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Anomalous Stock Returns Around Internet Firms' Earnings Announcements
Brett Trueman, M.H. Franco Wong, and Xiao-Jun Zhang
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Citation (published version)
Trueman, B., Wong, M. F., & Zhang, X. J. (2003). Anomalous stock returns around internet firms’ earnings announcements. Journal of Accounting and Economics, 34(1-3), 249-271.
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ANOMALOUS STOCK RETURNS AROUND INTERNET FIRMS’EARNINGS ANNOUNCEMENTS
Brett TruemanDonald and Ruth Seiler Professor of Public Accounting
M.H. Franco WongAssistant Professor of Accounting
Xiao-Jun ZhangAssistant Professor of Accounting
Haas School of BusinessUniversity of California, Berkeley
Berkeley, CA 94720
April, 2001
We thank Richard Dietrich, Andrew Leone, Rich Lyons, Tom Lys, Mark Rubinstein, JacobSagi, Richard Stanton, Siew Hong Teoh, Jacob Thomas, Sheridan Titman, Charles Wasley, RossWatts, seminar participants at Ohio State University and the University of Rochester, ananonymous referee, and, most especially, Brad Barber for their helpful comments. We alsothank the Center for Financial Reporting and Management for providing financial support. Wegratefully acknowledge the contribution of I/B/E/S International Inc. for providing earnings andsales forecast data. B. Baik, G. Jiang, D. Li, M. Luo, A. Sribunnak, and Y. Zang provided ableresearch assistance.
Abstract
This paper presents evidence of persistent anomalies in internet firms’ stock returns
surrounding their quarterly earnings announcements. There is a general runup in prices in the
days prior to the earnings announcement, which extends through the market opening on the day
subsequent to the release. This is followed by a price reversal lasting for several days. The
magnitude of the market-adjusted returns associated with these price movements exceeds 11
percent over a 10-day period. There is little evidence to suggest that these returns can be
explained either by the earnings news disclosed or by changes in risk around the earnings
announcements. Additional analyses suggest that these return patterns are driven, at least in
part, by price pressure which exists in the days before internet firms’ earnings announcements.
A trading strategy designed to exploit these price patterns would have generated a daily return of
more than 1 percent over the sample period.
1Two prior studies examining the short-term price movements around earnings announcements are Chari,Jagannathan, and Ofer (1988), covering the 1976-1984 period, and Ball and Kothari (1991), covering the years 1980-1988. In contrast to our sample of almost-exclusively Nasdaq stocks, theirs consists solely of NYSE and AMEXcompanies. There are two primary differences between their findings and ours. First, while the stocks in theirsamples do increase in price prior to earnings announcements, there is no consistent price movement (either positive ornegative) afterwards. Second, the magnitude of the pre-announcement returns they found is very small in comparisonto what we document (less than one-tenth in size). Furthermore, those returns are significant only for the day beforeand day of the earnings announcement.
1
ANOMALOUS STOCK RETURNS AROUND INTERNET FIRMS’EARNINGS ANNOUNCEMENTS
1. Introduction
This paper presents evidence of persistent predictable patterns to the stock returns of
internet firms around their quarterly earnings announcements. We find that the shares of these
firms generally run up in price in the few days preceding their quarterly disclosures. This runup
extends through the opening on the day subsequent to the earnings release and is followed by a
price reversal lasting for several days.
Over the period from January 1998 through August 2000 we show that purchasing shares
five days before a firm’s earnings announcement and selling them at the open on the day
immediately after the release would have yielded an average market-adjusted return of 4.9
percent. The short sale of shares at that time, followed by short-covering four days later, would
have earned 6.4 percent. This return pattern prevails in virtually all of the eleven quarters of our
sample period, as well as during both up and down markets.
The price reversal which takes place within the trading day after the earnings release is
quite remarkable. While the average market-adjusted return from the previous day’s close to the
open is a positive 1.6 percent, the average abnormal return from the open until the close that day
is -3.1 percent, almost twice as large in size.1
2Increases in stock return volatility in the period before earnings announcements has been documented byBeaver (1968) and Ball and Kothari (1991), among others.
2
We find little evidence to suggest that these documented returns are related to the
accounting information disclosed in the quarterly earnings announcements. In particular,
neither the reported earnings surprise nor the reported revenue surprise is significantly
associated with the pre-announcement price runup, thereby eliminating information leakage as a
possible explanation for the positive returns. The post-announcement price reversal is also not
significantly related to unexpected earnings or revenues, as would be expected if these returns
reflected either a delayed response or a market overreaction to the news contained in the
earnings report.
The pre-announcement returns are also too large to be explained by increases in risk
around earnings releases.2 We formally show this by allowing for daily changes in betas in the
period before earnings announcements (as in Ball and Kothari (1991)), and confirm that the
risk-adjusted abnormal returns remain significantly positive. We similarly calculate daily betas
for the period after earnings announcements, and show that the risk-adjusted abnormal returns
are significantly negative during that period. This latter result is not surprising, though, given
our finding that post-announcement raw returns are also negative; this could only be explained
by risk if internet stocks were negatively correlated with the market (which they are not).
Additional analyses suggest the possibility that these returns are being driven, at least in
part, by price pressure, whereby an unjustifiably high level of investor optimism and share
demand (relative to a firm’s earnings prospects) is boosting prices in the days before an earnings
announcement, and an abating of that demand is causing a subsequent price reversal. Several of
3Price pressure has been suggested as an explanation for anomalous price patterns in a number of differentsettings. Barber and Loeffler (1993) and Liang (1999), for example, show that stocks recommended in the Wall StreetJournal’s “Dartboard” column enjoy an initial two-day runup, which is reversed, for the most part, within the nextseveral weeks. The authors conclude that the runup is at least partially the result of temporary price pressurestemming from the buying activity of naive investors. Harris and Gurel (1986) and Lamoureux and Wansley (1987)similarly find that an initial positive price response to the announcement of a firm’s inclusion in the S&P 500 Index islater reversed. They conclude that the positive initial return is due to price pressure resulting from the buy orders ofinstitutional investors.
3
our test results are consistent with this notion. First, calculating the abnormal order imbalance
statistic (as implemented by Lee (1992)) reveals an unusually large number of buyer-initiated
trades relative to seller-initiated trades in the five days prior to a firm’s earnings announcement,
an imbalance which not only disappears, but reverses after earnings are released. Second, this
abnormal order imbalance is positively correlated with the abnormal returns generated during
the week before the earnings release. Third, the price reversal subsequent to the earnings
announcement results in a cumulative abnormal return over our entire event window that is
insignificantly different from zero.3
While our results, taken as a whole, suggest a market inefficiency, there may be other, as
of yet unexplored, explanations for these abnormal returns which are consistent with market
efficiency. Given the magnitude of the documented returns, additional research aimed at
understanding their origin may well be worthwhile.
The plan of this paper is as follows. In Section 2 we describe our sample selection
criteria and research design, and provide descriptive statistics. This is followed in Section 3 by
an examination of the abnormal returns surrounding internet firms’ earnings announcements.
