Journal of Accounting Research
Vol. 40 No. 3 June 2002
Printed in U.S.A.
Earnings Predictability, Information
Asymmetry, and Market Liquidity
J O H N A F F L E C K - G R A V E S ,∗ C A R O L Y N M . C A L L A H A N ,†
A N D N I R A N J A N C H I P A L K A T T I‡
Received 1 May 1996; accepted 21 December 2001
ABSTRACT
We investigate the relation between earnings predictability, information
asymmetry and the behavior of the adverse selection cost component of the
bid-ask spread around quarterly earnings announcements for NASDAQ firms.
While we find an increase in the adverse selection component of the bid-ask
spread on the day of and the day prior to quarterly earnings announcements
for firms with less predictable earnings, we find no evidence of such changes for
firms with more predictable earnings. During a non-announcement period, we
find that firms with relatively less predictable earnings have consistently higher
total bid-ask spreads than firms with more predictable earnings. This finding
suggests that firms with relatively less predictable earnings have a higher cost of
equity capital than comparable firms with more predictable earning streams,
ceteris paribus. Hence, earnings predictability may be a legitimate concern of
managers who wish to minimize their cost of equity capital at least as it pertains
to bid-ask spreads.
∗ University of Notre Dame; †University of Arkansas; ‡Ohio Northern University. We thank
I/B/E/S Inc. for their database of earnings estimates and two anonymous referees for their
many useful comments and suggestions. We gratefully acknowledge the comments of Linda
Bamber, Mary Barth, George Benston, Bill Cready, John Elliott, E. Ann Gabriel, Charles M.
C. Lee, Choa-Shin Liu, Mort Pincus, Chris Olsen, Grace Pownall, Tom Stober, Greg Waymire,
Ro Verrecchia, and Jerry Zimmerman as well as workshop participants at University of Notre
Dame, Case Western Reserve University, Emory University, Michigan State University, The Ohio
State University, University of Wisconsin, and Texas A & M University. We are also grateful to
workshop participants at the 1996 National American Accounting Association Annual Meeting
in Chicago and the 1997 Financial Management Association Meeting in New Orleans. The
research support received by Carolyn M. Callahan from the KPMG Foundation is gratefully
acknowledged.
561
C , University of Chicago on behalf of the Institute of Professional Accounting, 2002
Copyright 562
J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
1. Introduction
We examine the relation between earnings predictability and information
asymmetry, as revealed by the change in the adverse selection cost component (hereafter, adverse selection cost) of the bid-ask spread around quarterly earnings announcements while controlling for volume effects. Second,
during a non-announcement period, we examine the link between earnings
predictability and total bid-ask spreads arguing that higher spreads imply a
higher cost of equity capital for the firm, ceteris paribus. The prior empirical evidence on bid-ask spread changes around earnings announcements
is mixed. Morse and Ushman [1983] and Skinner [1991], using Over-theCounter (OTC) samples, find no clear evidence of such changes, while Krinsky and Lee [1996] and Lee, Mucklow, and Ready [1993], using New York
Stock Exchange (NYSE) samples, find a significant increase in spreads
surrounding earnings announcements.1
We extend these earlier studies in two ways. First, unlike Krinsky and Lee
[1996] and Lee, Mucklow, and Ready [1993] who focus on NYSE firms, our
study examines the bid-ask spreads of 247 National Association of Securities
Dealers Automated Quotations (NASDAQ) firms from 1985–90. Examining
earnings announcement effects and the cost of transacting using NASDAQ
stock market data is of interest because of differences in the accuracy with
which transaction costs can be measured on NASDAQ versus NYSE. Also,
the inside quotes on NASDAQ are likely to be a better proxy for the actual cost of transacting as NASDAQ market-makers do not face competition
from floor traders (Eleswarapu [1997]). Further, the NYSE quoted spreads
are only representative in nature, with many of the transactions actually
occurring inside the quotes.2
Second, using an ex-ante adverse selection cost model, we examine the impact of earnings predictability, which we operationalize using metrics based
on analysts’ annual forecast errors and dispersion. Consistent with the theoretical work in the literature (e.g., Glosten and Harris [1988]), we argue that
less predictable earnings releases increase the information asymmetry between privately informed investors and dealers (hereafter, market-makers)
in the capital markets. To compensate for this informational disadvantage,
we hypothesize that a market-maker’s increase in the bid-ask spread at the
time of an earnings announcement will be more pronounced for firms with
less predictable earnings.
Our empirical results show an increase in the adverse selection cost of
the bid-ask spread on the day of and the day prior to quarterly earnings
announcement dates for NASDAQ firms with less predictable earnings. In
1 For a survey of the research on the relation between accounting information and bid-ask
spreads, see Callahan, Lee, and Yohn [1997].
2 Huang and Stoll [1996] provide a detailed discussion of structural differences between
NASDAQ and the NYSE. They also document significantly more trades inside the quotes for
NYSE stocks.
EARNINGS PREDICTABILITY
563
contrast, we find no evidence of a significant change in the adverse selection cost of the bid-ask spread around quarterly earnings announcements
of firms with highly predictable earnings. Further, we control for the joint
effects of volume and spread. We show that our spread results for low predictability firms are not driven by volume decreases.
To investigate the broader cost of equity capital implications of our hypothesis, we examine also whether firms with more predictable earnings
have lower total bid-ask spreads in a non-announcement period. Our results
show that firms with relatively less predictable earnings have consistently
higher total bid-ask spreads in the non-announcement period, suggesting
that a firm with relatively less predictable earnings will have a higher cost
of equity capital than a comparable firm with a more predictable earning
stream.
2. Research Hypotheses
The extant market microstructure literature demonstrates that the quoted
bid-ask spread is a function of three cost components incurred by the marketmaker: order processing, inventory holding, and adverse selection costs.
The inventory holding cost represents the market-maker’s cost of holding suboptimal levels of inventories while order processing costs represent
the market-maker’s fixed costs such as clearing and settlement costs. The
“adverse selection” cost of the spread is directly related to the perceived level
of information asymmetry in the capital market as an uninformed marketmaker will increase the spread to compensate for expected losses to privately
informed traders (Glosten and Harris [1988]).
Prior literature demonstrates that earnings predictability can affect the
market response to an earnings release (Imhoff and Lobo [1992] and Pincus
[1983]). We argue that firms with less predictable earnings are characterized by relatively larger mean absolute annual forecast errors and larger
mean dispersions averaged over several years. Further, we contend that low
earnings predictability increases information asymmetry in the market and
increases trading opportunities for the informed trader which may influence the market-maker’s adverse selection cost. This argument suggests the
following hypothesis:
H1:
There will be a significant increase in the abnormal adverse selection cost of the bid-ask spread around quarterly earnings announcements for firms with low earnings predictability.
