small negative surprises: frequency and consequence

International Journal of Forecasting 19 (2003) 149–159www.elsevier.com/ locate/ ijforecast

S mall negative surprises: frequency and consequence

*Lawrence D. BrownGeorgia State University, J. Mack Robinson College of Business, School of Accountancy, Atlanta, GA 30302-4050,USA

Abstract

Using a large sample of quarterly observations for the 16 years, 1984–99, I present four types of related temporal evidence: (1) a decreasein the tendency of managers to report quarterly earnings that fall slightly short of analyst estimates [small negative surprises of no more thanthree cents]; (2) the temporal decrease in the tendency of managers to report small negative surprises pertains more to growth than to valuefirms; (3) the adverse valuation consequence of reporting small negative surprises has increased in severity in recent years; and (4) thetemporal increase in the adverse valuation consequence of reporting small negative surprises pertains more to growth than to value firms.My frequency results are robust to alternative definitions of small negative surprises, and my valuation results are robust to including mediansurprises as a potential correlated omitted variable and are not due to temporal changes in the frequency of losses. 2002 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved.

Keywords: Temporal trend; Earnings; Analysts; Small negative surprises; Frequency; Valuation consequences; Growth versus value

1 . Introduction Section 3, discuss major findings in Section 4,provide additional analyses in Section 5, and summa-

It has been alleged by regulators (Levitt, 1998; rize in Section 6.Johnson, 1999) and the financial press (Vickers,1999; McGough, 2000) that the valuation conse-quence of reporting quarterly earnings falling slightly 2 . Hypothesis developmentshort of analyst estimates [small negative surprises]has increased in severity in recent years, especially Burgstahler and Eames (1999) and Degeorge,for growth firms, causing managers to reduce their Patel, and Zeckhauser (1999) document ‘too few’propensity to report small negative surprises. Recent small negative surprises to be attributable to chance.academic research suggests that these concerns may Both studies pool temporal and cross-sectional data,be valid, but the evidence to date is fragmented and and neither examines if this phenomenon hasindirect. I examine the validity of four closely related changed over time. Both Brown (2001) and Mat-hypotheses pertinent to these issues. I develop hy- sumoto (2001) document a temporal increase in thepotheses in Section 2, describe data and tests in frequency of reported earnings that just meet or beat

analyst estimates, but they do not examine temporaltrends in the frequency of small negative surprises.*Tel.: 11-404-651-0545; fax:11-404-651-1033.

E-mail address: [email protected](L.D. Brown). Barth, Elliott, and Finn (1999) find a positive

0169-2070/02/$ – see front matter 2002 International Institute of Forecasters. Published by Elsevier Science B.V. All rights reserved.PI I : S0169-2070( 02 )00061-4

mailto:[email protected]

150 L.D. Brown / International Journal of Forecasting 19 (2003) 149–159

relation between firm value and earnings increases. frequency of small negative surprises. The last twoKasznik and McNichols (2001) show a positive hypotheses pertain to the valuation consequence ofrelation between firm value and the propensity of small negative surprises. All four hypotheses arefirms to meet or beat analysts’ estimates. Bartov, stated in alternative form. My first two hypothesesGivoly, and Hayn (2000), Lopez and Rees (2001) follow:and Payne and Robb (2000) document that the H1: The frequency of small negative surprises hasnegative valuation implications of negative surprises decreased over time.exceed the positive valuation implications of positive H2: The temporal decrease in the frequency ofsurprises. None of these studies examine growth and small negative surprises is more evident for growthvalue firms separately. than for value firms.

Growth and value firms differ in their propensity One plausible explanation for why managers haveto meet or beat analyst estimates (Brown, 2001), and mitigated the frequency of small negative surprisesthe negative reaction of the capital markets differs (H1) is that the negative valuation impact of report-when they fail to do so (Skinner and Sloan, 1999). ing estimate shortfalls has increased in recent years.Brown (2001) shows that, relative to value firms, I examine this issue via my third hypothesis:growth firms have increased their propensity to H3: The negative valuation consequence of re-report small positive surprises in recent years, but he porting small negative surprises has increased inprovides no evidence regarding temporal changes in recent years.valuation consequences of doing so. Skinner and A plausible explanation for why, relative to valueSloan (1999) show that the negative valuation impli- firms, growth firm managers have reduced theircation of negative surprises is more severe for frequency of small negative surprises (H2) is that thegrowth firms, but they do not examine if either the negative valuation impact of small negative surprisesfrequency or adverse valuation consequence of re- has become especially severe for growth firms inporting negative surprises has increased over time. recent years.