Potential explanations for the observed price patterns are analyzed in Section 4. Section 5
compares the returns we find for our sample of internet stocks with those of a broader sample of
technology shares and a sample of non-technology stocks. In Section 6 we estimate the
4These indices are advertising, consultants & designers, content & communities, e-commerce enablers, e-tailers, financial services, ISP/access, internet services, performance software, search & portal, security, and speed &bandwidth.
5The vast majority of these firms were delisted because they merged with or were acquired by othercompanies.
6We do not include earnings pre-announcements in our sample.
4
historical abnormal returns an investor would have earned over our sample period by following a
trading strategy based on the price patterns we document. A summary section concludes the
paper.
2. The Data, Research Design, and Descriptive Statistics
2.1. The Data
Our initial sample consists of the complete list of the component firms of internet.com’s
twelve internet indices as of June 2000, as reported on its Wall Street Research Net web site.4 In
order to minimize the effect of survivorship bias, we add to this set of firms all delisted
companies which had been public at some point between January 1998 and June 2000 and
which we classify as part of the internet industry.5 This augmented list is comprised of 403
firms.
For each firm-quarter whose earnings announcement falls between January 1998 and
August 2000 we collect (1) the date and time of the formal announcement of that quarter’s
earnings6, (2) the daily opening and closing stock prices for each of the 25 trading days prior to
and after that earnings announcement (or for as long a period as possible, if the firm was not
publicly traded during that entire time period), (3) bid and ask prices at the market opening and
closing on each of those days, and, if available, (4) earnings and revenue surprises, calculated
7While earnings forecasts are available for our entire sample period, most of the available sales forecasts arefor the third quarter of 1998 or later.
8Another high-tech stock index with historical open prices is TheStreet.com Internet index. However, itsprices only go back to January 5, 1999. We repeated our main tests (restricted to the 1999-2000 period) using thisindex, and obtained results similar to those reported below.
5
using analysts’ one-quarter ahead earnings and revenue forecasts (as described in more detail
below).7 Dow Jones Interactive is the source for the date and time of the earnings
announcements. Since several of our analyses involve the close-to-open and open-to-close stock
returns on the trading day immediately following the earnings release, 77 announcements (3.8
percent of the sample) for which no time is given (so that we cannot precisely identify the first
trading day post-announcement) and 81 announcements (4.0 percent of the sample) made during
regular trading hours (so that the open-to-close return on the earnings announcement day
includes both pre- and post-announcement price changes) are deleted from the sample. This
reduces our sample to 393 firms spanning 1,875 firm-quarters.
The Trade and Quotation (TAQ) database is the source of daily opening and closing
prices, as well as bids and asks. To calculate abnormal returns we require a high-tech stock
index with both opening and closing prices from the beginning of 1998. Only the Nasdaq
Composite Index was found to satisfy these criteria and so is used in all of our analyses of
abnormal returns.8
The I/B/E/S database provides the individual analysts’ earnings and sales forecasts.
Using the I/B/E/S data, consensus analyst forecasts are computed by averaging the three most
recent individual forecasts for the quarter. If there are fewer than three forecasts in the quarter,
then all of the available one-quarter ahead forecasts are used. I/B/E/S is also the source for
actual earnings per share, as the database adjusts eps to make it comparable to the number that is
9Total actual and forecasted revenues are converted to per-share amounts by dividing them by the weightedaverage (basic) number of shares outstanding for the quarter. This number is taken from Compustat (or companypress releases for the quarters in which Compustat data are not available).
6
forecasted by analysts. Actual revenues are obtained from Compustat or, for the most recent
quarters in which Compustat data are not yet available, the company’s press releases. I/B/E/S is
not used to obtain actual sales since the numbers are sometimes either missing or inconsistent
with the revenues reported by the firms.9 Earnings (revenue) surprise is defined as actual
earnings (revenue) less the consensus earnings (revenue) forecast.
2.2. Research Design
For each firm-quarter we calculate the daily (close-to-close) raw return on each trading
day t, t 0 {-25,...-1,1,...,25}, where t = -1 (t = 1) is the trading day just prior to (following) the
quarter’s earnings announcement. (We choose not to denote the earnings announcement date as
day 0 since all of our announcements occur outside of regular trading hours. Under our
convention, these announcements occur between the close of day -1 and the open of day 1.) We
also decompose the raw return on trading day t = 1 into a close-to-open return (from the close on
day -1 to the open on day 1) and an open-to-close return (from the open on day 1 to the close on
that day).
We next calculate the abnormal return corresponding to each of these raw returns. Since
the event window for most of our tests is very short (10 days in duration), the metric used for
abnormal returns is likely to have little effect on our inferences (see Fama (1998)). For most of
the analysis we use market-adjusted returns to measure abnormal returns. The close-to-close
abnormal return for an individual stock is then computed by subtracting the close-to-close return
10On the other hand, Fama (1998) argues that the model of market equilibrium employed should determinethe theoretically appropriate measure to use for cumulating returns. In our analysis, CAR’s and BHAR’s yield similarinferences.
7
on the Nasdaq Composite Index from the stock’s raw return. The close-to-open and open-to-
close abnormal returns are similarly defined. The average abnormal return on a given event day
is then the equal-weighted average of the individual stocks’ abnormal returns. The t-statistic for
each day’s average abnormal return is calculated using the corresponding cross-sectional
standard error.
We also report the results of cumulating the daily abnormal returns we find. Two
commonly employed cumulative return metrics are the cumulative average abnormal return
(CAR), which is the sum of the average daily abnormal returns, and the average buy-and-hold
abnormal return (BHAR). The average BHAR is calculated by compounding the raw return for
each security i over a specified event period, subtracting the compound return on the market
index over this period, and then averaging the excess returns over all securities. While these two
methods are not likely to diverge much over short windows, we choose to report the average
BHAR in our tables. Barber and Lyon (1997) suggest that this is the conceptually more
appropriate measure to use. They also find that it produces test statistics which are negatively
biased, making it less likely that we will find significant BHAR’s over our event window.10 The
t-statistic for the average BHAR over a given window is calculated based on the corresponding
cross-sectional standard error.
2.3. Descriptive Statistics
Table 1 presents descriptive statistics on the 393 companies in our final sample.
8
Reflective of high valuations relative to sales and earnings, the average firm market value is a
large $5.1 billion (the median is $579 million), while the average market/book ratio is 15.46 (the
median is 7.21). In contrast, average quarterly revenues is only $65 million (median of $118
million), while average quarterly earnings is a negative $1.32 million (median of -$3.21
million).