Further, we hypothesize that firms with more predictable earnings may also
have lower bid-ask spreads in non-announcement periods and hence a lower
cost of equity capital:
H2:
Firms with relatively more predictable earnings have consistently
lower total bid-ask spreads across time than firms with less predictable earnings.
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J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
3. Sample Selection and Variables
We draw our sample from the CRSP-NASDAQ 1989 data tapes from the
University of Chicago. We restrict the initial sample to NASDAQ firms that
are on both the COMPUSTAT tapes (to be used for quarterly and annual
financial data) and the Institutional Brokers’ Estimation System (I/B/E/S)
consensus tapes (to be used for earnings predictability data). The full analysis period is 1985–90, but the sample includes only firms with full financial
data for the years 1980 to 1989 to obtain companies that have been active
for at least ten years. We also require that all sample firms file 10-K reports
with the Securities Exchange Commission (SEC) on a regular basis because
we use these filing dates to select analyst earnings forecasts to calculate our
earnings predictability measures. To ensure comparability of our firms, we
limit our sample to firms with a December 31 fiscal year-end. Our sample
firms must also have annual and quarterly earnings forecast data on the
I/B/E/S tape for at least four of the six years from 1984 to 1989, and they
must have at least one quarterly earnings announcement published in the
Wall Street Journal (WSJ) during the analysis period. We use the annual forecast data to compute the earnings predictability scores while we use the
quarterly forecasts to calculate unexpected earnings, a control variable in
our robustness tests.
Initially, we obtained a sample of 329 firms.3 Fifty-eight of those sample
firms did not meet our SEC filing criteria. We excluded twenty firms due to
confounding events (mergers, acquisitions, and dividend announcements)
in the confounding window starting two days prior to the announcement
and ending two days after the announcement (Krinsky and Lee [1996] and
Lee, Mucklow, and Ready [1993]). We eliminated an additional four firms
from the final sample because of missing data on the I/B/E/S tape or other
sources for the variables needed to calculate the earnings predictability
score. These reductions led to a final sample of 247 firms. After excluding
57 observations with a value of zero for primary annual earnings per share
(eliminated as a forecast error deflator), we base our tests on 2,941 firmevents (quarterly earnings announcements; with observations per quarter,
quarter 1; 779, quarter 2; 788, quarter 3; 877, quarter 4; 497).
4. Research Methods
We use a standard event study methodology to examine our first hypothesis. Denoting the quarterly earnings announcement date as trading day 0, we
set the parameter estimation period to begin 146 trading days (day −146)
and to end 11 trading days (day −11) prior to the quarterly earnings announcement date. The analysis period ranges from trading day −3 to trading
3 The update code on COMPUSTAT indicates that the company has been updated from its
final source (usually the 10-K or 10-Q). We used this screen to avoid companies with unreliable
sources of financial data.
EARNINGS PREDICTABILITY
565
day +3, inclusive of trading day 0. In robustness tests, we use a multivariate
linear regression model to control for other factors related to the market
impact of an earnings announcement such as firm size, reporting lag, number of market-makers, the earnings surprise, and unexpected volume and
return. To examine our second hypothesis, we estimate a time series linear
regression model to test the prediction that the magnitude of the total bidask spread is decreasing in earnings predictability over a non-announcement
period, 9/1/88 to 12/31/90 excluding earnings announcement dates or 589
trading days.
4.1
EARNINGS PREDICTABILITY
We use both consensus analysts’ forecast errors and the dispersion of forecasts to measure earnings predictability. The consensus analysts’ forecast for
a firm i in year y (CYFi,y , y = 1984 to 1989) is the first consensus annual forecast dated after the year y −1 10-K filing date (Stice [1991]).4 We obtained the
primary annual earnings per share before discontinued operations, extraordinary items and cumulative effects of an accounting change (COMPUSTAT
annual item #58) (AEPSi,y ) for each firm i for the years y = 1984 to 1989.5 We
then calculated the standardized absolute forecast error (SAFEi,y ) as follows:
SAFEi,y = |(CYFi,y − AEPSi,y )/AEPSi,y |,
(1)
Following Imhoff [1992], we calculated the mean standardized absolute
forecast error over the period 1984 to 1989 for each firm as follows:
SAFEi = 1/K
k
SAFEi,y
(2)
y =1
where, K takes the value 4 to 6, depending on data availability. We argue
that the higher the mean standardized absolute forecast error, the lower
the predictability of the firm’s earnings.
Second, for each firm i and each year y, we calculated the relative dispersion of analysts’ forecasts for the first consensus annual forecast issued after
the year y −1 10-K report filing date using the standard deviation of analysts’
forecasts as reported by I/B/E/S:
DAFi,y = (Standard Deviation of Analysts’ Forecasti,y )/|CYFi,y |.
(3)
4 For firms where the SEC date stamp was not clear or was not found on the 10-K report,
we used the May consensus forecast to derive the earnings predictability score. This usage is
consistent with Swaminathan [1991].
5 When AEPS , the deflator, is zero, the forecast error measure is undefined, accordingly we
i,y
excluded 57 zero observations from our sample, less than 2% of the firm observations meeting
all other data requirement criteria (Foster [1977] and Sinha, Brown, and Das [1997]). See
O’Brien [1988, page 60, footnote 6] for a more detailed discussion of alternative forecast error
deflators.
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J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
For each firm i, we calculated the mean relative dispersion of analysts’ forecasts just after the year y −1 10-K report filing date over the period 1984 to
1989 as follows:
DAFi = 1/K
k
DAFi,y .
(4)
y =1
Once again, K varies from 4 to 6 depending on data availability. We argue
that the higher the mean relative dispersion of analysts’ forecasts, the lower
the predictability of the firm’s earnings.
4.2
EARNINGS PREDICTABILITY SCORE
We compose a firm classification scheme for the earnings predictability
scoring that requires benchmark NASDAQ sample medians for SAFE and
DAF. To determine the benchmarks for the time period 1984–89, we calculate the mean standardized absolute forecast error (equation 2) and the
mean relative dispersion of analysts’ forecasts (equation 4) for all NASDAQ
firms with forecast and financial data available on the COMPUSTAT and
I/B/E/S tapes with an annual earning announcement date on COMPUSTAT or Dow Jones News Retrieval (DJNR). Our earnings predictability
metrics also require a known 10-K SEC filing date. Based on the specified criteria, the NASDAQ sample medians for SAFE and DAF are based on
614 firms and the computed cross-sectional medians are 0.68 and 0.11. We
then use these NASDAQ medians to assign an earnings predictability score to
each firm event (2,941) in our sample based on the following decision rule:
Group
Score
Decision Rule
High Predictability
1
Mixed Predictability
2
Low Predictability
3
SAFEi and DAFi both less than NASDAQ
sample medians
SAFEi less than NASDAQ sample median
but DAFi greater than NASDAQ sample
median, or SAFEi greater than NASDAQ
sample median but DAFi less than NASDAQ sample median
SAFEi and DAFi both greater than NASDAQ sample medians
5. Statistical Models and Tests
5.1
UNIVARIATE TESTS
For our univariate event study tests for the abnormal adverse selection
cost of the spread and the mean-adjusted abnormal trading volume for the
high and low earnings predictability firms in our sample, we use the Brown
and Warner [1985] t-Statistic adjusted for autocorrelation (Seyhun [1986,
page 195]) based on the following procedures.
