An empirical investigation of the effect of quarterly earnings announcement timing on stock returns
Accounting Research Center, Booth School of Business, University of ChicagoAn Empirical Investigation of the Effect of Quarterly Earnings Announcement Timing onStock ReturnsAuthor(s): William Kross and Douglas A. SchroederReviewed work(s):Source: Journal of Accounting Research, Vol. 22, No. 1 (Spring, 1984), pp. 153-176Published by: Blackwell Publishing on behalf of Accounting Research Center, Booth School of Business,University of ChicagoStable URL: http://www.jstor.org/stable/2490706 .Accessed: 27/02/2012 13:15Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jspJSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact email@example.com. Blackwell Publishing and Accounting Research Center, Booth School of Business, University of Chicago are collaborating with JSTOR to digitize, preserve and extend access to Journal of Accounting Research.http://www.jstor.org
Journal of Accounting Research Vol. 22 No. 1 Spring 1984 Printed in U.S.A. An Empirical Investigation of the Effect of Quarterly Earnings Announcement Timing on Stock Returns WILLIAM KROSS AND DOUGLAS A. SCHROEDER*1. Introduction This research examines both the association between quarterly an-nouncement timing (early or late) and the type of news (good or bad)reported,and the relationship between stock returns and timing aroundthe earnings announcement date. Recent research on announcementtiming (Givoly and Palmon , Patell and Wolfson , Kross, and Whittred ) has provided evidence that delayed an-nouncements of annual earnings more often convey bad news (i.e., lowerthan expected earnings) than do early announcements. However, weknow of no study which has reportedevidence of the same phenomenonfor quarterlyearnings.Furthermore, there is a limited amount of evidenceregardingthe reaction of market participants to announcement timing.While three studies (Givoly and Palmon , Kross , andChambersand Penman ) find that early (late) announcementsareassociated with higher (lower) abnormal returns or high (low) stockreturn variability, relative to late (early) announcments, only Kross controlled for the sign of the earnings forecast error and nonecontrolled for the magnitudeof the earnings forecast error. It is well accepted that stock returns are associated with the sign ofthe earnings forecast error (EFE). Since announcement timing is also * Associate Professor and Assistant Professor, Purdue University. We wish to thank themembers of the Purdue University and the University of Chicago accounting seminars fortheir comments. [Accepted for publication June 1983.] 153 Copyright ?, Institute of Professional Accounting 1984
154 JOURNAL OF ACCOUNTING RESEARCH, SPRING 1984associated with the EFE this variable must be controlled if one is toexamine market reaction to announcement timing. Similarly, Beaver,Clarke, and Wright  reported that stock returns are also associatedwith the magnitude of the earnings forecast error, so it is necessary tocontrol for forecast error magnitudes as well. This is because early (late)announcers could be releasing extremely good (bad) news. Finally, in thelight of recent research which shows that stock returns around theannouncement date are inversely related to the size (market value) ofthe firm (Atiase  and Ro ), like Chambers and Penman, we decided to control for the potentially confounding size effect.1 Our objective, then, was to determine whether the association betweenannouncement timing and stock returns persists after controlling for thesign and the magnitude of the earnings forecast error and firm size.Briefly, our results show that early quarterly earnings announcements(1) contain better news and (2) were associated with larger abnormalreturns relative to late announcements. These findings hold both forlarge and small firms, for positive and negative EFEs, and for smallabsolute values of the EFE. In section 2 we describe the procedures and the data used in the tests.The lag and earnings expectations models used to classify firms intoreporting and earnings categories are discussed in section 3. The resultsappear in section 4, followed by a summary and conclusions (section 5).2. Procedure and Data2.1 PROCEDURE First, we computed a time lag forecast error for each of 12 quarters onthe basis of a comparison of the actual quarterly announcement datewith a forecasted date. Second, we computed an earnings forecast errorfor each firm and each quarter on the basis of a comparison of actual toforecasted quarterly earnings. We then examined the earnings forecasterrors for the earliest and latest quarterly announcements for each firmwith the expectation that the earliest announcements (relative to expec-tations) would have a higher (larger positive or smaller negative) medianEFE than observed for the latest announcements. Next, we categorizedeach firm on the basis of both its lag forecast error (early, on time, late)and its earnings forecast error (good news, bad news). This processresulted in six distinct groups of firms: early-good, early-bad, on time-good, on time-bad, late-good, late-bad. Finally we examined the abnormalstock return behavior on the days surrounding the quarterly announce-ments for all six groups of firms in order to determine whether theannouncement timing conveyed or was associated with information otherthan that contained in the earnings number. We want to thank the reviewer for making this suggestion.