No single study has examined if the frequency of This gives rise to my fourth and final hypothesis:falling slightly short of analysts’ earnings expecta- H4: The negative valuation consequence of justtions has decreased over time, especially for growth missing analyst estimates has increased in severityfirms, and if the valuation consequence of so doing more for growth than for value firms in recent years.has increased in severity in recent years, especiallyfor growth firms. There are two advantages ofexamining all of these phenomena in one study: (1) 3 . Data and methodologyuse of the same data set to examine several phenom-ena enhances validity, and (2) it is easier to ascertain I access data from three sources: Thompsonif the phenomena are related, rather than distinct. FinancialI /B/E/S, Standard and Poor’s Compustat,Based on data for the 16 years, 1984–99, I examine and University of Chicago Center for Research in

1these phenomena using four related hypotheses. Security Prices (CRSP).I /B/E/S data are used toThe first two hypotheses pertain to the temporal obtain proxies for analyst quarterly earnings esti-

mates, reported earnings and earnings announcementdates. Compustat data are utilized to classify firmsinto growth and value categories. CRSP data are

1 employed to measure the three-day market-adjustedTo provide a time frame of reference, the studies mentionedabove used data for the years shown in parentheses: Barth et al.cumulative abnormal returns around earnings(1982–92), Bartov et al. (1983–97), Brown (1984–99), Burg- announcement dates. The sample time-period is thestahler and Eames (1986–96), Degeorge et al. (1974–96), Francis16 years, 1984–99.and Schipper (1952–94), Kasznik and McNichols (1988–93),

I measure earnings expectations using the forecastLopez and Rees (1983–98), Matsumoto (1993–97), Payne andby the last analyst predicting quarterly earnings priorRobb (1986–97), Skinner and Sloan (1984–96), and Soffer et al.

(1993–95). to its announcement. This prediction often occurs

L.D. Brown / International Journal of Forecasting 19 (2003) 149–159 151

after the firm’s fiscal quarter end. Actual earnings are be negative and significant. I run Eq. (1) for growthbased on theI /B/E/S definition, which typically is and value firms separately, and I conduct a Chow

2‘operating earnings’ (I /B/E/S,1999). I group firms test to compare the slope coefficient for the twointo growth and value categories using the method of groups. If H2 is valid,b for growth firms should beFrancis and Schipper (1999), who classify firms as significantly more negative thanb for value firms.high tech (my measure of growth) and non-high tech To examine H3 and H4, I run the following(my measure of value) based on three-digit SIC ordinary least squares regression for all firm–yearcodes. observations, replacingFREQ in Eq. (1) with the

After presenting evidence on industry classifica- medianCAR for the year in question. More spe-tions, I document, by year, the frequency of small cifically, I examine temporal changes in the negativenegative surprises, defined as reported quarterly valuation consequences of falling slightly short ofearnings falling no more than three cents below the analyst estimates:analyst estimate, for all firms, growth and value

3 CAR 5 a 1 d*YEAR (2)firms. I then document, by year, for all firms, growth t t

firms and value firms, the median market-adjustedwhere CAR5three-day market-adjusted return (21,return for the three trading days surrounding the0, 11); day 0 is the quarterly earnings announce-announcement for firms whose quarterly earnings fallment day; andd is the temporal change in theshort of analyst estimates by no more than three

4 valuation consequence of falling slightly short ofcents.expectations. If H3 is valid,d should be negative andI begin by running the following ordinary leastsignificant. I run Eq. (2) for growth and value firmssquares regression for all firm-year observations:separately, and I conduct a Chow test to compare theslope coefficient for the two groups. If H4 is valid,dFREQ 5 a 1b*YEAR (1)t tfor growth firms should be significantly more nega-tive thand for value firms.where FREQ is the frequency of falling short of

expectations by no more than three cents in a given5year, andYEAR equals 1 to 16 for 1984 to 1999.

b indicates the temporal change in the frequency 4 . Primary resultsof small negative surprise. If H1 is valid,b should

4 .1. Industrial breakdown

2According to I /B/E/S (1999, page 5): ‘With very few excep- Table 1 shows the industrial breakdown of thetions analysts make their earnings forecasts on a continuing sample. There are 28 414 firm–quarter observationsoperations basis. This means that I /B/E/S receives an analyst’s used to test H1 and H3. Of these, 5168 firm–quarterforecast after discontinued operations, extraordinary charges, and