EARNINGS PREDICTABILITY
567
5.1.1. Abnormal Adverse Selection Cost Measure. To examine the quarterly
earnings announcement effect of the adverse selection cost of the bid-ask
spread in the analysis period (day −3 to day +3), we define the abnormal
adverse selection cost of the spread as the difference between the actual
percentage spread (Si,t ) and the expected spread E(Si ) in the absence of
informed trading:
AAC i,t = Si,t − E (Si,t )
(5)
The actual percentage bid-ask spread is defined as:
Si,t = ln[ASKi,t − BIDi,t ]/Pi,t
(6)
where ASKi,t is the ask price for firm i, BIDi,t is the bid price for firm i, and
Pi,t is the stock price for firm i defined as the average of the bid and ask
prices at the close of trading day t.6
The estimated spread is based on a simultaneous equation model. A system of equations is necessary because of simultaneity between spread and
volume (Glosten and Harris [1988] and Hegde and Miller [1989]). In addition, given the presence of non-normalities in the data, we use a log-linear
version of the model. We estimate the model based on our 2,941 quarterly
earnings announcements (quarter 1; 779, quarter 2; 788, quarter 3; 877,
quarter 4; 497) from 1984 to 1989. At each quarterly announcement date,
we estimate the following simultaneous equation model separately for each
firm observation over the estimation period (day −146 to day −11) using
Three Stage Least Squares, assuming cross-correlation between the structural error terms:7
Si,t = a0 + a1 (TV i,t ) + a2 (Pi,t ) + a3 (Ri,t ) + a4 (Si,t−1 ) + ηi,t
TVi,t = b 0 + b 1 (Si,t ) + b 2 (MFt ) + b 3 (TVi,t−1 ) + µi,t
(7a)
(7b)
where, (7a) and (7b) are the structural equations; and,
TVi,t = the logarithm of the volume of trade for firm event i on trading
day t ;
Pi,t = the logarithm of the closing price for firm event i on trading day t ;
Ri,t = the logarithm of the absolute change in price for firm event i from
trading day t − 1 to trading day t ;
MFt = the logarithm of the total trading volume on the NASDAQ on trading day t ;
t
= trading day −146 to trading day −11, the estimation period;
6 Although our focus is on the behavior of the abnormal adverse selection cost of the spread
around quarterly earnings releases, we examined also the mean adjusted total spread for comparability with previous studies. Results are available from the authors upon request.
7 If the restrictions imposed in our estimation of equations (7a) and (7b) are in error,
parameter estimates arising from Three Stage Least Squares estimation will be inconsistent. To
assess the robustness of our estimation results, we reestimated the system using two stage least
squares; all results were similar.
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J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
ηi,t = the normally distributed random error term;
µi,t = the normally distributed random error term.
Pi,t , Ri,t , MFt , Si,t−1 , and TVi,t−1 are exogenous variables, while Si,t and
TVi,t are endogenous.8 The first structural equation controls for the inventory and order processing costs of the spread assuming that the abnormal adverse selection cost is orthogonal to these components. The second
structural equation accounts for the market-maker’s inventory policy and
for possible first-order autocorrelation in volume and spread (Conrad and
Niden [1992] and Seyhun [1986]). These exogenous proxy variables are
drawn from prior research (Glosten and Harris [1988], Morse and Ushman
[1983], and Stoll [1978]).
We use Equation (7a) to estimate the expected spread on each trading
day in the analysis period (day −3 to day +3) inclusive of the earnings announcement. The estimation equation (7a) is based on the reduced form
coefficients from the structural equations and the values of the exogenous
variables to obtain the expected spread. The difference between the actual
spread on each trading day in the analysis period and the expected spread
computed using equation (7a) is an estimate of the abnormal adverse selection cost of the spread.
5.1.2. Brown and Warner [1985] t-Statistic. For tests over the analysis period (day −3 to day +3), the Brown and Warner [1985, page 7] test t -Statistic
is used. The t -Statistic is the ratio of the cross-sectional mean abnormal
adverse selection cost on event day t to the estimation period time series
standard deviation.
AACt
t-Statistic =
(8)
sˆAACt
where t represents day −146 to day −11, AAC t is the average abnormal
adverse selection cost over the 2,941 firm events on day t and ŝ AAC t is the
standard deviation of the average abnormal adverse selection cost estimated
over the estimation period. By averaging the abnormal adverse selection
costs across all observations for an event day, the test statistic adjusts for
cross-sectional dependence in firm-specific abnormal adverse selection costs
but ignores time series dependence in the same. To correct for the effect
of first-order autocorrelation on the mean AAC stream, the test statistic
was modified to incorporate the adjustments suggested by Seyhun [1986,
page 195]. Empirical examination of the AAC series indicates the process is
generated by a first-order autoregressive process of the form:
AAC i,t = δ + 1 AAC i,t−1 + µt
(9)
The standard error of the AAC series is computed by solving the Yule-Walker
8
We reject the null hypothesis that Si,t and TVi,t are exogenous using the Hausman test
= 153.74, one-tail p < 0.01). Hence simultaneous equation estimation of spread and volume
is appropriate.
(χ 2
EARNINGS PREDICTABILITY
569
equations corresponding to equation (9) above.9 The sample standard errors calculated in the estimation
period (day −146 to day −11) are multi
plied by a factor equal to σ 2 /(1 − φ1 ) where φ is equal to the first order
autocorrelation and σ 2 is equal to the random error associated with the
first-order autoregression function.
5.2
MULTIVARIATE CONTROL REGRESSION TESTS
We use a linear regression model to control for trading lag effects
(Hasbrouck [1991]) and other factors related to the impact of the earnings announcement such as firm size, reporting lag, the number of marketmakers, and the earnings surprise. We also control for cross-sectional price
and volume effects. With signed predictions of the independent variables
indicated, our cross-sectional model estimated over all 2,941 quarterly announcements is defined as follows:
+
+
+
+
CSUi = α1 + α2 (Q E 2i ) + α3 (Q E 3i ) + α4 (U Ei ) + α5 (L A Gi )
+
+
¯ i ) + α8 (CAR i ) + α9 (C VOLi ) + τi (10)
+ α6 ( M̄ Mi ) + α7 (SI ZE
The dependent variable, CSUi is the abnormal adverse selection cost estimates cumulated over the two-day event window (day −1 to day 0) as follows:
0
CSUi =
P̄ i
AAC i,t
(11)
t=−1
where AACi,t is defined in equation (5) and P̄ i is the average of the mean
bid price and the mean ask price of firm i event in the estimation period
(day −146 to day −11).