The average daily (close-to-close) raw return for these firms during the 50 days
surrounding their earnings announcements is 0.03 percent. The average close-to-open return is a
larger 0.85 percent, while the average open-to-close return is -0.79 percent. These greater
intraday returns are likely due to these stocks’ tendency to close at their bid price and open at
their ask price. The average bid-ask spread at the open of trading is 1.34 percent of the
midpoint of the spread, while it is 1.29 percent of the midpoint at the close of trading. These
spreads are somewhat larger than the average bid-ask spreads (of about 1 percent) reported by
Carhart (1997) and Barber and Odean (2000). This is not surprising, given that our sample is
comprised mostly of Nasdaq stocks, which typically have higher spreads. Section 4.1 discusses
the impact of the bid-ask spread on our calculated returns.
Abnormal returns display a similar pattern to the raw returns. Over the 50 days
surrounding our sample firms’ earnings announcements, the close-to-close daily abnormal return
averages -0.15 percent. The average close-to-open abnormal return is a larger 0.56 percent,
while the average open-to-close abnormal return is a similarly large -0.68 percent.
3. Stock Returns Around Earnings Announcements
Table 2, column 2, presents the average daily abnormal (market-adjusted) returns for
11Since the BHAR’s span multiple days, individual observations are cross-sectionally correlated. Therefore,the t-statistics we report for the BHAR’s should be interpreted with caution.
9
event days t 0 {-25,...-1,1,...,25}, while column 3 reports the corresponding t-statistics. With
just one exception, the returns for days -25 through -5 are insignificant. Beginning on day -4 the
average abnormal return becomes significant and positive, and remains so through day -1, where
it equals 1.4 percent. The average abnormal return then switches sign and becomes significantly
negative in each of the five days after the earnings announcement. Interestingly, while the close-
to-close day 1 average abnormal return is a significant -1.6 percent, the close-to-open average
abnormal return that day is significantly positive, at 1.6 percent, reflecting a continuation of the
upward price movement of the prior few days. The open-to-close abnormal return on day 1 is a
significant -3.1 percent. The average daily abnormal returns for days 6 through 25 are, with one
exception, once again insignificantly different from zero.
Column 4 of Table 2 reports the average buy-and-hold abnormal return (BHAR)
cumulated from the close on day -26. The corresponding t-statistics appear in column 5.11
Figure 1 depicts these returns graphically. Through day -5 the average BHAR fluctuates
between marginal significance and marginal insignificance (and remains below 2 percent in
magnitude), showing no discernible trend over time. The average BHAR increases steadily from
day -4, though, reaching a maximum of 5.3 percent at the open on day 1. From that point it
begins to decrease, relatively rapidly at first, reaching a minimum of -2.3 percent on day 13.
The average BHAR then turns back up, and from day 14 through day 25 is insignificantly
different from zero; on day 25 the average BHAR is only -0.3 percent.
Since virtually all of the significant daily abnormal returns occur within the 5 days before
12The cumulative average abnormal returns for these two time periods, estimated from the CAPM, are 5.0and -8.0 percent, respectively. (This can be verified by reference to Table 3.)
13Several years ago it would have been difficult to implement the first part of this strategy since earningsannouncement dates were often not known in advance. This is not a significant issue during our sample period, asseveral web sites now provide listings of expected announcement dates. See, for example, Yahoo!Finance(finance.yahoo.com), Silicon Investor (www.siliconinvestor.com) and CBSMarketwatch.com (cbs.marketwatch.com).
14The historical returns generated from implementing this two-part strategy are reported in Section 6.
10
and after the earnings announcement, the bulk of our analysis focuses on this time period. Table
3, panel A, reports that the average buy-and-hold abnormal return from the close on day -6
through the open on day 1 (referred to as the average BHAR-6,1 below) is a significant 4.9
percent. From day 1's open through the close on day 5 the average BHAR (referred to as the
average BHAR1,5 below) is a significant -6.4 percent.12
These findings suggest a potentially profitable two-part trading strategy. The first part
entails buying an internet stock a week prior to its earnings release and selling it at the open on
the trading day immediately subsequent to the announcement.13 The second part involves short
selling the stock at the open that day and covering the position at the close on day 5. The
historical BHAR earned over this 10-day window averages a remarkably large 11.3 percent for
the individual stocks in our sample.14
Since many firms’ earnings announcements are clustered on the same calendar date, their
abnormal returns may be positively cross-correlated, leading to upwardly biased test statistics.
As a robustness check on our results, for each calendar date we form an equal-weighted portfolio
of all stocks announcing earnings that day (along the lines of Chari, Jagannathan, and Ofer
(1988)). This portfolio formation process yields 388 separate observations, from which we then
estimate both average abnormal returns and BHAR’s. In untabulated results, we find that the
15See “For Day Traders, An Hour is a Long Wait,” by Suzanne Woolley (Business Week, November 4, 1996)and “Day of Reckoning for Day-Trading Firms?,” by Geoffrey Smith (Business Week, January 18, 1999).
11
average BHAR-6,1 resulting from this process is 6.0 percent (somewhat greater than that
previously reported) while the average BHAR1,5 is -5.5 percent (slightly smaller in magnitude
than previously found). Both of these abnormal returns are significantly different from zero.
The calendar-time clustering of earnings announcements does not appear to be responsible for
the abnormal returns we observe.
4. Potential Explanations for the Observed Price Patterns
4.1. Possible Misestimation of Transactions Prices
Implicit in our return calculations thus far is the assumption that all buy orders can be
executed at a stock’s closing price while all sell orders can be filled at its opening price. In
reality we would expect most end-of-day buy orders to fill at the closing ask price and
beginning-of-day sell orders to execute at the opening bid price. To the extent that the recorded
closing price reflects a sell order, executed at the bid, and the recorded opening price a buy
order, executed at the ask, our analysis will overestimate abnormal returns. For internet stocks,
in particular, it is likely that closing prices reflect sell orders and opening prices buy orders,
since day traders have come to dominate the activity in these stocks. These traders typically sell
out their positions at the close of the trading day and enter into new ones at the start of trading
the following day.15
To adjust the BHAR calculations for the bid-ask spread, we assume that all beginning-
of-day sell orders are executed at the opening bid price while all end-of-day buy orders are filled
at the closing ask price. As reported in the second row of Table 3, panel A, the average
12
abnormal return to a strategy of buying these firms’ stocks at the closing ask on day -6 and
selling them at the opening bid on day 1 is a significant 3.7 percent. The average abnormal
return to a strategy of short-selling at the opening bid on day 1 and covering at the closing ask
on day 5 is a significant 5.5 percent. Each of these returns is approximately one percentage
point less than the corresponding unadjusted return. In unreported results, we also find that the
adjusted close-to-open abnormal return on day 1 is 0.6 percent, while the adjusted open-to-close
return is -2.1 percent. Both of these are significantly different from zero, but are again about
one percentage point less than the corresponding unadjusted returns. Even after deducting the
bid-ask spread, the average BHAR’s remain economically large.
