) following strike - Relative Forecast Superior-ity (RFS) prior to strike.

Expected sign of the Z-statistic is positive if the null hypothesis is rejected.


Panel C). Moreover, the mean relative forecast superiority (RFS) during a strikeexceeds RFS prior to a strike in eight cases. We conclude that, at most, FAFsuperiority is enhanced moderately at the time of a strike.

Strikes may affect relative FAF superiority in the quarter after the strike as wellas during the quarter(s) that the strike takes place. The TS models included in thisstudy revise their forecasts of future quarterly earnings numbers, in large part,based upon their error in forecasting the most recent quarterly earnings number. '^If strikes impart a transitory nature to reported earnings during a strike, TS models(but not analysts who are aware of the nature of the disturbance) will overstate thepersistence of the error, resulting in relative FAF superiority being greaterimmediately after than prior to a strike. If so, hypothesis //02 will be rejected infavor of its alternative.

Table 2 provides strong evidence in favor of the alternative hypothesis. Morespecifically, //02 is rejected in all nine cases in Panels A-C at the 11 percentsignificance level or better, and the mean RFS following a strike is always greaterthan the mean RFS prior to a strike. We conclude that FAF superiority immedi-ately following a strike is greater than it is immediately preceding a strike.

Tests of information content

Magnitude testsTests relating market model prediction errors to unexpected earnings numbersemploy a multiple regression methodology. More specifically, excess returnsduring the two-day holding period, CPE(—1,0),,, are regressed on unexpectedearnings announced on day 0, FE,rm, and a strike interaction variable. StrikeDummy,ftn times FE,,m, to determine if the slope coefficient for unexpectedearnings (i.e., the mapping) is affected by the occurrence of a strike. Morespecifically:

CPE(-l,O),v = a-h bx FE,,^ + c x (Strike Dummy,-, x FE,,^) (1)

where:

Strike Dummy,, = 1 if a strike occurs in quarter t for firm /, 0 otherwise;

and FE is the unexpected earnings.The b coefficient for FE,,^ represents the mapping of unexpected earnings into

market model prediction errors for quarters in which strikes do not occur. B ased onthe extant literature (e.g.. Ball and Brown (1968) and Beaver, Clarke and Wright(1979)), we expect the b coefficient to be positive. Tbe coefficient for the strikeinteraction term, c, represents the mapping of unexpected earnings into risk-adjusted stock returns for strike quarters relative to the mapping for nonstrike

12 See Brown and Rozeff (1979a) andEaston and Zmijewski (1986) for evidence of this phenomenon.


quarters. If, as expected, strikes dampen the mapping of FE into CPE, c should benegative.'^

The regression results are reported in Table 3. ''* Panels A through C respectivelyreport results for all firms with at least one strike, with strikes of at least five days'duration, and with strikes of at least ten days' duration. The results suggest thatstrikes do impact the mapping of unexpected earnings numbers into abnormalshare price movements. More specifically, the coefficient for the mapping ofunexpected earnings into stock prices in nonstrike quarters, b, is significantlygreater than zero (significance equals one percent or better) and the strike inter-action coefficient, c, is significantly less than zero (significance equals one percentor better).

Variance testsTests examining the variance of the stock price change associated with earningsannouncements regress the variance of stock price reaction, SCPE(—1,0),,, onproxy variables for (1) the "amount" of information contained in earningsannouncements and (2) the precision of that information. We argued in the secondsection that strikes cause additional noise in the earnings signal (i.e., reduce itsprecision), thus decreasing the variance of the stock price reaction to an earningsannouncement. Our proxy for the "amount" of information contained in theearnings announcement is the unsigned earnings forecast error, |FE,,m|. Moreformally, we employ the following regression formulation:

SCPE(-1,O),, = a + b X |FE,m| + c X Strike Dummy,,. (2)

The coefficient for |FE,,m|, b, is expected to be positive since more informationis expected to result in a larger stock price variance. The coefficient for the StrikeDummy,, (which is equal to 1 if there is a strike during the quarter and 0 otherwise)is expected to be negative since unexpected earnings numbers in strike quarters areexpected to be noisy signals, resulting in less stock price variance than identicalunexpected earnings numbers reported during nonstrike quarters.