The independent variables are as follow:
QE2i = an indicator variable set to 1 if the firm observation is in the medium
earnings predictability group (i.e., with score 2) and 0 otherwise;
QE3i = an indicator variable set to 1 if the firm observation is in the low
earnings predictability group (i.e., with score 3) and 0 otherwise;
UEi = the logarithm of the unexpected quarterly earnings of the firm in
that quarter;
LAGi = an indicator variable set to 1 if the firm reports later than the expected lag period and 0 otherwise;
MMi = the logarithm of the average number of market-makers dealing in
the security in the estimation period;
SIZEi = the logarithm of the firm’s total assets measured at the beginning
of the calendar year prior to the analysis period;
9
The first order serial correlation was determined by examining the partial autocorrelation
coefficients (PACF) of AACt in equation (9) at lag (1). The PACF is 0.27 with t-statistic = 8.89 and
p = 0.00 for the high earnings predictability group, N = 850, and 0.15 with t-statistic = 2.45 and
p = 0.01 for the low earnings predictability group, N = 1, 124. For a discussion of autoregressive
processes and diagnostic tests, see Box and Jenkins [1976].
570
J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
CARi = the risk adjusted two-day cumulative abnormal return over the
event period (day −1 to day 0);
CVOLi = the mean adjusted cumulative abnormal volume over the event
period (day −1 to day 0);
τi
= the normally distributed random error term.
We examine the robustness of the earnings predictability effect using
two indicator variables QE2i and QE3i . Our hypothesis that the cumulative
adverse selection cost is expected to be greater for firms with low earnings
predictability predicts that α3 > α2 > α1 .
We compute unexpected earnings UEi as the logarithm of the absolute
value of the difference between the reported primary quarterly earnings
per share (COMPUSTAT quarterly item #19) and the last I/B/E/S consensus analyst forecast of the firm’s primary earnings per share for the
quarter issued prior to the earnings announcement date. α4 is predicted
to be positive as the larger the magnitude of the unexpected earnings
component, the greater the value of the informed traders’ unique firm
information which increases the adverse selection cost faced by the marketmakers.
The timeliness of the release of the earnings report can effect the adverse
selection cost of the market-maker as the longer the earnings announcement
is delayed, the larger the window available to informed traders to trade on
their unique information. We define the actual reporting lag as the calendar
time from the end of the quarterly fiscal period up to and including the
announcement date (Chambers and Penman [1984]). The actual reporting
lag in the previous fiscal period is used as the expected lag in the analysis
period. α5 is predicted to be positive as it reflects the unanticipated delay in
the release of the earnings report.
As the number of market-makers making a market in a stock increases,
the more liquid will be the market for the security (Copeland and Galai
[1983]). We obtain the number of market-makers for each trading day in
the estimation period (day −146 to day −11) from the CRSP-NASDAQ tape
and the average over the 136 trading days in the estimation period provides
the estimate for each firm event. We expect that α6 will be negative as an
increase in the number of market-makers reduces the adverse selection cost
faced by each market-maker individually.
Atiase [1987] provides empirical evidence that the pre-disclosure information available for a firm varies positively with the firm’s size. Therefore,
α7 is predicted to be negative as small firms with lower levels of pre-disclosure
information are more likely to provide opportunities for informed trading
against the market-maker and thereby increase adverse selection cost.
Lee, Mucklow, and Ready [1993, table 5] report a more pronounced
widening of spreads in firms with greater subsequent price movements and
show changes in spread around earnings announcements are primarily related to volume. To examine whether our low earnings predictability results
are driven by cross-sectional volume and price response to the quarterly
EARNINGS PREDICTABILITY
571
earnings announcement, we include the two-day cumulative abnormal
return (CAR i ) and abnormal volume (CVOL i ) in our model.
To control for price response, CAR i is defined as the risk-adjusted two-day
cumulative abnormal return from trading day −1 to trading day 0, where
trading day 0 is the quarterly earnings announcement date obtained from
the quarterly COMPUSTAT tape or DJNR. Market model parameters necessary to compute CAR i are estimated using daily stock returns and the
value-weighted market returns as collected from the CRSP files over the
estimation period (day −146 to day −11).
To control for volume effects, we include cumulative abnormal volume
(CVOL i ) in our model. We define CVOL i as the two-day cumulative mean
adjusted abnormal volume from trading day −1 to trading day 0. First we
calculate the mean daily percentage of firm i’s shares traded in the estimation period (day −146 to day −11). Abnormal volume is then defined as
the difference between the actual percentage of firm i’s shares traded on
trading day t and the mean pre-announcement period volume.
5.3
LINEAR REGRESSION TIME SERIES TEST
Based on our earnings predictability classification scores (high, medium,
and low), three portfolios of firms were formed on each trading day over
a non-announcement period, 9/1/88 to 12/31/90 excluding earnings announcement dates, 589 trading days. We then computed the portfolio mean
percentage spread (the total bid-ask spread deflated by the average price at
the close of trading on day d) for each earning predictability group on each
of the 589 trading days in the non-announcement period. To test the prediction that the magnitude of the total bid-ask spread is decreasing in earnings
predictability, we use the 1,767 daily earnings predictability group means
(3 groupings × 589 trading days) to estimate a time series linear regression
model as follows:
PSPRDg,d = α1 + α2 (QE2g,d ) + α3 (QE3g,d ) + α4 (Y 90) + τg,d
(12)
where g is the earnings predictability group (high, medium, low) and d
is trading day 1 (9/1/88) through trading day 589 (12/31/90) in the nonannouncement period. Huang and Stoll [1996] document an increase in the
level of the bid-ask spread in 1990. Therefore, we use the indicator variable
Y90 which is set to 1 if the trading day is in year 1990 and 0 otherwise.10
6. Results
Our sample contains 247 companies and 2,941 firm quarter observations.11 Of the 2,941 firm observations, we classify 1,124 in the low earnings
10 Figure 1 also suggests that spreads may be larger in 1990 than in 1988 and 1989. Excluding
Y90 does not alter the significance we report for coefficients α2 and α3 in table 4.
11 For most events in our sample (76%), the mean bid-ask spread falls between 1/4 and 3/4.
Descriptive statistics (means and standard deviations) for the spread, daily volume, bid and ask
price, and firm size for sample firms are also available from the authors.