EFFECT OF EARNINGS ANNOUNCEMENT TIMING 155 TABLE 1 Median Autocorrelations at Lags One-Four Autocorrelation Raw Residuals Residuals at Lag Time Lag RandomWalk Model Model Autoregressive 1 -.1712 +.1909 -.1015 2 -.0939 +.0517 -.0069 3 -.1887 -.0086 -.0302 4 +.5809 -.2832 -.13102.2 THE SAMPLE Our total sample consisted of 297 NYSE and American Stock Exchangefirms that met the following conditions: (1) daily stock price data wereavailable for the years 1977-80 on the daily ISL (Investment StatisticsLaboratory) tapes produced by Standard and Poors and Chase Econo-metrics; (2) quarterly earnings from the third quarter of 1968 throughthe third quarter of 1980 (the latest available at the time this study wasconducted) were available on the quarterly COMPUSTAT tapes; (3)quarterly earnings announcements dates were available from the secondquarter of 1971 through the third quarter of 1980 on the quarterlyCOMPUSTAT tapes; and (4) the fiscal year ended in December. These filters resulted in 3,564 observations-12 quarters for each of297 firms. All observations were used when we examined the relationshipbetween announcement timing and the earnings forecast error. However,missing stock return data caused us to eliminate one firm, yielding 3,552observations for the examination of stock price behavior.3. Models3.1 ANNOUNCEMENT LAG FORECAST MODELS A firm was classified as reporting early or late based on a comparisonof the actual time of announcement with the expected time of announce-ment. The expected time of announcement was formulated via a time-series analysis of each firms reporting history. We examined the auto-correlation functions for the 26 quarterly report time lags from thesecond quarter of 1971 through the third quarter of 1977 for the 297firms in our sample.2 The median autocorrelations for lags one throughfour are presented in table 1. As one would expect, there is clearly a spike in the autocorrelationfunction at lag four. The autocorrelation function of the fourth differ-ences (a random walk model) still produced small spikes at lags one andfour. Since we expected fourth-quarter (annual) earnings to be reportedat longer lags, on average, than interim reports, we estimated an auto- 2 A lag was measured by the number of days elapsing from the end of the reportingperiod to the earnings announcement.
156 W. KROSS AND D. A. SCHROEDERregressive model with an indicator variable associated with fourth-quarter announcements for predicting report time lags for each firm.Model (1) predicts the report lag as follows: Lagiq = ai + fj(Lagiq-4) + Yi(Q4) + Fi(Lagiq-) (1)where: Lagiq= forecast of the number of days spanning the end of the quarterand the earnings announcement for firm i in quarter q; Lagi-4 = actual number of days spanning the end of the quarter andthe earnings announcement for the same quarter of the preceding year; Lagi,_q = actual number of days spanning the end of the quarter andthe earnings announcement for the quarter immediately precedingquarter q; Q4= 1 if quarter q is the fourth fiscal quarter, 0 otherwise; a, y,y, r = firm-specific parameters. The medians of the autocorrelation function of the residuals for model(1) (column 4 in table 1) indicate that they are nearly white noise. Thismodel was used to forecast the report time lag for each of the next 12quarters with a reestimation of the parameters after each quartersforecast. A second lag forecast model, model (2), was chosen as a benchmarkfor comparison to model (1). This model is a random walk in whichreport lags are predicted as: La~gq= Lagiq-4 (2)where the terms are defined as before. As reported in column 3, table 1,small spikes appear in the autocorrelation function at lags one and fourfor this model. Nevertheless model (2) seemed a reasonable and appro-priate benchmark for purposes of comparison with model (1). Each ofthese models was used to forecast the announcement lag for each of 12consecutive quarters beginning with the fourth quarter of 1977.3.2 EARNINGS FORECAST MODEL The type of earnings news reported was classified vis-A-vis an extrap-olation of each firms quarterly earnings. We utilized the premier quar-terly forecasting model proposed by Griffin  and Watts , theparameters of which were estimated for each firm: Zq = Zq-4 + Zq-i - Zq-5 - Oaq1 - yaq4 + Oyaq-5 (3)where: Zq = one-quarter-ahead forecast of EPS in quarter q, Zq = actual EPS in quarter q, 0= a regular first-order moving average parameter, y= a seasonal moving average parameter, a= a serially uncorrelated error term.