observations are for growth and 18 402 are for valueother non-operating items have been backed out. While this is far 6firms. The sum of these two groups, 23 568 dataand away the best method for valuing a company, it often causesdiscrepancy when a company reports earnings. I /B/E/S adjustsreported earnings to match analysts’ forecasts on both an annualand quarterly basis.’3 6I examine the robustness of my results to an alternative definition The Francis–Schipper procedure is meant to distinguish high-techof small negative surprise in Section 5.1. from non high-tech firms. To examine the validity of my approach4Consistent with Brown (2001), I define small surprise as within for using their classification system to distinguish growth fromthree cents of actual earnings, but I obtain similar results by value firms, I determine the average P/E (price to earnings) ratiosdefining near misses as reported earnings below one or two cents of the two groups of firms. If my proxy is valid, the average P/Eof analyst estimates. ratio for growth firms should be significantly larger than that for5In Section 5.2, I examine the sensitivity of my results to adding a value firms. Consistent with the validity of my proxy, I reject thepotentially correlated variable to the right-hand side of Eqs. (1) null hypothesis that the average P/E of value firms is at least asand (2), namely the median small negative earnings surprise for great as that of growth firms at the 0.01 level using boththe year. parametric and non-parametric tests.


Table 1Samples: growth, value and total (includes other)

3-Digit SIC Descriptions No. obs.

Growth firms283 Drugs 1034357 Computer and office equipment 773360 Electrical machinery and equipment, excluding computers 33361 Electrical transmissions and distribution equipment 24362 Electrical industrial apparatus 112363 Household appliances 46364 Electrical lighting and wiring equipment 109365 Household audio, video equipment, audio receiving 37366 Communication equipment 522367 Electronic components, semiconductors 780481 Telephone communications 288737 Computer programming, software, data processing 1339873 Research, development, testing services 71

Total growth firm observations 5168

Value firms100s Agricultural, construction, and mining 1132200s Manufacturing, excluding 283 3309300s Manufacturing, excluding 357, 360–367 4297400s Shipping and transportation, excluding 481 1767500s Retail 2731600s Financial services 3446700s Services, excluding 737 1029800s Services, excluding 873 687999 Miscellaneous 4

Total value firm observations 18 402

Others (SIC unknown) 4844

Total observations 28 414

The number of observations indicates ‘near misses’ of not more than three cents in absolute magnitude for firms for the 16 years,1984–99. Three digit SIC code is obtained from Stanford and Poor’s Compustat. Classification into growth and value firms is based on thehigh-tech, non-high-tech designation of Francis and Schipper (1999).

7points, is used to test H2 and H4. Two industries in 4 .2. Frequency evidence: all observationsthe growth-firm category, drugs (SIC code 283) andcomputer programming, software and data process- Panel A of Table 2 provides evidence pertinent toing (SIC 737) have sample sizes in excess of 1000. H1. Consistent with H1, there is a significant de-Because there are nearly four times as many value- crease in the temporal frequency of firms reportingfirm as growth-firm observations, there are seven quarterly earnings falling slightly short of analyst

8industries in the value category with sample sizes estimates. For most of the early years, 1984–92, theexceeding 1000; the largest is manufacturing, with percent frequency of falling slightly short of analyst4297 cases (SIC 300s, excluding 357, 360–367).

7 8The three-digit Standard Industrial Classification (SIC) code does The numbers in Table 2 and following differ from those in Tablenot exist for 4844 observations so these data points are used to test 1 in the sense that the latter pertains to all firms, while the formerH1 and H3, but they cannot be used to test H2 and H4. pertains to firms reporting small negative surprises.


Table 2Percent of earnings that miss analyst estimates by at most three cents

Year Panel A: All firms Panel B: Growth firms Panel C: Value firms

Total obs. Percentage Total obs. Percentage Total obs. Percentage

1984 3297 21.84% 380 32.89% 1846 21.18%1985 4533 23.58% 564 29.79% 2543 24.50%1986 5171 20.77% 626 28.43% 2964 21.39%1987 5583 20.74% 745 28.32% 3220 20.68%1988 6636 18.31% 948 21.31% 3838 18.47%1989 8493 20.15% 1163 25.37% 5046 20.37%1990 8963 21.41% 1243 26.31% 5474 22.20%1991 9497 20.50% 1361 25.72% 5943 20.73%1992 10 708 20.53% 1659 24.47% 6742 20.29%1993 11 039 19.39% 1814 21.33% 6969 19.54%1994 13 804 17.21% 2275 15.60% 8929 18.04%1995 14 315 16.42% 2526 14.77% 9396 17.17%1996 15 918 15.39% 3263 15.51% 10 383 15.71%1997 16 663 14.21% 3735 13.90% 10 967 14.74%1998 16 616 13.80% 3776 12.00% 11 414 14.59%1999 12 139 11.76% 2970 10.54% 8525 12.18%

Trend per year 20.64% 21.40% 20.60%t-value 27.85*** 211.19*** 26.99***

F-value (Chow test) 21.55***

Year is the sample year. Total obs. of growth (value) firms is the total number of observations of growth (value) firms in a given year.Percentage of growth (value) firms show the percent of reported quarterly earnings for growth (value) firms that fall at most three cents shortof analyst estimates. For example, 32.89% indicates that 125 of the 380 actual quarterly earnings of growth firms miss analyst estimates byat most three cents in 1984. Total obs. for all firms exceeds total obs. of growth plus value firms because some firms cannot be classified aseither growth or value firms. Trend per year is the slope of the ordinary least squares regression ofFREQ on Year. Chow test is a test ofdifference in the slope of the regressions for growth and value firms.***Significant at the 1% level or better (one tailed test).