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J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
TABLE 1
Industry Classification of Sample Observations From 1985 to 1990 a
Two-Digit
SIC
12
13
20
22
24
25
26
27
28
29
30
32
33
34
35
36
37
38
42
45
48
Industry
Coal/lignite mining
Mining: non-metallic
minerals
Food & kindred
products
Textile mill products
Lumber & wood
products
Furniture & fixtures
Paper & allied products
Printing and publishing
Chemicals & allied
products
Petroleum refining
& related
Rubber & misc.
plastic products
Stone, clay, glass,
concrete products
Primary metal industry
Fabricated metal
except machinery
Machinery except
electrical
Electrical, electrical
machinery
Transportation
equipment
Instruments and related
products
Motor freight,
transport, etc.
Transportation by air
Communication
No. of
Firms Observations
Number of Firm Events by
Earnings Predictability Groupb
Highc
Mediumd
Low e
5
5
45
55
15
0
0
0
30
55
4
63
0
26
37
2
2
16
43
16
0
0
43
0
0
2
11
5
7
9
106
72
98
0
18
72
86
0
36
0
12
9
52
0
0
3
28
28
0
0
2
23
0
0
23
2
21
0
21
0
2
2
45
11
0
0
0
0
45
11
14
195
0
78
117
13
182
29
84
69
7
72
0
67
5
13
179
43
58
78
6
64
0
18
46
2
11
21
105
0
0
0
72
21
33
predictability group and 850 in the high earnings predictability group. The
industry classification of our sample based on two-digit SIC codes is displayed in table 1. One hundred and six firms (42.9% of the sample) are
clustered in regulated industries (SICs 49, 60, 63, and 65) and represent
1,193 firm-events. Of these 1,193 firm-events, 451 are classified in the high
predictability group, and 385 in the low predictability group.12
12 To ensure that our results were not caused by the special characteristics of regulated
firms, we replicated all tests using the subsample excluding these firms. All our inferences were
unchanged.
EARNINGS PREDICTABILITY
573
T A B L E 1—Continued
Two-Digit
SIC
49
50
51
54
60
63
65
73
87
Industry
Electric, gas,
sanitary services
Durable goods-wholesale
Non-Durable
goods-wholesale
Retail-Food Stores
Banking
Insurance
Real Estate
Business Services
Other
Totals
No. of
Firms Observations
Number of Firm Events by
Earnings Predictability Groupb
Highc
Mediumd
Low e
9
99
49
24
26
6
4
92
60
0
60
0
0
92
0
2
70
11
16
7
2
16
780
147
167
108
19
16
380
22
0
16
0
0
170
45
118
76
19
0
230
80
49
16
0
247
2,941
850
967
1,124
a
The sample includes 247 firms and 2,941 firm quarter observations (quarter 1; 779, quarter 2; 788,
quarter 3; 877, quarter 4; 497) classified by two-digit SIC code. Each observation is based on a quarterly
earnings announcement.
b
Firms are classified into an earnings predictability group based on both the mean standardized absolute
value consensus analysts’ forecast error (SAFE) and dispersion of forecasts (DAF) over the period 1984–89.
SAFE is the absolute value of primary annual earnings per share before discontinued operations, extraordinary items and cumulative effects of an accounting change (COMPUSTAT annual item #58) minus
the first consensus annual forecast dated after the year y -1 10-K filing date, standardized by primary annual
earnings per share defined per the numerator. DAF is dispersion of analysts’ forecasts for the first consensus
annual forecast issued after the year y -1 10-K report filing date using the standard deviation of analysts’
forecasts as reported by I/B/E/S, deflated by the absolute value of the first consensus annual forecast dated
after the year y -1 10-K filing date. The firm classification scheme for the earnings predictability scoring
requires benchmark NASDAQ sample medians for SAFE and DAF. To determine the benchmarks for the
time period 1984–89, we calculate the mean standardized absolute forecast error (equation 2 in the text) and
the mean relative dispersion of analysts’ forecasts (equation 4 in the text) for all NASDAQ firms with forecast
and financial data available on the COMPUSTAT and I/B/E/S tapes with an annual earning announcement
date on COMPUSTAT or DJNR (known 10-K filing date). Based on the specified criteria, the NASDAQ sample
medians for SAFE and DAF are based on 614 firms and the computed cross-sectional medians are 0.68 and
0.11. We then use these NASDAQ medians to assign an earnings predictability score to each firm event
(2,941) in our sample.
c
Firms classified in the highest predictability group (1) have both the average absolute forecast error
and average dispersion over the period 1984–89 less than the respective NASDAQ sample medians for the
same metrics.
d
Firms classified in the medium predictability group (2) have average absolute forecast error less than
the NASDAQ sample median for SAFE but average dispersion greater than the NASDAQ sample median
for DAF over the period 1984–89 or average absolute forecast error greater than the NASDAQ sample
median for SAFE but average dispersion less than the NASDAQ sample median for DAF over the period
1984–89.
e
Firms classified in the lowest predictability group (3) have both the average absolute forecast error and
average dispersion over the period 1984–89 greater than the respective NASDAQ sample medians for the
same metrics.
In panel A of table 2, we present the frequency and the median of both the
standardized absolute forecast error (SAFEi ) and the dispersion of analysts’
forecasts (DAFi ) for each category of earnings predictability based on our
sample of 2,941 firm events. To classify the firm events into earnings predictability groups, we estimate also a standardized absolute forecast error
and a dispersion of analysts’ forecasts for all firms on NASDAQ with available
financial and forecast data (614 firms). Our NASDAQ sample medians are
0.68 and 0.11 for SAFEi and DAFi . Using a Median Chi-Square (KruskalWallis) test, we reject the null hypothesis of equality of the population
574
J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
medians across earnings predictability groups for SAFEi and DAFi at twotailed p < 0.01 in both cases.
We examine the association between our earnings predictability measures and the number of prior accounting changes and earnings variability.
Variability of earnings per share was calculated as the standard deviation
of the basic primary earnings per share—excluding extra-ordinary items
(COMPUSTAT quarterly item #19) for each available quarter for each firm
i for the years y = 1980 to 1989. The number of accounting policy changes
for each firm i is the frequency of such changes over the ten year period
1980 to 1989 and was obtained from COMPUSTAT(COMPUSTAT annual
footnote file code AC) and verified with the firm’s 10-K report. This investigation follows from Elliott and Philbrick [1990] who document that
analysts’ annual earnings forecasts are more dispersed and less accurate in
the year of an accounting policy change.