EFFECT OF EARNINGS ANNOUNCEMENT TIMING 157The choice of a "common model" structure follows from Jenkins as well as empirical studies of earnings. Jenkins suggests that for rela-tively short time series a common model may be appropriate where theenvironments generating the data have common features. In addition,empirical studies such as Foster , Lorek , and Schroeder provide evidence that time-series models generate quarterly fore-casts that are superior to naive martingalelike models. For each firm, the 37 quarterly EPS figures (adjusted for stock divi-dends and stock splits) from the third quarter of 1968 through the thirdquarter of 1977 were used to estimate the parameters of this model. Allforecasts of interim and annual (fourth-quarter) EPS were one-quarter-ahead forecasts generated by updating the model for each of the next 12quarters.3.3 STOCK RETURN MODEL In the evaluation of stock return response to earnings announcements,we used the traditional market model posited by Sharpe : Rik = ai + O2iRmk Like + (4)The parameters of this model were estimated using one year of dailyreturns (approximately 252 observations) prior to the quarter of theearnings announcement. The parameter estimates were used to computethe abnormal return for days -2 through +2.4. Results4.1 RESULTS-TIMING VERSUS TYPE OF NEWS In this section we report on the nature of the report timing for bothinterim and annual earnings and the association of report timing withthe EFE. Figures 1 and 2 depict the distributions of the actual reporting lags(the number of days between the end of the fiscal period and the earningsannouncement), while figures 3-6 depict the unexpected (actual-ex-pected) lag over four-(calendar) -day reporting intervals. The distributionof the raw reporting lag is quite similar to that reported by Chambersand Penman . Interim reports, reported in figure 1, cluster between22 to 30 days after the quarter and are characterized by a distributionthat is skewed to the right. However, the distribution of annual an-nouncements, reported in figure 2, seems more symmetric, with someclustering of announcements between 28 and 40 days after the end of theyear. The distributions of the lag forecast errors, reported in figures 3-6,are almost symmetrical for both annual and interim announcements. Asexpected, all four distributions are characterized by a sharp spike at zerowith a similar number of observations above (late) and below (early) themean.
158 W. KROSS AND D. A. SCHROEDERbays -6 10 14 18 22 26 30 34 38 42 46 50 54 58 -62 FIG. 1.-Frequency distribution of reporting time lags: interim announcements. Days .56 104 14_ 18 - 224 264 30 1-38 42 -46 50 54 58 62 FIG. 2.-Frequency distribution of reporting time lags: annual announcements. Tests of the relationship between the unexpected lag and EFEs arereported in table 2 for both interim and annual announcements. The rawforecast error was first deflated by its standard error (se). Thus: EFE = (Zq Zq)/se.When EFE is positive (negative), earnings are greater (less) than ex-pected. Panel A of table 2 shows the average earnings forecast error across allfirms from the earliest to the latest announcement (relative to expecta-tions). The Wilcoxon statistic on the difference between the averages for
EFFECT OF EARNINGS ANNOUNCEMENT TIMING 159 A * (4) ,~, I I * (3) co I -ID I * (1) 0~~~~(0 I * v *H(4) I . L F I I * (04) I ** (10) I (3) I** I ***********(125) o I ********************* (405)) I I ) *********************** (1214) I CI0. o *********** (533)) iI IL, o ***** (235) ..Io I .. ** (616) -I I D * I I v * (4) esI T IIILI 40)0 800 1200) 1600 200 Freque-ncy
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EFFECT OF EARNINGS ANNOUNCEMENT TIMING 161 0, A ** (5) I I I W * (2) Hcn l I, ** (5) :: I ** (8) I _-~-7 1 (87) -. I.. I **** (27) I I ******** (71) I Io j ***~************* 17 cLI I o ************************************ ( 353) I ?I ***************** (155)< 00 I ~Fq o? ******* (59 )0 I cLI e **** (25) I ,_ * (13) Io I : - ** (6) I I 8v * (1) * I I IllII 100 200) 300 400 500 Frequency