4 .3. Frequency evidence: growth versus valueestimates generally exceeds 20% per year; late in thefirmssample period (1997–99), it drops below 15% per

9year. The temporal trend is20.64%, indicating thatPanels B and C of Table 2, which respectivelythe tendency of managers to report earnings falling

represent growth and value firms, provide evidenceslightly short of analyst estimates has dropped nearlypertinent to H2. The temporal trends in the reductiontwo-thirds of one percent per year (t-value527.85,in frequency of small negative surprises for growthP,1% level, one-tailed test). The evidence supportsand value firms are21.40% and20.60%, respec-H1.tively, suggesting that, relative to value firms, mana-gers of growth firms have reduced their tendency to

9 report small negative surprises by four-fifths of oneThe pattern looks fairly stable to 1992, with a steady declinethereafter. A similar pattern is evident in Table 3. One possible percent per year. In other words, the tendency ofexplanation is that I /B/E/S may have reduced measurement error growth firm managers to reduce their tendency toin its data in the early 1990s, pressured to do so by its first serious

report small negative surprises is more than doublecompetitor, First Call Corporation. As a result, the precision of mythat of value firm managers. A Chow test reveals thatmetrics for defining small earnings surprises may have beenthe difference in the temporal trend for the twoenhanced.


groups is significant (t-value521.55, P,1% level, year (t-value525.11%, P,1% level, one-tailedone-tailed test). The evidence clearly supports H2. test).

This evidence strongly supports H3, and helps4 .4. Valuation evidence: all observations explain the results supporting H1. Managers have

mitigated their propensity to report quarterly earn-Panel A of Table 3 provides evidence pertinent to ings falling slightly short of analyst estimates be-

H3. Eq. (2) is estimated for the valuation conse- cause the valuation consequence of doing so hasquence (CAR) of falling slightly [no more than three increased in severity. Consistent with the tremendouscents] short of analyst estimates. Consistent with H3, temporal growth in managerial compensation inthe regression shows a significant temporal increase stocks and options (The Economist, 1999), and thein the adverse valuation consequence of reporting increase in lawsuits triggered by stock price dropssmall negative surprises. Early in the sample period (Skinner, 1994; Kasznik & Lev, 1995), managers(1984–93), the negative valuation consequence have become more reluctant in recent years to reportnever exceeds 80 basis points per year; late in the small negative surprises.sample period (1994–99), it exceeds 80 basis pointsin every year. The temporal trend is20.04%, 4 .5. Valuation evidence: growth versus value firmsshowing that the adverse valuation consequence ofreporting quarterly earnings falling slightly short of Panels B and C of Table 3, which respectivelyanalyst estimates increased by four basis points a represent growth and value firms, provide evidence

Table 3Valuation consequences of missing analyst estimates by at most three cents


Total obs. MedianCAR Total obs. MedianCAR Total obs. MedianCAR

1984 3297 20.22% 380 20.64% 1846 20.27%1985 4533 20.59% 564 20.69% 2543 20.56%1986 5171 20.39% 626 0.62% 2964 20.60%1987 5583 20.66% 745 21.31% 3220 20.71%1988 6636 20.36% 948 20.36% 3838 20.36%1989 8493 20.29% 1163 20.76% 5046 20.11%1990 8963 20.38% 1243 20.75% 5474 20.30%1991 9497 20.42% 1361 20.42% 5943 20.44%1992 10708 20.62% 1659 20.97% 6742 20.54%1993 11039 20.75% 1814 21.72% 6969 20.55%1994 13804 20.88% 2275 21.86% 8929 20.83%1995 14315 20.92% 2526 21.20% 9396 20.86%1996 15918 20.81% 3263 20.93% 10383 20.77%1997 16663 20.81% 3735 21.59% 10967 20.68%1998 16616 21.09% 3776 21.82% 11414 20.94%1999 12139 20.83% 2970 20.90% 8525 20.85%