4.2. A Risk-Based Explanation
In this subsection we examine the extent to which the returns around internet firms’
earnings announcements can be explained by the risk inherent in these stocks. We first
document the frequency with which the average BHAR-6,1 (BHAR1,5) is negative (positive) over
the sample period. This provides a broad measure of the riskiness of the observed returns. As
reported in Table 3, panel B, the return pattern is remarkably robust over time, and is present in
virtually all quarters of our sample period. This period includes 9 up-quarters (as measured by
the change in the Nasdaq Composite Index) and 2 down-quarters, the third quarter of 1998 and
the second quarter of 2000. The average BHAR-6,1 is greater than zero in all but one of the 11
sample quarters, and is significantly positive in 8 quarters. The greatest abnormal return is 11.0
percent, occurring in the fourth quarter of 1998. The average BHAR1,5 is negative for all of the
sample quarters, and is significant in all but two. The largest negative abnormal return, -9.6
16Ball and Kothari (1991) use a similar methodology in their analysis of risk changes and abnormal returnsaround earnings announcements.
13
(1)
percent, occurs in the first quarter of 2000. While our sample period spans only 11 quarters, the
consistency of both the pre- and post-announcement returns suggests that risk cannot fully
account for the observed price pattern.
Next, we assess the impact of risk by employing the Ibbotson (1975) methodology,
which allows for risk changes in the estimation of abnormal returns. This analysis is motivated
by prior studies which document increases in risk around the time of earnings announcements.
We estimate the following cross-sectional regression, separately for each event day:16
where:
Ritc = the daily return on security i for calendar date c and event day t,
Rfc = the daily risk-free rate of return on calendar date c for treasury bills having one month
until maturity,
Rmc = the return on the Nasdaq Composite Index for calendar date c,
"t = the estimated CAPM intercept (Jensen's alpha) for event day t,
$t = the estimated market beta for event day t, and
,itc = the regression error term.
These regressions yield estimates of systematic risk, $t, and abnormal return, "t, for each event
day t.
Table 4, columns 2 and 4, present the estimated "t’s and $t’s, respectively, for days -5 to
17Untabulated results show the same to be true for days -25 to -6 and days 6 to 25.
14
5. Columns 3 and 5 report the corresponding t-statistics, which are based on White’s (1980)
standard error. The "t’s are significantly positive, as well as significantly negative, on the same
event days as are the market-adjusted returns (reported in column 2 of Table 2).17 Moreover,
these two metrics never differ by more than 0.2 percentage points. The $t’s are significantly
greater than one during the pre-announcement period, consistent with prior findings of increased
stock return volatility during this time, and are highest between the close on day -1 and the open
on day 1. The $t’s decrease after earnings are released, and are no longer significantly different
from one. The stock return pattern documented for the market-adjusted returns clearly remains
intact when estimating abnormal returns in this alternative manner, which accounts for changes
in risk.
4.3. An Information-Based Explanation
We now consider whether the observed stock return patterns around internet firms’
earnings releases can be explained by the information reflected in those announcements. Ex-
ante, it is doubtful that an information-based story could fully explain these price movements,
however. To do so would require that generally favorable news leak out in advance of the
earnings announcements, and that investors overreact to the leaks, necessitating a price reversal
after the actual earnings are announced.
We partition the 10-day buy-and-hold abnormal return surrounding each earnings
announcement into four components: (i) the buy-and-hold abnormal return from the close on day
-6 through the close on day -1, (ii) the close-to-open return on day 1, (iii) the open-to-close
18See Hand (2000a), Rajgopal, Kotha, and Venkatachalam (2000), Demers and Lev (2001), and Trueman,Wong, and Zhang (2000).
15
return on day 1, and (iv) the buy-and-hold abnormal return from the close on day 1 through the
close on day 5. Each return component is then separately regressed on the earnings and revenue
surprises in the earnings announcement. Earnings surprise is defined as the difference between
actual earnings per share and the consensus analyst earnings forecast, scaled by the beginning
price of the return window. Revenue surprise is defined as the difference between actual
revenues per share and the consensus analyst revenue forecast, again scaled by the beginning
price of the return window. Revenue surprise is chosen as an independent variable because of
recent findings that internet firm stock prices are positively related to their reported revenue.18
There are 856 (470) firm-quarters for which earnings (revenue) forecasts are available and for
which earnings (revenue) surprises can be calculated. In order to minimize the influence of
outliers, we treat as missing any earnings (revenue) forecast which deviates more than 50
percent from actual earnings (revenues). Nineteen (five) percent of the earnings (revenue)
forecasts exceed these bounds. Including these extreme surprises in the sample does not
qualitatively change our results.
Table 5 presents the regression results. In the pre-announcement period the coefficient
on earnings surprise is an insignificant 0.21, while the coefficient on revenue surprise is an
insignificant 0.305. Apparently, the observed pre-announcement price runup is not due to the
leakage of favorable news in advance of the earnings release. For the close-to-open period on
day 1 the coefficient on earnings surprise is 0.374, which is marginally insignificant, while the
coefficient on revenue surprise is an insignificant 0.190. Furthermore, neither the earnings
19At first glance the insignificance of the revenue surprise, especially on day 1, appears to be at odds withprior studies which find the prices of internet stocks to be significantly and positively related to revenues. It suggeststhat analysts’ revenue forecasts may not adequately reflect the market’s expectation of revenues, a conjecturesupported by the findings of Trueman, Wong, and Zhang (2001) for portals, content/community firms, and e-tailers.
20In addition to the previously mentioned articles discussing the day trading phenomenon, see “Day Trading:It’s a Brutal World,” by Daniel Kadlec (Time Magazine, August 9, 1999) and “Market on a High Wire: MomentumPlayers Ignore the ‘Tomorrow Factor’,” by Rebecca Buckman (The Wall Street Journal, January 18, 2000).
21As few as 10 to 15 percent of these firms’ shares are sold in the initial public offerings. In addition, theshares held by insiders cannot be sold during the share lockup period, which normally lasts for at least six months.(See the discussion later in this subsection.)
16
surprise nor the revenue surprise is significantly related to the open-to-close abnormal return that
day (the regression coefficients are 0.322 and 0.287, respectively). The same is true for the
abnormal return over the next four days, as well, where the coefficient on earnings surprise is an
insignificant -0.458 and the coefficient on revenue surprise is an insignificant 0.511.19 The post-
announcement price decline appears not to be due to either a delayed response or a market
overreaction to the news contained in the earnings report.
4.4. A Price Pressure Explanation
An alternative potential explanation we now explore is that of price pressure, whereby an
unjustifiably high level of investor optimism and share demand (relative to a firm’s earnings
prospects) boosts prices in the days before an earnings announcement, and an abating of that
demand causes a subsequent price reversal. By causing prices to deviate from fundamental
values, price pressure reflects a form of market inefficiency. The possibility that price pressure
might be driving returns in our setting is suggested by the rather unique conditions surrounding
the trading of internet stocks: a relatively large demand for shares from short-term retail
investors, especially day and momentum traders,20 and a small supply of firm shares available
for trading in the marketplace.21
22Including trades executed when the market is closed would have required us to identify, for each earningsrelease, those trades taking place before the after-hours announcement and those taking place afterwards. We do notexpect that our results would change if these trades were included, especially given the relatively small level of after-hours trading volume.