Using our classification system, we find that both the mean number of
accounting policy changes and the variability of earnings per share are lower
for our high earnings predictability firms than for the low predictability
firms. An analysis of variance (ANOVA) F -test of mean difference across the
three groupings for the number of accounting policy changes and variability
of primary earnings per share indicated in panel B of table 2 was significant
at the 0.01 and 0.00 level, respectively. In addition, the mean difference
t -test comparing firms with high predictability of earnings (group 1) to
low predictability firms (group 3) is significant at the 0.01 level for both the
accounting policy change variable and the variability of primary earnings per
TABLE 2
Classification of Sample Firm Events into Earnings Predictability Groups a
Panel A: Grouping Based on Analysts’ Forecast Errors and Dispersion Metrics
Earnings
Predictability
Group
Firm
Events
High: (Score = 1)
850
Medium:
(Score = 2)
967
Low: (Score = 3)
NASDAQ
sample firms
1,124
614
Decision Rule
Forecast error and
dispersion less than
NASDAQ sample
medians
Forecast error less than
NASDAQ sample median
but dispersion greater
than NASDAQ sample
median, or forecast error
greater than NASDAQ
sample median but
dispersion less than
NASDAQ sample median
Forecast error and
dispersion greater than
NASDAQ sample median
Median
Standardized
Absolute Forecast
Error
Dispersion
of Analysts’
Forecast Error
0.21
0.03
0.52
0.05
1.14
0.20
0.68
0.11
EARNINGS PREDICTABILITY
T A B L E 2—Continued
575
J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
576
TABLE 3
Univariate t-Tests and Regression Results Associated with Differential Adverse Selection Costs Based
on Earnings Predictability Groups For 1984 to 1989
Panel A: Behavior of Abnormal Adverse Selection Cost and Trading Volume Around the
Quarterly Earnings Announcements (QEA) Segmented by Earnings Predictability
(High or Low)
Abnormal adverse selection costa
Dayc
−3
−2
−1
0
1
2
3
Abnormal volumeb
High (n = 850)
Low (n = 1,124)
High (n = 850)
Low (n = 1,124)
Mean
(s.d.)e t-Statisticd
0.028
1.54
(0.018)
−0.024
−1.38
(0.018)
0.030
1.64
(0.018)
0.028
1.56
(0.018)
−0.025
−1.38
(0.018)
−0.018
−0.98
(0.018)
0.016
0.87
(0.018)
Mean
(s.d.) t-Statistic
−0.037 −3.67∗∗
(0.009)
0.019
1.85
(0.009)
0.065
6.42∗∗
(0.009)
0.029
2.87∗∗
(0.009)
−0.017 −1.65
(0.009)
−0.015 −1.45
(0.009)
−0.048 −4.78∗
(0.009)
Mean
(s.d.)
t-Statistic
−0.186 −2.71∗∗
(0.069)
−0.036 −0.52
(0.069)
0.261
3.81∗∗
(0.069)
0.341
4.97∗∗
(0.069)
0.105
1.53
(0.069)
0.169
2.47∗
(0.069)
0.027
0.40
(0.069)
Mean
(s.d.)
t-Statistic
−0.133 −0.96
(0.140)
−0.109 −0.78
(0.140)
0.481
3.45∗∗
(0.140)
0.727
5.14∗∗
(0.140)
0.574
4.11∗∗
(0.140)
0.252
1.81
(0.140)
0.184
1.32
(0.140)
Panel B: Regression of the Cumulative Adverse Selection Cost of the Spread Surrounding
Quarterly Earnings Announcements on Earnings Predictability Indicators and Other Market
Related Factors
Model: CSUi = α1 + α2 (QE 2)i + α3 (QE 3)i + α4 (UE)i + α5 (LAG)i + α6 (MM)i + α7 (SIZE)i
+ α8 (CAR)i + α9 (CVOL)i + τi
Variablef
Expected Sign
Estimated Coefficient
t-Statistic
Intercept
QE 2i
QE 3i
UEi
LAGi
MMi
CARi
SIZEi
CVOLi
−
+
+
+
+
−
+
−
+
−0.025
0.005
0.017
0.300
0.042
0.034
0.000
0.016
0.072
−1.417
1.544
2.657∗
1.471
0.047
2.367∗
3.675∗∗
1.481
3.395∗∗
Number of Observations
F -Statistic
Prob Value
Adjusted R2
2,941
4.643
0.001
0.170
a
In panel A, we define the abnormal adverse selection cost of the spread for a firm i as the difference
between the actual percentage spread and the expected spread in the absence of informed trading. The actual
spread is defined as the logarithm of the difference between the ask price for firm i and the bid price for firm
i deflated by the stock price for firm i (defined as the average of the bid and ask prices) at the close of trading
day t. The estimated spread is based on a log-linear simultaneous equation model because of simultaneity
between spread and volume (Glosten and Harris [1988] and Hegde and Miller [1989]). We estimate the
model based on our sample of 2,941 quarterly earnings announcements from 1984–89. At each quarterly
announcement date, we estimate equations (7a) and (7b) in the text separately for each firm observation
over the estimation period (day −146 to day −11) using Three Stage Least Squares. We use equation (7a)
to estimate the expected spread on each trading day in the analysis period (day −3 to day +3) inclusive of
the earnings announcement date. The estimation equation (7a) is based on the reduced form coefficients
from the structural equations and the values of the exogenous variables to obtain the expected spread.
EARNINGS PREDICTABILITY
577
b
We calculate the mean daily percentage of firm i’s shares traded in the estimation period (day −146
to day −11). Abnormal volume is then defined as the difference between the actual percentage of shares
traded on trading day t and the mean pre-announcement period volume.
c
This represents the trading day relative to the quarterly earnings announcement.
d
For the high and low earnings predictability firms in our sample associated with the tests over the
analysis period (day −3 to day +3), we use the Brown and Warner [1985, page 7] t-Statistic. The t-Statistic
is the ratio of the cross-sectional mean abnormal adverse selection cost on any event day t to the estimation
period time series standard deviation.
t-Statistic =
AAC t
sˆ AAC t
where t represents trading day −146 to trading day −11, AAC t is the average abnormal adverse selection
cost over the 2,941 firm events on trading day t and sˆ AAC t is the standard deviation of the average abnormal
adverse selection cost estimated over the estimation period. To correct for the effect of first-order autocorrelation on the mean AAC stream, the Brown and Warner [1985] test statistic was modified to incorporate the
autocorrelation adjustments suggested by Seyhun [1986, page 195]. The sample standard
errors calculated
in the estimation period (day −146 to day −11) are multiplied by a factor equal to σ 2 /(1 − φ1 ) where φ is
2
equal to the first order autocorrelation and σ is equal to the random error associated with the first-order
autoregression function.
e
Standard deviation of the firm events.
f
Definition of variables:
CSUi : the dependent variable, the two-day abnormal adverse selection cost estimates cumulated over
the two-day event window (day −1 to day 0) where day 0 is the quarterly earnings announcement
date obtained from the quarterly COMPUSTAT tape or DJNR.
QE 2i : an indicator variable set to 1 if the firm observation is in the medium earnings predictability group
(i.e., with score 2) and 0 otherwise.
QE 3i : an indicator variable set to 1 if the firm observation is in the low earnings predictability group
(i.e., with score 3) and 0 otherwise.