Trend per year 20.04% 20.80% 20.03%t-value 25.11*** 22.77** 23.37***

F-value (Chow test) 4.62**

Total obs. of growth (value) firms is the total number of observations of growth (value) firms in a given year. MedianCAR shows themedian valuation consequence of just missing analyst estimates by at most three cents in a given year. MedianCAR is the mediancumulative three-day market-adjusted abnormal return (CAR) surrounding the earnings announcement date. For example,20.64% is themedianCAR for observations of growth firms missing analyst estimates by at most three cents in 1984. Total obs. for all firms exceeds totalobs. of growth plus value firms because some firms cannot be classified as either growth or value firms. Trend per year is the slope of theordinary least squares regression ofCAR on Year. Chow test is a test of difference in the slope of the regressions for growth and value firms.***(**)Significant at the 1% (5%) level or better (one tailed test).


pertinent to H4. The temporal trends in the increase share of $30 than it is for one with $0.05, I considerin severity of the adverse valuation consequences for an alternative definition of surprise, which deflatesgrowth and value firms are 80 and 3 basis points ‘raw error’ (i.e., actual minus forecast) by therespectively, showing that the negative valuation absolute value of the forecast. After eliminatingimpact of small negative surprises has increased 77 cases where the forecast is $0.00, I consider smallbasis points per year more for growth firms. Another negative surprises to be those within 10% of theway to state this is that the temporal increase in the absolute value of the forecast.adverse valuation consequence is nearly27 times as Table 4 provides frequency results using thissevere for growth firms! A Chow test reveals the alternative definition of small negative surprise. Thedifference in trends for the two groups is significant results are qualitatively similar to those in Table 2,(t-value54.62, P,5% level, one-tailed test). providing further evidence consistent with hypoth-

This evidence strongly supports H4, and helps eses one and two. More specifically, consistent withexplain my findings supporting H2. Relative to value hypothesis one, there has been a significant temporalfirm managers, growth firm managers have mitigated decrease (trend per year520.41%,t-value527.95)their propensity to report quarterly earnings falling in the frequency of small negative surprises, andslightly short of analyst estimates because the ad- consistent with hypothesis two, the decrease isverse valuation consequence of doing so has in- significantly greater for growth than for value firmscreased in severity much more for growth firms. (i.e., a Chow test of the respective trends,20.71%Because the large temporal increase in managerial and20.35%, is significant).compensation in stock and options and in lawsuits Table 5 provides valuation consequence resultstriggered by stock price drops primarily pertains to using my alternative definition of a little bit of badgrowth firms, managers of growth firms are especial- news earnings. The results are mixed. Consistently reluctant in recent years to report small negative with hypothesis three and the results in Table 3,surprises. there is a significant increase in the negative valua-

tion consequences of missing analyst estimates by alittle bit (trend per year520.04%, t-value52

5 . Additional analyses 5.16%). However, inconsistent with hypothesis fourand the results in Table 3, there is no difference in

In this section I conduct three additional analyses. trend for value versus growth firms (i.e., both equalFirst, I examine if my frequency and valuation 20.04% per year). Thus, the results of the first threeconsequence results are robust to an alternative hypotheses (but not the fourth) are robust to andefinition of small negative surprise. Second, I alternative measure of small earnings surprise.determine if my valuation consequence results arerobust to inclusion in the ordinary least squares 5 .2. A potential correlated omitted variableregressions of a potentially correlated omitted vari-able, namely the small median negative earnings The valuation consequence of an earnings surprisesurprise. Third, I see if my valuation consequence is monotonically related to its magnitude (Beaver,results can be attributed to temporal changes in the Clarke & Wright, 1979), so an alternative explana-frequency of losses constituting my small negative tion for my results consistent with hypothesis three issurprise sample. that the magnitude of the near miss has increased in

recent years. To examine this issue, I correlate the5 .1. An alternative definition of small negative magnitude of median small earnings surprise eachsurprise year with year, and find the correlation to be of the

wrong sign [negative] and insignificant (PearsonThe results in Tables 2 and 3 are based on un- correlation520.38; Spearman correlation520.11).

deflated surprises, namely actual quarterly earnings I also rerun Eq. (2) above for all firms, growth firmsthat are not more than three cents below analyst and value firms, after adding the magnitude of theestimates. Because an error of three cents is rather median small earnings surprise as an additionaldifferent for a firm with actual quarterly earnings per independent variable. My results (not tabulated) are