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164 W. KROSS AND D. A. SCHROEDERthe earliest and the latest announcing quarters is significant at a = .01for both the autoregressive and the random walk models. The results ofthe Page L test (which tests the null against the ordered alternative a,1> 2> **. *> 12 and thereby uses more of the sample information) is alsosignificant at a = .01. Panel B of table 2 shows the results when the three annual announce-ments are omitted for each firm. Again, the results are significant andconsistent with the results based on all quarters. We conclude, therefore,that earlier (later) quarterly announcements are characterized by higher(lower) unexpected earnings. We next investigate whether this relation-ship had any effect on stock returns.4.2 RESULTS-ANNOUNCEMENT TIMING AND STOCKRETURNS Using daily data, the possibility of cross-sectional correlation in theresiduals existed due to earnings announcement clustering. For example,as many as 39 firms announced their earnings on the same calendar date.In order to mitigate the problem of cross-sectional correlation we gener-ated controlled residuals by subtracting the residuals of a randomlyselected nonannouncing sample firm from that of each announcing firmduring the earnings announcement time period (day -2 through day +2).The controlled residual was computed as follows: Vik = fik - fjkwhere: Vik = controlled residual for treatment firm i, day k; cik = market model residual for treatment firm i, day k; cjk = market model residual for control firm j, day k.The only restriction applied in the selection of the control firm was thatthe quarterly earnings announcement of the treatment firm did not occurwithin seven calendar days of the announcement of its control firm. Thisprocess was repeated for each firm for each quarter tested. The resultsof all succeeding tests are reported using these controlled residuals(hereafter residuals).3 In order to assess the relationship between announcement timing andstock returns, we had to control for the announcements news effects onthose returns. We did so using a two-factor analysis of variance. Onefactor classified firms by the type of news (good or bad) reported, whilethe other represented the timing (early, on time, or late) of the announce-ment. This approach allowed us to examine the timing effect indepen-dently of the type of news reported. For test purposes we classified firmsas reporting on time if the actual announcement date was within ? one 3We also conductedtests on the raw residualsof the treatment firms. Our conclusionswere not altered.
EFFECT OF EARNINGS ANNOUNCEMENT TIMING 165day of the expected announcement date. Announcements arriving earlier(later) were categorized as early (late). The results for all observations are presented in figure 7 and table 3for the autoregressive lag expectation model. The residuals are reportedas percentages. A glance at figure 7 reveals immediately that announce-ment timing as well as the type of news had a distinct association withthe stock return residuals. Since the interaction between the two factors was never significant wedo not report it. Consistent with expectations, good news firms, with afive-day cumulative average residual (CAR) of .83%, outperformed badnews firms whose five-day CAR was -.97%. A stratification on timingalone yielded a CAR on early firms of .43%, while late or on timeannouncements had CARs of -.16% and -.27% respectively. Since earlierannouncements typically contain good news the better performance forearly firms is not surprising. However, when the sample is stratified intonews/timing categories the effect of announcement timing still persists. 1.200 - .900 -~~- // .900 / A/ -.300} e a:.QOO ~~~~~~~~~~~~~~~~- -1.200 -1.600-, -2.000 -1.000 DRYS 1.() 2.000 FIG. 7.-Cumulative averageresiduals (in percentages)aroundthe announcementdate(day 0): good/early (GE), good/late (GL), good/on time (GOT), bad/early (BE), bad/late(BL), bad/on time (BOT).