17
(2)
If price pressure is at least partially responsible for these stock return patterns, then we
should observe an abnormally high number of buyer-initiated relative to seller-initiated trades in
the days before an earnings announcement as well as a positive association between the pre-
announcement price runup and the magnitude of the abnormal order imbalance. During the
post-announcement period the excess of buyer-initiated trades should disappear. Finally, the
pre-announcement BHAR should be completely reversed in the days after the earnings release.
In the remainder of this subsection we examine the extent to which these predictions are
consistent with our data.
We employ the tick test to determine the daily number of buyer-initiated and seller-
initiated trades. A trade is considered to be buyer-initiated if either the immediately preceding
trade, as recorded on the TAQ database, is at a lower price or, if at the same price, the last non-
zero price change is positive. Similarly, a trade is considered to be seller-initiated if either the
immediately preceding trade is at a higher price or, if at the same price, the last non-zero price
change is negative. We include trades made during normal market hours (9:30 a.m. until 4:00
p.m. Eastern time), but exclude those made either before market opening or after market
closing.22 Following Lee (1992) the order imbalance on event day t surrounding the quarter m
earnings announcement of firm i, denoted by OIitm, is defined as:
18
(3)
where:
NBUYitm = number of buyer-initiated trades for firm i on event day t in quarter m,
NSELLitm = number of seller-initiated trades for firm i on event day t in quarter m, and
NTRDim = number of trades per day, averaged over the days {-25,...,-11,11,...,25} surrounding
the quarter m earnings announcement of firm i.
For each earnings announcement in our sample, the order imbalance is calculated for each of the
event days from t = -5 to t = 5. The difference between the number of buyer-initiated and seller-
initiated trades each day is normalized by the average number of daily trades over days t = -25 to
t = -11 and t = 11 to t = 25 associated with that earnings announcement.
The abnormal order imbalance, denoted by AOIitm, is given by:
where the ‘normal’ order imbalance is the average of the order imbalances over the period t = -
25 to t = -11 and t = 11 to t = 25.
Table 6, panel A reports the average order imbalance for event days -5 through 5.
Consistent with a price pressure explanation for the documented returns, the average abnormal
order imbalance is significantly positive for each of the four days before the earnings
announcement, reflecting an abnormally high number of buyer-initiated relative to seller-
initiated trades. Furthermore, as reported in panel B of Table 6, the pre-announcement price
runup, BHAR-6,1, is significantly positively related to this abnormal order imbalance, again
suggesting that price pressure is at least partially driving returns. (The regression coefficient on
19
the abnormal order imbalance is a significant 0.502.) Once earnings are announced, the
imbalance turns significantly negative (see panel A), signifying an abnormally large number of
seller-initiated relative to buyer-initiated trades. Apparently, many traders take advantage of the
temporarily high stock price just after the earnings announcement to either liquidate their
holdings or establish short positions. In fact, traders’ actions subsequent to the earnings release
cause the positive pre-announcement BHAR to be completely reversed within a few weeks (see
Table 2), as a price pressure explanation would predict.
We further examine the extent to which price pressure might be driving our results by
dividing our sample into those firm-quarters whose earnings announcements occur within the
first six months after the initial public offering and those falling outside of that period. We
choose six months as it is the normal share lockup period. During this period insiders (such as
managers, directors, employees, and venture capitalists) are prohibited from selling their shares;
as a result, the share float is a small fraction (often 20 percent or less) of the total number of
shares outstanding. To the extent that price pressure is a driving force behind the stock returns
observed around earnings announcements, we would expect a more pronounced price pattern
during the lockup period than afterwards. There are 618 earnings announcements occurring
within the lockup period and 1,257 announcements post-lockup.
As reported in column 1 of Table 7, the average BHAR-6,1 for the lockup period is a
significant 6.7 percent, while the average BHAR1,5 is a significant -7.4 percent. Additionally,
the close-to-open return on day 1 averages a significant 2.2 percent, while the average open-to-
close return is a significant -3.8 percent. All of the corresponding post-lockup abnormal returns
are smaller, consistent with the price pressure hypothesis, but they are still significant (see
20
column 2 of Table 7). The average BHAR-6,1 for the post-lockup period is 4.1 percent, while the
average BHAR1,5 is -5.9 percent. Further, the close-to-open day 1 abnormal return averages 1.3
percent while the open-to-close abnormal return that day averages -2.8 percent. Untabulated
results reveal that the differences between the returns for the lockup period and the post-lockup
period are significant at the 5 percent level (with the exception of the average BHAR1,5 return
difference, which is significant at the 10 percent level).
4.5. Other Possible Explanations for the Documented Price Patterns
There is some evidence that earnings announcements, in and of themselves, are
informative and elicit certain price reactions (see Chambers and Penman (1984) and Kross and
Schroeder (1984)). Under the assumption that managers release good earnings news earlier than
bad, a firm would be expected to experience negative average stock returns between the end of
its fiscal quarter and the time of its earnings announcement (with the lack of a disclosure being
interpreted by investors as bad news.) When earnings are released, investors would be positively
surprised, and the firm’s stock price should increase, on average. These actions would produce
a ‘v’-shaped price pattern around earnings announcements. This is clearly inconsistent with the
inverted ‘v’ pattern observed in our data.
Another possibility for explaining some of our findings requires the supposition that
internet stocks’ pre-announcement prices incorporate a very small chance of a large earnings
surprise and corresponding post-announcement price jump, and a high likelihood of a small
earnings disappointment and price decline. If this is the case, our sample could be biased in the
sense of capturing none (or a disproportionately small number) of these large positive outcomes.
23Even in the extreme case of a potential upside price move of 1,000 percent, for example, the probability ofits realization would be more than one-half of a percent (assuming, for simplicity, risk neutral pricing, and an efficientmarket). This is not a very low probability, especially given that, with our sample size, about ten of these eventsshould be observed (assuming independence across observations). By way of comparison, the largest post-announcement price movement in our sample is less than 400 percent.
21
This could, at least theoretically, explain the negative post-announcement price change we
observe (although it cannot account for the price runup in advance of the earnings release).
Practically speaking, though, the relatively large post-announcement average price drop of 6.4
percent implies that, at least from investors’ viewpoint, a large upside price movement is
actually not a very low probability event.23
5. Price Patterns Around Non-Internet Firms’ Earnings Announcements
In this section we examine whether the price pattern surrounding internet firms’ earnings
announcements can be detected for either other technology firms or non-technology companies.
With conventional wisdom suggesting that day and momentum traders have been more actively
trading internet stocks than shares of other companies in recent years, we expect that price
pressure (and the resulting price patterns around earnings announcements) would be less in
evidence for other technology firms, and even less prevalent for non-technology companies.