Table 4Percent of earnings that miss analyst estimates by at most 10%


Total obs. Percentage Total obs. Percentage Total obs. Percentage

1984 3268 16.22% 378 18.78% 1832 16.21%1985 4469 16.29% 548 15.15% 2520 16.51%1986 5065 14.06% 603 13.27% 2911 14.81%1987 5492 14.73% 723 15.49% 3178 14.82%1988 6522 13.29% 926 12.42% 3784 14.01%1989 8353 15.20% 1141 13.85% 4974 16.22%1990 8805 15.32% 1216 14.56% 5383 16.35%1991 9294 13.48% 1328 13.03% 5844 14.25%1992 10 498 14.05% 1624 13.05% 6627 14.56%1993 10 870 13.23% 1785 11.48% 6866 13.76%1994 13 618 12.75% 2236 8.94% 8830 13.99%1995 14 104 12.54% 2491 8.39% 9273 13.64%1996 15 660 11.55% 3198 8.60% 10 245 12.52%1997 16 442 11.09% 3664 7.78% 10 845 12.45%1998 16 376 10.30% 3713 7.11% 11 270 11.58%1999 11 964 8.38% 2920 5.96% 8409 9.19%

Trend per year 20.41% 20.71% 20.35%t-value 27.95*** 210.15*** 26.08***

F-value (Chow test) 23.34***

Year is the sample year. Total obs. of growth (value) firms is the total number of observations of growth (value) firms in a given year.Percentage of growth (value) firms show the percent of reported quarterly earnings for growth (value) firms that fall at most ten percent shortof the absolute value of analyst estimates. For example, 18.78% indicates that 71 of the 378 actual quarterly earnings of growth firms missthe absolute value of analyst estimates by at most ten percent in 1984. Total obs. is less than in Table 2 because observations are omittedwhen the forecast equals zero. Total obs. for all firms exceeds total obs. of growth plus value firms because some firms cannot be classifiedas either growth or value firms. Trend per year is the slope of the ordinary least squares regression ofFREQ on Year. Chow test is a test ofdifference in the slope of the regressions for growth and value firms.***Significant at the 1% level or better (one tailed test).

qualitatively similar to those I reported in Sections profits, but it does not pertain to losses. Thus, an4.4 and 4.5 above, providing additional evidence in analysis failing to distinguish between profits and

10favor of hypotheses three and four. losses underestimates the valuation consequences ofearnings surprises, and the extent of the underesti-

5 .3. Profits versus losses mate is positively related to the frequency of lossesin the sample. An alternative explanation for my

The valuation consequence of an earnings surprise evidence in favor of hypotheses three is that thediffers for profits versus losses (Hayn, 1995) in that frequency of losses in the total sample has decreasedthe monotonic relation between the magnitude of over time. An alternative explanation for my evi-surprise and its valuation consequence pertains to dence in favor of hypothesis four is the frequency of

losses in the growth sample has decreased more in10More specifically, when I run the regression: recent years than it has in the value sample.

To examine these issues, I first correlate theCAR 5a1d *YEAR 1d*MEDIANSURPRISE ,t t t frequency of losses in my total sample of small

negative surprises each year with year. The Pearsonthe year coefficient,d, for all firms, growth firms and value firmsand Spearman correlations are 0.92 and 0.95, respec-is negative and significant, and thed for growth firms is

significantly more negative than that for value firms. tively, suggesting a significant temporal increase in


Table 5Valuation consequences of missing analyst estimates by at most 10%


Total obs. MedianCAR Total obs. MedianCAR Total obs. MedianCAR

1984 3268 20.04% 378 20.22% 1832 20.07%1985 4469 20.33% 548 20.27% 2520 20.30%1986 5065 20.35% 603 0.16% 2911 20.46%1987 5492 20.46% 723 21.04% 3178 20.50%1988 6522 20.36% 926 20.32% 3784 20.47%1989 8353 20.23% 1141 20.89% 4974 20.08%1990 8805 20.36% 1216 20.75% 5383 20.30%1991 9294 20.08% 1328 20.40% 5844 20.09%1992 10 498 20.37% 1624 20.52% 6627 20.40%1993 10 870 20.61% 1785 21.23% 6866 20.53%1994 13 618 20.67% 2236 21.42% 8830 20.62%1995 14 104 20.81% 2491 21.11% 9273 20.67%1996 15 660 20.64% 3198 20.75% 10 245 20.61%1997 16 442 20.62% 3664 20.51% 10 845 20.62%1998 16 376 20.87% 3713 21.01% 11 270 20.85%1999 11 964 20.76% 2920 20.55% 8409 20.90%

Trend per year 20.04% 20.04% 20.04%t-value 25.16*** 22.05* 24.34***

F-value (Chow test) 1.99

Total obs. of growth (value) firms is the total number of observations of growth (value) firms in a given year. MedianCAR shows themedian valuation consequence of just missing the absolute value of analyst estimates by at most ten percent in a given year. MedianCAR isthe median cumulative three-day market-adjusted abnormal return (CAR) surrounding the earnings announcement date. For example,20.22% is the medianCAR for observations of growth firms missing the absolute value of analyst estimates by at most ten percent in 1984.Total obs. is less than in Table 3 because observations are omitted when the forecast equals zero. Total obs. for all firms exceeds total obs.of growth plus value firms because some firms cannot be classified as either growth or value firms. Trend per year is the slope of theordinary least squares regression ofCAR on Year. Chow test is a test of difference in the slope of the regressions for growth and value firms.***(**)Significant at the 1% (10%) level or better (one tailed test).