Finally, in an attempt to control for other firm-specific factors that affect thevariance of stock price changes resulting from earnings announcements but thatshould be stationary overtime, we examine the above regression in first differenceform:

CSCPE(-1,O),, = a + fo X C |FE,,^| + cx C (Strike Dummy),, (3)

13 A limitation of the regression formulation is its implicit assumption that unexpected earnings,FE,,™, can be measured without error. Brown et at. (1987) have demonstrated that this assump-tion may be violated for the proxy variables we employ m these tests. However, we are notaware of another proxy variable and/or an alternative test design that can avoid the potentialbiases resulting from measurement error in the unexpected earnings proxy. As the coefficient forunexpected earnings is positive for the model we utilize, the expected sign of the coefficient forthe strike interaction term, c, will be negative (positive) if the strike interaction variable is posi-tively (negatively) correlated with the measurement error in the proxy for unexpected earnings.

14 A caveat is in order: the regression model is estimated using pooled time-series and cross-sectional data. This may bias the tests in favor of rejecting the null hypotheses

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If the model is well specified, the signs and magnitudes ofthe coefficients ofthefirst differences regression will be identical to those ofthe levels regression. Thus,the first differences regression serves as a check on the specification of the levelsregression. The change in IFE,,^! proxies for the change in the amount ofinformation contained in a finn's sequential earnings announcements. Increases(decreases) in |FE,,m| between two quarters results in positive (negative) values ofC[FE,,m|. Thus, the coefficient for C|FE,,^|, b, is expected to have a positivesign. The change in the strike dummy variable has a value of 0 if strikes either do ordo not occur in two consecutive quarters (no change in precision); a value of — 1 ifa strike occurs in the preceding quarter but not in the current quarter (an increase inprecision or a decrease in noise); and a value of -f 1 if a strike occurs in the currentquarter but not in the preceding quarter (a decrease in precision or an increase innoise). Thus, the coefficient of the change in the strike dummy variable, c, isexpected to have a negative sign.

The results ofthe levels and first differences regressions are reported in Tables 4and 5, respectively. With the exception of the intercept, the results of the levelsand first differences regressions are qualitatively equivalent and consistentlysupport our expectations that the variance of stock price changes resulting fromearnings announcements is an increasing function ofthe amount of information inthe earnings number, and is a decreasing function of the precision of theinformational signal. More specifically, the b coefficient is always positive andsignificant at the 0.02 level or better in both tables; the c coefficient is alwaysnegative and significant at the 0.08 level or better in both tables.

SummaryThis paper assesses the effect of strikes on the forecasting and information contentof accounting income numbers. Two forecasting hypotheses are formulated: onecomparing FAF superiority during a strike with that preceding a strike (//oi)". theother comparing FAF superiority after a strike with that prior to a strike (//02) • Twoinformation content hypotheses are examined, one regarding the mapping ofunexpected earnings into stock prices (//03); the other involving the variance ofstock price changes (HQ^.

We find that, at most, FAF superiority is enhanced moderately at the time of astrike; that FAF superiority is enhanced significantly immediately after a strike;that strikes dampen the mapping of unexpected earnings into market modelprediction errors; and that strikes reduce the variance ofthe stock price reaction toan earnings announcement. Thus, we conclude that strikes affect both the fore-casting and information content of quarterly earnings numbers.

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As is true with all empirical studies in accounting, our study is subject tonumerous caveats. Some potential limitations of our study include (1) the size andrepresentativeness of our sample, (2) the viability of our testing procedures, (3) theimpact of the possible time lag between the publication of the forecast and thestrike, and (4) the unknown degree of measurement error to which our proxyvariables are subject.

Assuming that our models and tests are not severely misspecified, our findingssuggest that strikes impact upon both the forecasting and information content ofaccounting earnings numbers. More specifically, FAF superiority appears to beenhanced by strikes, and earnings reported during strikes appear to be moretransitory and less precise than earnings reported during nonstrike periods.

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the effect of labor strikes on security analysts' forecast superiority and on the association...

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