Our technology sample consists of all firms with the “technology” industry classification
on the I/B/E/S detailed earnings forecast database (aside from internet firms). The non-
technology sample is comprised of those firms classified as either “consumer durables”, “basic
industries”, or “capital goods” on I/B/E/S. Given the large number of observations in these two
samples, we do not hand-collect the earnings announcement dates; rather, we rely on the dates as
24We assume that all earnings announcements are made after the close of trading, consistent with theobserved practice of most companies.
25Due to data unavailability we do not consider earnings announcements past June 2000.
22
reported by I/B/E/S.24 Since any inaccuracies in these dates, as well as uncertainty over the
exact timing of these announcements (that is, whether they occur before or after trading hours),
will tend to reduce the magnitude of the observed returns, a comparison with the abnormal
returns for our internet sample must be approached with some caution. With the exception of
relying on the I/B/E/S dates, the methodology used to calculate the average BHAR’s around
earnings announcements is the same as for our internet sample.25
There are 952 firms and 3,521 firm-quarters in the technology sample. As reported in
the first column of Table 8, the average BHAR-6,1 is a significant 3.2 percent for these
companies. This compares to an average abnormal return of 4.9 percent for our internet firm
sample (see the last column of Table 8). The average BHAR1,5 is a significant -1.5 percent,
compared to a -6.4 percent average abnormal return for the internet firms. The close-to-open
abnormal return on day 1 for the technology sample averages 0.5 percent, while the open-to-
close abnormal return averages -0.9 percent; both of these are significant. The corresponding
average abnormal returns for the internet sample are 1.6 and -3.1 percent, respectively. While a
price pattern similar to that documented for our internet firm sample exists for these non-internet
technology companies, it is, as expected, much less pronounced.
The non-technology sample is comprised of 340 firms, spanning 1,075 firm-quarters.
For these firms the average BHAR-6,1 is a significant 0.7 percent (see the second column of Table
26Given the small, but significant, pre-announcement returns documented by Ball and Kothari (1991) andChari, Jagannathan, and Ofer (1988) for a large sample of NYSE and AMEX firms, it is not surprising that our non-technology sample also exhibits significant pre-announcement returns. Unlike those studies, however, we find asignificant post-announcement price decline as well.
23
8), while the average BHAR1,5 is a significant -1.3 percent.26 The close-to-open and open-to-
close average abnormal returns on day 1 are both insignificant (-0.1 and -0.2 percent,
respectively). As we had conjectured ex-ante, the magnitude of these returns is much smaller
than the corresponding returns for both the internet firms and the non-internet technology
companies. Moreover, given the small sizes of both the pre- and post-announcement abnormal
returns, it is likely that they will become insignificant after taking the bid-ask spread into
account.
6. The Returns to a Trading Strategy Around Internet Firms’ Earnings Announcements
The analysis in Section 3 revealed that large and significant average buy-and-hold
abnormal returns (in the range of 1 percent per day) could be generated by purchasing an
internet firm’s shares five days before its earnings announcement and selling them at the open
on the day after the release. Similar returns could be earned by short-selling those shares at that
time and covering the position four days later. These event-time returns, though, do not
necessarily reflect how much an investor could have earned in calendar time. In this section we
estimate the historical return from implementing the two parts of this trading strategy over our
sample period. This analysis explicitly recognizes that on each calendar date the investor would
be holding a portfolio of stocks, whose composition changes over time.
We begin by constructing two portfolios, referred to as the pre-announcement and post-
announcement portfolios. As of any day’s close, the equal-weighted pre-announcement
24
portfolio is comprised of all stocks whose earnings will be announced sometime before the open
five trading days hence. At each day’s open, every stock whose earnings were announced while
the market was closed is dropped from the portfolio. The portfolio is then rebalanced, so that
there is an equal dollar amount invested in all of the remaining stocks. This procedure is
followed for all the trading days within our sample period. As of any day’s open, the equal-
weighted post-announcement portfolio contains all stocks whose earnings were announced
sometime between the close five trading days earlier and the current day’s open. At each day’s
close, any stock whose earnings were announced after the market close five trading days earlier
(and before the next day’s open) is dropped from the portfolio. The portfolio is then rebalanced
so that it is composed of equal dollar amounts of all the remaining stocks. Again, we follow this
procedure for all the trading days in the sample period. For any period of time in which a
portfolio does not contain any stocks, we assume that it is entirely invested in the market index.
Untabulated results reveal that there are 15 (12) stocks on average in the pre-announcement
(post-announcement) portfolio.
The close-to-open (open-to-close) raw return for each portfolio on a given trading day is
the equal-weighted return of each of the individual securities in the portfolio as of the prior
day’s close (that day’s open). The close-to-close return for the trading day is the compounded
portfolio return over the close-to-open and open-to-close periods. We compute two alternative
measures of each portfolio’s daily abnormal return. The first is the market-adjusted return,
calculated by subtracting the close-to-close return on the Nasdaq Composite Index from the
compounded close-to-close raw portfolio return. The second is Jensen’s alpha, computed from
an estimate of the following time-series market model:
27Untabulated results reveal a beta of 1.41 for the pre-announcement portfolio and 1.06 for the post-announcement portfolio. This is consistent with previous results which document an increase in beta prior to earningsannouncements. The average daily market-adjusted return and Jensen’s alpha are almost identical because the dailymarket premium is very small (less than 0.01 percent, on average).
25
(4)
where:
Rpc = the daily return on portfolio p (either the pre-announcement or post-announcement
portfolio) for calendar date c,
Rfc = the daily risk-free rate of return on calendar date c for treasury bills having one month
until maturity,
Rmc = the return on the Nasdaq Composite Index for calendar date c,
"p = the estimated CAPM intercept (Jensen's alpha) for portfolio p,
$p = the estimated market beta for portfolio p, and
,pc = the regression error term.
These regressions yield estimates of the time-series systematic risk, $p, for each of the two
portfolios, as well as each portfolio’s abnormal return, "p.
Table 9 reports the results of our analysis. For the pre-announcement portfolio the
average daily raw return is 1.24 percent, while for the post-announcement portfolio it is -0.57
percent. Both returns are significantly different from zero. The average daily market-adjusted
return, as well as Jensen’s alpha, is 1.07 percent for the pre-announcement portfolio and -0.73
percent for the post-announcement portfolio.27 These returns are significantly greater than zero
and are of roughly the same order of magnitude as the buy-and-hold returns previously
26
documented. As expected, implementing a strategy of purchasing internet stocks in advance of
their earnings announcements and short-selling them after the earnings are reported would have
generated large abnormal returns over our sample period.
7. Summary and Conclusions
This paper presents evidence of persistent and significant anomalies in the stock returns
of internet firms surrounding their quarterly earnings announcements. Over the five days
preceding these announcements, and extending through the market opening on the day
immediately afterwards, the average buy-and-hold abnormal (market-adjusted) return is 4.9
percent. From the open that day through the close four days later the average abnormal return is
an even greater -6.4 percent. This abnormal return is positive in virtually every quarter within
this period, during both up and down markets. Furthermore, it remains significant even after
accounting for changes in risk around earnings announcements, the clustering in time of many
of our observations, and the magnitude of the bid-ask spread.