consequence results of growth versus value firms, thethe loss frequency. Because a significant temporal results for hypothesis four cannot be explained bydecrease in the loss frequency is a necessary con- this factor.dition for temporal changes in the loss frequency toexplain my valuation consequence results, my thirdhypothesis cannot be explained by this factor. I next 6 . Summarycorrelate the frequency of losses in my sample ofsmall negative surprises of growth firms minus that I present four types of related evidence: (1)of value firms each year with year. Both the Pearson temporal decrease in the tendency of managers toand Spearman correlations are 0.88, suggesting a report quarterly earnings falling slightly short ofsignificantly greater temporal increase in the loss analyst estimates; (2) reduced temporal tendency offrequency of growth firms versus value firms. Be- managers to report earnings falling slightly short ofcause a significant temporal decrease in the loss analyst estimates better describes managers offrequency of growth firms versus value firms is a growth firms; (3) adverse valuation impact of report-necessary condition for temporal changes in loss ing quarterly earnings falling slightly short of analystfrequencies to explain my differential valuation estimates has become more severe in recent years;


and (4) adverse valuation consequence of falling pected small positive earnings surprises late in theslightly short of analyst estimates has increased in sample period, so when managers reported smallrecent years relatively more for growth than for negative surprises, investors pummeled stocks that

11value firms. failed to meet their expectations. In other words, theI interpret my results as follows. There has been same amount of a little bit of ‘bad news’ (using the

tremendous growth in managerial compensation in researcher’s conventional definition) was indeed farthe form of stock and options during the sample worse late, rather than early, in the sample period.period, 1984–99, especially for growth firms (TheEconomist, 1999). This time period has witnessed atremendous growth in momentum investing (Fox,

A cknowledgements1997; Byrnes, Melcher & Sparks, 1998), and inlitigation of firms whose stocks plunge (The

I am grateful to Marcus Caylor and IndrariniEconomist, 2000). Managers are loath to be sued, seeLaksmana for their assistance and to Thomsontheir stock get torpedoed or find their options underFinancial I /B/E/S for providing me with data forwater. To mitigate the occurrence of these phenom-this study.ena, managers have reduced their propensity to

report quarterly earnings falling slightly short ofanalysts’ estimates. One way they have done this isby increasing their propensity to pre-announce bad R eferencesnews (Skinner, 1994; Soffer, Thiagarajan & Walther,2000). Some have argued that Regulation Full Barth, M., Elliott, J., & Finn, M. (1999). Market rewardsDisclosure has increased this propensity even more associated with increasing earnings patterns.Journal of Ac-

counting Research, 37, 387–414.(I /B/E/S, 2001). Evidence that the valuation conse-Bartov, E., Givoly, D., & Hayn, C. (2000). The rewards toquence of falling slightly short of analyst estimates

meeting or beating earnings expectations. Working paper. Newhas become more severe in recent years is consistentYork University. http: / /papers.ssrn.com/sol3 /pa-

with temporal increases in options, momentum in- pers.cfm?cfid5803941&cftoken597066437&abstract-id5vesting and the frequency of lawsuits triggered by 247435

Beaver, W. H., Clarke, R., & Wright, W. F. (1979). The associationearnings shortfalls. Because options, momentumbetween unsystematic security returns and the magnitude ofinvesting and lawsuits triggered by stock plungesearnings forecast errors.Journal of Accounting Research, 17,primarily pertain to growth firms, the phenomena I316–340.12document are more descriptive of growth firms. Brown, L. D. (2001). A temporal analysis of earnings surprises:

My results are consistent with analysts having Profits versus losses.Journal of Accounting Research, 39,221–241, http: / /papers.ssrn.com/sol3/papers.cfm?cfid5rational expectations of earnings surprises. During803976&cftoken583800757&abstrac-id5265441.my sample period, earnings surprise shifted from

Burgstahler, D., & Eames, M. (1999). Management of earningssmall negative to small positive (Brown, 2001).and analysts’ forecasts. Working paper. University of Washing-

Investors expected small negative earnings surprises ton.early in the sample period, so when firms reported Byrnes, N., Melcher, R., & Sparks, D. (1998). Earnings hocus-

pocus: how companies come up with the earnings they want.same, the negative valuation consequences of smallBusiness Week(November 5), 134–142.negative surprises were not severe. Investors ex-