166 W. KROSS AND D. A. SCHROEDER * LO cqc t r cv cv r, cs cv Cq M Lo t O ce > 4 00 f- = H m cq r cv u o T C C 5 CD CeD Ce t5 CD CD t5 Ce Ce 00 I I I I I I+ I 10 .) + O 00 CD _ O Ce C- C- CD OC c + I ++ I + I+ + +Lc U I1+ 2 > O ~~~~~~ ~~~ O O O n ~~ ~~~~~~ ~~~~~~~~ O O L > _ >) cq cq cq cll cq _q 00 t- E -< + .:II III +++t-O- Clz o, t~-C~ m ClcO LC LO c C m cT c l CZ Z! C. * * Ce +l- ClC +C I: + II+ C.) -U I Iz-Cl O O- > % O CS h- 0q 0U-~ U-~ 00 L-00 C 0 Cl~0 L O L+O + 1 c + 1 c c C? r-- r-- x~~~~~~~~~~~~~~~~~~~ L LO LO t- (s s.: ~O C.)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~C >) ,,* ,E5 E- -H z E- Z m C
EFFECT OF EARNINGS ANNOUNCEMENT TIMING 167The CAR for good news announced early was 1.39%, but only .54% forgood news announced late. Early bad news produced a five-day CAR of-.79%, compared to a -1.02% CAR when announced late. Thus, evenafter controlling for the sign of the earnings forecast error, the timingeffect was still significant at day -2 through day 0 and for the five-dayCAR. The results of separate tests on annual and interim announcementsare reported in panels A and B of table 4. Both timing and news effectswere significant for the five-day CAR for both annual and interimannouncements, at day -2 for annual announcements and at day -2through day 0 for interim announcements. These results lend strongsupport to the notion that the timing effect is independent of the sign ofthe EFE. Although the ANOVA results indicate that the timing effect wasdistinct from the sign of the EFE, it is still plausible that it was a functionof the magnitude of the EFE. That is, "good news" firms might havereported very good news when they announced early, but only moderatelygood news when they announced late. Similarly, late "bad news" mighthave been very bad compared to early bad news announcements. This isa reasonable alternative hypothesis, particularly in the light of the studyby Beaver, Clarke, and Wright  which reported a positive relation-ship between stock returns and the magnitude of EFE. In order to address this issue, we subclassified each main group into amoderate and an extreme subgroup-for example, moderately bad (neg-ative EFE less than one standard error) and very bad (negative EFEgreater than one standard error in magnitude), with a similar dichotomyfor "good news" firms. If the report timing was a surrogate for magnitudeof EFE, we would not expect a relationship between announcementtiming and stock returns for moderate news classifications. As before, anANOVA was used, but only on the firms that reported moderately goodor bad news in early and late announcements. The test results in table 5 are not consistent with the proposition thatthe timing effect was a proxy for the magnitude of the EFE. The timingeffect persisted at day -1 and day 0 and for the five-day CAR eventhough, by construction, the magnitude of the EFE was small. Separateexaminations on annual and interim announcements, reported in panelsA and B of table 6, yielded similar results. Again the timing effect wassignificant for the five-day CAR and for one or more of the days aroundthe announcement date. The news effect was very weak, as would beexpected since all extreme EFEs were omitted for these tests. Theseresults provide strong evidence that report timing was not a surrogatefor the magnitude of the earnings forecast error, but rather conveyed orwas correlated with information distinct from the sign and the magnitudeof EFE.