UEi : the logarithm of the absolute value of the difference between the reported primary quarterly
earnings per share (COMPUSTAT quarterly item #19) and the last I/B/E/S consensus analyst
forecast of the firm’s primary earnings per share for the quarter issued prior to the earnings
announcement date.
LAGi : an indicator variable set to 1 if the firm reports later than the expected lag period and 0 otherwise.
The actual reporting lag is the calendar time from the end of the quarterly fiscal period up to
and including the announcement date (Chambers and Penman [1984]). The actual reporting
lag in the previous fiscal period is used as the expected lag in the analysis period.
MMi : the logarithm of the number of market-makers for each trading day in the estimation period (day
−146 to day −11) from the CRSP-NASDAQ tape and the average over the 136 trading days in the
estimation period provides the estimate for each firm event.
SIZEi : the logarithm of the firm’s total assets measured at the beginning of the calendar year prior to
the analysis period.
CARi : the risk-adjusted two-day cumulative abnormal return from trading day −1 to trading day 0,
where trading day 0 is the quarterly earnings announcement date obtained from the quarterly
COMPUSTAT tape or DJNR. Market model parameters necessary to compute CARi are estimated
using daily stock returns and the value-weighted market returns as collected from the CRSP files
over the estimation period (day −146 to day −11).
CVOLi : the two-day cumulative mean adjusted abnormal volume from trading day −1 to trading day
0 where trading day 0 is the quarterly earnings announcement date for firm event i. First we
calculate the mean daily percentage of firm i’s shares traded in the estimation period (day −146
to day −11). Abnormal volume is then defined as the difference between the actual percentage
of firm i’s shares traded on trading day t and the mean pre-announcement period volume.
τi : the normally distributed random error term.
∗ and ∗∗ indicate statistical significance at the 0.05 and 0.01 levels, respectively (two-tailed tests).
cost of the spread and the mean-adjusted abnormal trading volume (computed as the logarithm of volume on trading day t during the analysis period
(day −3 to day +3) less the mean volume computed over the preannouncement estimation period (day −146 to day −11)) for the high and
low earnings predictability firms in our sample.13
13
Given end of the year seasonal return effects, we examine the sensitivity of our findings
(inclusive of all four quarterly announcements) to the inclusion of the fourth quarter by
examining the behavior of the abnormal adverse selection cost and trading volume around
578
J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
Consistent with prior research (e.g., Bamber [1986]), we find a significant
positive increase in trading volume around the announcement of quarterly
earnings for both the high and low earnings predictability groups. There
are, however, no large apparent differences in the abnormal volume we
document between the high and low predictability groups during the twoday event window from day −1 to day 0.
In the post announcement period, the results indicate that the abnormal
increases in the spread are partially reversed on the third day following
the earnings release. There is, however, a marked difference between the
high and low earnings predictability groups with respect to the abnormal
adverse selection cost of the spread. For our sample of 850 high predictability
of earnings events, there is no evidence of any significant change in the
abnormal adverse selection cost of the spread on the trading day of or
the trading day before the earnings announcement. However, the results
associated with the 1,124 events for firms with low predictability of earnings
are strikingly different. The abnormal adverse selection cost of the spread
is significantly increased (at the 0.01 level) on both the day before and the
day of the announcement.14
These Brown and Warner [1985] t-tests adjusted for autocorrelation results provide support for our hypothesis that low predictability of earnings firms have spreads with a higher abnormal adverse selection cost at
the time of the quarterly earnings releases. The increase in the spread
we observe suggests that the increase in volume is not sufficient to offset the increase in the abnormal adverse selection cost. This conclusion
is consistent with our hypothesis that lower earnings predictability increases
the information asymmetry between investors and traders in the capital
markets.
We examine the robustness of our conclusion by using a cumulative adverse selection cost metric in the event period from trading day −1 to trading
day 0. This cumulative analysis allows us to control for other factors such as
firm size, reporting lag, number of market-makers, the earnings surprise,
the fourth quarter earnings announcements segmented by earnings predictability (155 high
earnings predictability firm events and 195 low earnings predictability firm events) in our
standard univariate event study tests. In the current study, the full NASDAQ sample results and
the fourth quarter results (available from authors) yield the same conclusion.
14 The increase in the adverse selection cost (AAC) around an earnings announcement is
statistically and economically significant for the low earnings predictability firms. The estimation period AAC of $1.00 as a percentage of the estimation period mean price (average of the
bid and ask price) of $22.20 is 4.51% for low earnings predictability firms, while the estimation
period average AAC of $.99 as a percentage of the estimation period mean price of $38.19 is
only 2.62% for high earnings predictability firms. The average increase in the ACC for the high
earnings predictability firms is 14 cents on day −1 and 21 cents for day 0. On the other hand,
for the average low earnings predictability firms the average increase in the ACC is 50 cents on
day −1 and 61 cents on day 0, a considerable difference. Our calculations are consistent with
Krinsky and Lee [1996, page 1533], who find that the adverse selection cost for an average
stock with a price of $38.26 increases by 13.20 cents per round trip.
EARNINGS PREDICTABILITY
579
and unexpected volume and return. The results are summarized in table 3,
panel B.15
In examining our control variables, we note that the earnings surprise
coefficient is positive but not significant. The number of market-makers
variable, however, has a significant coefficient opposite in sign to that expected. This finding suggests that as market-maker competition increases,
the adverse selection cost of the spread associated with information asymmetry increases. This result is inconsistent with our theoretical predictions
but supports Christie and Shultz [1994]. Both abnormal volume and return
are highly significant in the predicted direction at the 1 percent level. The
other control variables, reporting lag and firm size, are not significant at
conventional levels.
The estimates of α2 and α3 , the coefficients associated with the earnings
predictability variables, are both positive as expected. Also, the magnitude
of the coefficients increases as the earnings predictability decreases (i.e.,
α2 < α3 ). Finally, the coefficient associated with the lowest predictability
group (group 3) is significant at the 5% level. Consistent with our hypothesis, this result confirms that the adverse selection cost on average increases
significantly around the earnings announcement for lower earnings predictability firms compared to higher earnings predictability firms after controlling for firm specific factors. This occurrence suggests that there are
relatively higher levels of information asymmetry associated with firms with
lower earnings predictability around quarterly earnings announcements.
Our final test examines whether earnings predictability affects the bid-ask
spread at times other than earnings announcement periods. In this analysis,
we examine the total bid-ask spreads for our NASDAQ firms and predict that
firms with more predictable earnings may also have lower bid-ask spreads
in non-announcement periods. We use our group classification of firms to
examine the effect of earnings predictability on the total bid-ask spread
during non-announcement periods.