Degeorge, F., Patel, J., & Zeckhauser, R. (1999). Earningsmanipulation to exceed thresholds.Journal of Business, 72,

11 1–33.It may seem that my first two findings follow directly fromFox, J. (1997). Learn to play the earnings game (and Wall StreetBrown (2001), who showed: (1) a temporal increase in small

will love you). Fortune(March 31), 76–80.positive earnings surprises and (2) the increased tendency toFrancis, J., & Schipper, K. (1999). Have financial statements lostslightly beat analyst estimates pertains more to growth than to

their relevance?Journal of Accounting Research, 37, 319–352.value firms. However, Brown’s results may imply something otherthan what is documented in this study, such as a temporal decreaseHayn, C. (1995). The information content of losses.Journal ofin the tendency of firms to report large positive earnings surprises. Accounting and Economics, 20, 125–153.12Recall, however, that my valuation consequence results (hypoth- I /B/E/S (1999).The I /B /E /S glossary: a guide to understandingesis four) are sensitive to my definition of small surprise. I /B /E /S terms and conventions. New York: I /B/E/S.

http://papers.ssrn.com/sol3/papers.cfm?cfid=803941






























I /B/E/S (2001).U.S. monthly comments. February. New York: Skinner, D., & Sloan, R. (1999). Earnings surprises, growthI /B/E/S. expectations and stock returns or don’t let a torpedo sink your

Johnson, N. (1999). Managed earnings and the year of the portfolio. Working paper. University of Michigan.http: /accountant. Speech before the Utah state bar mid-year conven- /papers.ssrn.com/sol3 /papers.cfm?cfid5804081&cftoken5tion. (March 6).http: / /www.sec.gov/news/speech/speecharc- 985006&abstract-id5172060.hive/1999/spch264.htm. Soffer, L., Thiagarajan, S., & Walther, B. (2000). Earnings

Kasznik, & Lev, (1995). To warn or not to warn: management preannouncement strategies.Review of Accounting Studies, 5,disclosures in the face of an earnings surprise.Accounting 5–26.Review, 70, 113–144. (1999). A survey of pay.The Economist, May 8th, 1–20.

Kasznik, R., & McNichols, M. (2001). Does meeting expectations (2000). Ally, get your gun.The Economist, September 30th,matter? Evidence from analyst forecast revisions and share 33–34.prices. Journal of Accounting Research (forthcoming).http: / Vickers, M. (1999). Ho-hum, another earnings surprise.Business/papers.ssrn.com/sol3 /papers.cfm?cfid5804000&cftoken5 Week, May 24, 83–84.6364640&abstract-id5189750.

Levitt, A. (1998). The numbers game. Remarks delivered at the Biography: Lawrence D. BROWN is the J. Mack RobinsonNYU center for law and business. (September 28). Distinguished Professor of Accountancy at Georgia State Uni-

Lopez, T., & Rees, L. (2001). The effect of meeting analyst versity. He has a Ph.D. from the University of Rochester, anforecasts and systematic positive forecast errors on the in- M.B.A. from the University of Chicago, and a B.S. from SUNY-formation content of unexpected earnings. Journal of Account- Buffalo. He is the author or co-author of 80 publications, and heing, Auditing and Finance (forthcoming). http: / has made over 100 presentations at universities and professional/papers.ssrn.com/sol3 /papers.cfm?cfid5804017&cftoken5 conferences. He is an Associate Editor or Editorial Board Member40855408&abstract-id5181929. of five journals,The Accounting Review, Contemporary Account-

Matsumoto, D. (2001). Management’s incentives to avoid nega- ing Research, International Journal of Forecasting, Journal oftive earnings surprises. Working paper. University of Washing- Accounting, Auditing and Finance, and Review of Quantitativeton. http: / /papers.ssrn.com/sol3/papers.cfm?cfid5 Finance and Accounting, and he is an Executive Committee804036&cftoken567784125&abstract-id5173390. Member of the Annual Conference on Financial Economics and

McGough, R. (2000). How ‘round-ups’ can give stocks a hard Accounting. He has consulted for numerous organizations, includ-ride. Wall Street Journal, July 21, C1–2. ing Bulldogresearch.com, DAIS Group, Grantham, Mayo, Van

Payne, J., & Robb, S. (2000). Earnings management: The effect of Otterloo, I /B/E/S International Inc., Department of Justice,ex ante earnings expectations.Journal of Accounting, Auditing Volume Investor, and Zacks Investment Research Inc.and Finance, 15, 371–392.

Skinner, D. (1994). Why firms voluntarily disclose bad news.Journal of Accounting and Economics, 32, 38–60.












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small negative surprises: frequency and consequence

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