There is also little support for an information-based explanation for the returns we
document. In particular, neither the earnings surprise nor the revenue surprise is significantly
related to the pre-announcement price runup, thereby eliminating information leakage as a
possible explanation for these returns. As well, the post-announcement price reversal is not
significantly related to either the earnings or revenue news contained in the earnings report.
Additional analyses suggest that these return patterns are at least partially driven by price
pressure existing in the week prior to internet firms’ earnings announcements. Consistent with
price pressure, there is an abnormally high number of buyer-initiated relative to seller-initiated
28For empirical and anecdotal evidence of the futility of day trading see Barber and Odean (2000), “DayTrading is a Sucker’s Game,” by Leah Nathans Spiro (Business Week, August 16, 1999), and “The Day Trader –Online Investing can be All-consuming – If You Let It,” by Aaron Elstein (The Wall Street Journal, June 12, 2000).
27
trades in the five days before earnings announcements, an imbalance which not only disappears,
but reverses after earnings are released. Moreover, the post-announcement price reversal results
in an average buy-and-hold abnormal return over our entire event window that is insignificantly
different from zero. These results, taken as a whole, suggest a market inefficiency in the pricing
of internet stocks around their earnings announcements.
With day and momentum traders appearing to play a significant role in the trading
activity of internet stocks, it is likely that they are responsible, at least in part, for the price
pressure we document. If so, it is their (often futile) search for short-term profits that is,
somewhat ironically, creating the price pattern which others can exploit.28 To the extent that
their trading activity will diminish during a prolonged market downturn, we would expect the
abnormal return patterns surrounding the earnings announcements of internet firms to become
less pronounced as well.
28
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Rajgopal, S., S. Kotha, and M. Venkatachalam. “The Relevance of Web Traffic for InternetStock Prices.” Working Paper, University of Washington and Stanford University (2000).
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TABLE 1 Descriptive statistics
The sample consists of 393 publicly traded internet firms (see Section 2 for sample selection criteria). Summary statistics are presented for 1,875 firm-quarter earnings announcements, covering the period from January 1998 to August 2000. Market value of common shareholders’ equity is calculated using the closing price on the day of the earnings announcement, multiplied by the weighted average number of shares outstanding during the quarter. The opening (closing) bid-ask spread is the difference between the opening (closing) bid and ask prices, scaled by the average of the two prices. The abnormal return is a market-adjusted return, with the Nasdaq Composite used as the market index. Variable
Mean
Median
Std Dev
Minimum
Maximum
Market value of equity ($MM) 5113.28 579.10 28067.06 3.42 461251 Market-to-book ratio 15.46 7.21 43.29 0.37 915.30 Quarterly revenues ($MM) 65.17 117.92 366.02 0 5720 Quarterly earnings ($MM) -1.32 -3.21 63.65 -431.36 1127 Close-to-close daily raw return (%) 0.03 -0.13 1.48 -4.82 12.06 Close-to-open daily raw return (%) 0.85 0.76 0.97 -2.56 7.32 Open-to-close daily raw return (%) -0.79 -0.80 1.50 -8.63 7.04 Close-to-close daily abnormal return (%) -0.15 -0.03 1.32 -4.13 11.34 Close-to-open daily abnormal return (%) 0.56 0.44 0.95 -2.53 7.05 Open-to-close daily abnormal return (%) -0.68 -0.69 1.42 -8.24 6.94 Opening bid-ask spread (%) 1.34 0.91 1.51 -0.58 10.19 Closing bid-ask spread (%) 1.29 0.92 1.25 -0.02 8.97
TABLE 2 Average daily abnormal returns and buy-and-hold abnormal returns, January 1998 – August 2000
The daily abnormal return on trading day t, t ∈ {-25,…,-1, 1,…,25}, is the close-to-close market-adjusted return, with the Nasdaq Composite used as the market index. The average abnormal return is an equal-weighted average of the abnormal returns of the individual stocks. Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. The close-to-open return on day 1 is the return from the close on day –1 to the open on day 1. The open-to-close return on day 1 is the return from the open to the close that day. The buy-and-hold abnormal return (BHAR) for trading day t is the cumulative return from the close on day –26 to the close on day t, less the cumulative return on the Nasdaq Composite Index during that period. The average BHAR is the equal-weighted average of the BHAR’s of the individual stocks. The BHAR through the open on day 1 is the cumulative return from the close on day –26 to the open on day 1, less the cumulative return on the Nasdaq Composite Index during that period. t-statistics in bold indicate statistical significance at the 5 percent level (two-tailed). The number of daily observations varies between 1,750 and 1,874 for the abnormal returns, and between 1,729 and 1,758 for BHAR.
Daily Abnormal Return BHAR Daily Abnormal Return BHAR Trading day Average t-statistic Average t-statistic Trading day Average t-statistic Average t-statistics
-25 -0.000 -0.02 -0.000 -0.02 1 (close-to-open) 0.016 9.54 0.053 5.64 -24 0.000 0.07 -0.000 -0.11 1 (open-to-close) -0.031 -16.14 - - -23 -0.000 -0.04 0.000 0.07 1 -0.016 -6.56 0.021 2.32 -22 0.004 2.35 0.004 1.20 2 -0.012 -7.38 0.008 0.82 -21 0.000 0.05 0.005 1.11 3 -0.013 -8.19 -0.006 -0.65 -20 0.002 1.00 0.007 1.46 4 -0.004 -2.45 -0.012 -1.26 -19 0.003 1.39 0.008 1.69 5 -0.005 -3.70 -0.017 -1.88 -18 -0.003 -1.52 0.005 1.05 6 -0.001 -0.57 -0.017 -1.82 -17 0.002 0.95 0.007 1.23 7 -0.003 -1.82 -0.019 -1.99 -16 -0.000 -0.05 0.007 1.26 8 -0.003 -1.85 -0.022 -2.27 -15 0.002 1.17 0.010 1.67 9 -0.001 -0.77 -0.022 -2.13 -14 0.003 1.31 0.014 2.13 10 -0.000 -0.03 -0.022 -2.03 -13 0.002 1.06 0.017 2.43 11 -0.001 -0.79 -0.022 -2.10 -12 -0.002 -0.93 0.015 2.16 12 -0.001 -0.55 -0.022 -2.10 -11 -0.001 -0.47 0.014 1.98 13 -0.001 -0.79 -0.023 -2.18 -10 0.000 0.02 0.013 1.77 14 0.002 1.13 -0.020 -1.85 -9 0.002 1.28 0.015 2.02 15 0.002 1.01 -0.016 -1.43 -8 -0.003 -1.55 0.015 1.87 16 -0.000 -0.06 -0.016 -1.34 -7 0.001 0.28 0.018 2.16 17 0.000 0.08 -0.013 -1.13 -6 -0.003 -1.67 0.013 1.64 18 0.002 0.74 -0.012 -0.96 -5 0.002 0.95 0.014 1.64 19 -0.002 -1.11 -0.015 -1.26 -4 0.006 3.42 0.019 2.22 20 -0.000 -0.23 -0.014 -1.13 -3 0.004 2.33 0.022 2.53 21 -0.001 -0.69 -0.013 -1.08 -2 0.008 4.30 0.030 3.35 22 -0.001 -0.77 -0.015 -1.17 -1 0.014 7.04 0.040 4.36 23 0.002 1.30 -0.011 -0.87 24 0.001 0.60 -0.010 -0.76 25 0.004 2.26 -0.003 -0.25
TABLE 3 Average buy-and-hold abnormal returns from day –6 to day 5,
January 1998 – August 2000
The buy-and-hold abnormal return (BHAR) on an individual stock for a given period is the cumulative return over this period, less the corresponding cumulative return on the Nasdaq Composite Index. The average BHAR is the equally-weighted average of the BHAR’s of the individual stocks. BHAR-6,1 (BHAR1,5) denotes the buy-and-hold abnormal return from the close on day –6 through the open on day 1 (from the open on day 1 through the close on day 5). Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. Average BHAR’s are provided for the entire sample period, as well as by quarter. Each individual firm-quarter observation is classified by the calendar quarter of its earnings announcement date. Also reported are average BHAR’s adjusted for earnings announcements clustered in time, through the formation of equally-weighted portfolios of stocks announcing on the same calendar date. Finally, BHAR’s computed based on the assumption that all buys (sells) take place at the ask (bid) price are reported. t-statistics in bold indicate statistical significance at the 5 percent level (two-tailed).