The results in table 4 indicate a mean percentage spread of 0.016 for
the high predictability group. The low earnings predictability firms have
percentage spreads that are 0.008 higher on average than the high predictability group, the increase being significant at the 0.01 level. This difference represents a 51 percent increase in the spread relative to the high
earnings predictability firms.
The persistence of this difference is illustrated in figure 1 where we plot
the mean percentage spread for each of the 589 trading days for both the
high and low predictability of earnings groups. This figure shows that on
almost every trading day we examined, the low predictability of earnings
15 In comparing the mean-adjusted bid-ask spread metric to the adverse selection cost estimate, we find a non-significant relation between the cumulative mean-adjusted spread, predictability of the earnings signal and the control variables. This suggests that the mean-adjusted
bid-ask spread is not a proxy for adverse selection cost.
580
J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
TABLE 4
OLS Estimation of the Time Series Analysis of the Percentage Bid-ask Spread a from 9/1/88 to
12/31/90, 589 Trading Days (the Non-Announcement Period) Results for High b , Medium c and Low d
Predictability Groups e
Model: PSPRDg,d = α1 + α2 (QE 2g,d ) + α3 (QE 3g,d ) + α4 (Y 90g,d ) + τg,d
Indicator Variablesc
Intercept
QE 2
QE 3
Y 90
Number of Observationsc
F -Statistic
Prob Value
Adjusted R2
Expected Sign
Estimated Coefficient
t-Statistic
+
+
+
0.016
0.010
0.008
0.009
81.36∗∗
33.31∗∗
26.95∗∗
36.67∗∗
1,767
865.15
0.00
0.60
a
Firms are classified into an earnings predictability group based on both the mean standardized absolute
value consensus analysts’ forecast error (SAFE ) and dispersion of forecasts (DAF ) over the period 1984–
1989. SAFE is the absolute value of primary annual earnings per share before discontinued operations, extraordinary items and cumulative effects of an accounting change (COMPUSTAT annual item #58) minus the
first consensus annual forecast dated after the year y −1 10-K filing date, standardized by primary annual
earnings per share defined per the numerator. DAF is dispersion of analysts’ forecasts for the first consensus
annual forecast issued after the year y −1 10-K report filing date using the standard deviation of analysts’
forecasts as reported by I/B/E/S, deflated by the absolute value of the first consensus annual forecast dated
after the year y −1 10-K filing date. The firm classification scheme for the earnings predictability scoring
requires benchmark NASDAQ sample medians for SAFE and DAF. To determine the benchmarks for the time
period 1984–89, we calculate the mean standardized absolute forecast error (equation 2 in the text) and the
mean relative dispersion of analysts’ forecasts (equation 4 in the text) for all NASDAQ firms with forecast and
financial data available on the COMPUSTAT and I/B/E/S tapes with an annual earning announcement
date on COMPUSTAT or DJNR (known 10-K filing date). Based on the specified criteria, the NASDAQ
sample medians for SAFE and DAF are based on 614 firms and the computed cross-sectional medians are
0.68 and 0.11. We then use these NASDAQ medians to assign an earnings predictability score to each firm
event (2,941) in our sample.
b
Firms classified in the highest predictability group (1) have both the average absolute forecast error
and average dispersion over the period 1984 to 1989 less than the respective NASDAQ sample medians for
the same metrics.
c
Firms classified in the medium predictability group (2) have average absolute forecast error less than
the NASDAQ sample median for SAFE but average dispersion greater than the NASDAQ sample median for
DAF over the period 1984–89 or average absolute forecast error greater than the NASDAQ sample median
for SAFE but average dispersion less than the NASDAQ sample median for DAF over the period 1984 to
1989.
d
Firms classified in the lowest predictability group (3) have both the average absolute forecast error and
average dispersion over the period 1984–89 greater than the respective NASDAQ sample medians for the
same metrics.
e
Based on our earnings predictability classification scores (high, medium, and low), three portfolios of
firms were formed on each trading day over a non-announcement period, 9/1/88 to 12/31/90 excluding
earnings announcement dates, 589 trading days. We then computed the portfolio mean percentage spread
(the total bid-ask spread deflated by the average price at the close of trading on trading day d) for each
earning predictability group on each of the 589 trading days in the non-announcement period.
f
Definition of variables:
PSPRDg,d : the mean percentage bid-ask spread, the logarithm of the difference between the ask price for
firm i and the bid price for firm i deflated by the stock price for firm i (defined as the average of the bid
and ask prices) at the close of each trading day for each earnings predictability group.
QE 2g,d : 1 if the firm is in the medium earnings predictability group (i.e., with score 2) and 0 otherwise.
QE 3g,d : 1 if the firm is in the low earnings predictability group (i.e., with score 3) and 0 otherwise.
Y 90g,d : 1 if the trading date is in 1990 and 0 otherwise.
τg,d : the assumed normally distributed random error term.
∗ and ∗∗ indicate statistical significance at the 0.05 and 0.01 levels, respectively (two-tailed tests).
firms has higher average percentage spreads than the high predictability of
earnings firms. These results suggest that earning predictability may have
permanent and substantial effects on the bid-ask spread, and hence the
firm’s cost of equity capital.
EARNINGS PREDICTABILITY
581
FIG. 1.—Based on our earnings predictability classification scores (high and low), three
portfolios of firms were formed on each trading day over a non-announcement period, 9/1/88
to 12/31/90 excluding earnings announcement dates, 589 trading days. We then computed
the portfolio mean percentage spread (the total bid-ask spread deflated by the average price at
the close of trading on day d) for each earning predictability group on each of the 589 trading
days in the non-announcement period.
7. Conclusions
This study examines the bid-ask spreads of 247 National Association of Securities Dealers Automated Quotations (NASDAQ) firms from 1985–90. We
investigate the association between earnings predictability and the behavior
of the adverse selection cost of the bid-ask spread around quarterly earnings
announcements. Consistent with our prediction, we find an increase in the
adverse selection cost of the bid-ask spread on the trading day of and the trading day prior to quarterly earnings announcements for NASDAQ firms with
less predictable earnings. In contrast, we find no evidence of a significant
change in the adverse selection cost of the bid-ask spread around quarterly
earnings announcements of firms with highly predictable earnings. Our
findings persist after controlling for price and volume movements, as well
as other firm specific factors associated with the market response to earnings
announcements.
We also investigate the cost of equity capital implications of our findings
and examine whether earnings predictability affects total bid-ask spreads
in non-announcement periods. We find that firms with relatively less predictable earnings have consistently higher total bid-ask spreads across time
than firms with more predictable earnings. This finding suggests that a firm
with relatively less predictable earnings will have a higher cost of equity capital than a comparable firm with more predictable earnings. Our earnings
predictability metrics, however, do not distinguish between real underlying
volatility differences and accounting volatility differences.
582
J . AFFLECK - GRAVES, C . M . CALLAHAN, AND N . CHIPALKATTI
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