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4.3 RESULTS-ANNOUNCEMENT TIMING AND FIRM SIZE Previous studies (Chambers and Penman  and Ro ) foundan inverse relationship between firm size and the absolute value of stockreturns around the earnings announcement date. A question naturallyarises about whether the timing effect phenomenon would be observablefor both large and small firms. In order to provide evidence on this issuewe took our "moderate news" subsample and split it into small and largefirms and conducted tests on each size substratum. All firms whosemarket value was below (above) the medium market value at the end ofthe second quarter of 1978 were defined as small (large) for the entiresample period. The test results on large (small) firms are reported inpanel A (panel B) of table 7. As shown there, the timing effect persisted across both size categories.For large firms (panel A), announcement timing was significantly relatedto stock returns at day -2, day -1, (weakly) at day 0, and for the five-day CAR. Late announcers of moderate news saw their share prices dropby .89%, on average, as opposed to an increase of .30% for their early-announcing counterparts. Announcement timing also affected moderategood news firms, which had residual returns of .72% if they announcedearly, but only .24% if they announced late. The test results on the small firms, reported in panel B of table 7, tella similar story. The announcement timing effect was significant at day-1, day 0, and (weakly) for the five-day CAR. The share prices of firmsthat announced moderately bad news late fell by .90%, on average, overthe five-day announcement period, whereas the early-announcing coun-terparts fell by only .03%. Moderately good news announced early wasrewarded by a .31% abnormal return, whereas later-announced good newswas greeted with a .32% negative return. Thus, it appears that the "timingeffect" persisted for both large and small firms.5. Summary and Conclusions Generally, we found that earnings announcement timing was associ-ated with abnormal stock returns around the earnings announcementdate. Abnormal returns of firms that announced early (late) were sig-nificantly higher (lower) than the returns of firms that announced late(early). This general result is consistent with previous research by Givolyand Palmon , Kross , and Chambers and Penman ;however, these other studies did not completely control for potentiallyconfounding factors regarding the "timing effect." After controlling forthese, our results are remarkably consistent. The "timing effect" persistedwhether the earnings announcement (1) contained good news or badnews, (2) was an annual or interim announcement, (3) was made by alarge or small firm, or (4) contained moderately good or moderately badnews.
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should affect stock returns. Indeed, we believe that the announcementtiming itself should have no effect, but rather is probably associated withsome other event that either is typically associated with a reporting delayor is usually viewed as "bad" news. Loss contingency disclosures or CPAswitches could be two such types of events. Of course, additional researchis needed to explore this possibility. Because "timing" (or other events associated with it) can greatlyinfluence stock returns, we suggest that future studies on announcementsincorporate adjustments which control for announcement timing whenexamining stock return responses to the announcements. Failure to dothis could bias, or induce noise into, the test results. REFERENCESATIASE, R. K. "Predisclosure Informational Asymmetries, Firm Capitalization, Financial Reports, and Security Price Behavior." Ph.D. dissertation, University of California, Berkeley, 1980.BEAVER, W., R. CLARKE, AND W. WRIGHT. "The Association Between Unsystematic Security Returns and the Magnitude of Earnings Forecast Errors." Journal of Accounting Research (Autumn 1979): 316-40.CHAMBERS, A. E., AND S. H. PENMAN. "Timeliness of Reporting and the Stock Price Reaction to Earnings Announcements." Journal of Accounting Research (Spring 1984): 21-47.FOSTER, G. "Quarterly Accounting Data: Time-Series Properties and Predictive Ability Returns." The Accounting Review (January 1977): 1-21.GIVOLY, D., AND D. PALMON. "Timeliness of Annual Earnings Announcements: Some Empirical Evidence." The Accounting Review (July 1982): 486-508.GRIFFIN, P. A. "The Time-Series Behavior of Quarterly Earnings: Preliminary Evidence." Journal of Accounting Research (Spring 1977): 71-83.JENKINS, G. M. Practical Experiences with Modeling and Forecasting Time Series. San Francisco: Holden-Day, 1979.KROSS, W. "Earnings and Announcement Time Lags." Journal of Business Research (September 1981): 267-81. . "Profitability, Earnings Announcement Time Lags, and Stock Prices." Journal of Business Finance and Accounting (Autumn 1982): 313-28.LOREK, K. S. "Predicting Annual Net Earnings with Quarterly Earnings Time-Series Models." Journal of Accounting Research (Spring 1979): 190-204.PATELL, J., AND M. WOLFSON. "Good News, Bad News, and the Timing of Corporate Disclosures." The Accounting Review (July 1982): 509-27.Ro, B. "Firm Size and the Informational Asymmetry of Annual Earnings Announcements." Working paper, Purdue University, February 1983.SCHROEDER, D. A. "Expectations Data and the Stochastic Properties of Accounting Numbers: A Multiple Time Series Methodology." Ph.D. dissertation, University of Kansas, 1980.SHARPE, W. F. "A Simplified Model for Portfolio Analysis." Management Science (January 1963): 277-93.WATTS, R. L. "The Time Series Behavior of Quarterly Earnings." Working paper, Depart- ment of Commerce, University of Newcastle, 1975.WHITTRED, G. P. "Audit Qualification and the Timeliness of Corporate Annual Reports." The Accounting Review (October 1980): 563-69.