BHAR-6,1
BHAR1,5 Average t-statistic Average t-statistic
Panel A: Entire sample period Close-to-close return 0.049 11.37 -0.064 -19.17 Bid-ask adjusted 0.037 8.20 -0.055 -15.02 Panel B: By quarter 1998, quarter 1 0.042 2.78 -0.013 -0.55 quarter 2 0.025 1.14 -0.047 -4.13 quarter 3 0.021 1.25 -0.073 -5.14 quarter 4 0.110 4.59 -0.006 -0.27 1999, quarter 1 0.079 3.59 -0.038 -2.73 quarter 2 0.045 2.86 -0.082 -7.70 quarter 3 -0.011 -1.12 -0.072 -7.74 quarter 4 0.106 8.72 -0.056 -6.08 2000, quarter 1 0.056 5.55 -0.096 -12.11 quarter 2 0.051 3.99 -0.047 -5.07 quarter 3 0.022 2.45 -0.079 -10.83 (through August)
TABLE 4 Daily values of Jensen’s alpha and systematic risk, January 1998 – August 2000
For each event day t, security i’s excess return is regressed on the excess market return (see Ibbotson (1975)), with the Nasdaq Composite used as the market. Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. The table reports the estimated abnormal return, αt (Jensen’s alpha), the systematic risk estimate, βt, and the adjusted R2 for each daily regression. Also reported are the estimates for regressions based on the day 1 close-to-open returns and the day 1 open-to-close returns. All t-statistics are based on White’s (1980) standard errors and are in bold if statistically significant at the 5 percent level (two-tailed).
Event day t αt t (αt = 0) βt t (βt = 1) Adjusted R2
-5 0.002 0.94 1.200 2.21 0.147 -4 0.007 3.85 1.339 4.25 0.170 -3 0.006 2.72 1.308 3.14 0.162 -2 0.008 4.36 1.196 2.30 0.147 -1 0.013 6.96 1.304 3.38 0.144
1 (close-to-open) 0.014 8.33 1.984 7.13 0.095 1 (open-to-close) -0.031 -15.63 1.020 0.20 0.069
1 -0.016 -6.51 1.157 1.49 0.068 2 -0.012 -7.20 0.991 0.10 0.096 3 -0.012 -8.03 1.000 0.00 0.127 4 -0.004 -2.57 0.954 0.65 0.112 5 -0.005 -3.65 1.068 0.99 0.123
TABLE 5 OLS regressions of buy-and-hold abnormal returns on earnings and revenue surprises,
January 1998 – August 2000 The earnings (revenue) surprise regressions are based on 823 (453) firm-quarter observations. The 10-day buy-and-hold abnormal return surrounding each earnings announcement is partitioned into four components: (i) the return from the close on day –6 through the close on day –1, (ii) the close-to-open return on day 1, (iii) the open-to-close return on day 1, and (iv) the return from the close on day 1 through the close on day 5. Each return component is separately regressed on earnings and revenue surprise. Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. Earnings (revenue) surprise is defined as the difference between actual earnings (revenue) per share and the consensus analyst earnings (revenue) forecast, scaled by the beginning price of the return window. The t-statistics are underneath the estimated coefficients. Those in bold indicate statistical significance at the 5 percent level (two-tailed).
close on day –6 to
close on day -1
close on day -1
to open on day +1
open on day +1
to close on day +1
close on day +1
to close on day +5
Panel A: Earnings regressions
INTERCEPT 0.037 0.012 -0.031 -0.034 (t-statistic) (5.23) (4.54) (-9.68) (-6.81)
Earnings surprise 0.210 0.374 0.322 -0.458 (t-statistic) (0.45) (1.84) (1.36) (-1.44)
Adjusted R2 -0.001 0.004 0.001 0.002
Panel B: Revenue regressions
INTERCEPT 0.037 0.018 -0.031 -0.038 (t-statistic) (4.26) (4.67) (-7.84) (-5.94)
Revenue surprise 0.305 0.190 0.287 0.511 (t-statistic) (0.58) (0.79) (1.12) (1.21)
Adjusted R2 -0.002 -0.001 0.001 0.001
TABLE 6 Abnormal order imbalance results, January 1998 – August 2000
Panel A reports the average daily abnormal order imbalance over days –5 to 5. Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. Daily order imbalance is the number of buyer-initiated trades minus the number of seller-initiated trades, scaled by the average number of daily trades over days –25 to –11 and 11 to 25. Abnormal order imbalance for a given day is the order imbalance for that day less the average order imbalance over days –25 to –11 and 11 to 25. Average abnormal order imbalance is an equally-weighted average of the individual stocks’ abnormal order imbalances. t-statistics in bold indicate statistical significance at the 5 percent level (two-tailed). Panel B presents the results of regressing a stock’s buy-and-hold abnormal return from the close on day –6 to the open on day 1 (BHAR-6,1) on its abnormal order imbalance averaged over days –5 to –1. The buy-and-hold abnormal return (BHAR) on an individual stock for the period is the cumulative return over the period, less the corresponding cumulative return on the Nasdaq Composite Index. The t-statistics are underneath the estimated coefficients. Those in bold indicate statistical significance at the 5 percent level (two-tailed).
Panel A: Average abnormal trade imbalance
Trading day
Average abnormal trade imbalance
t-statistic
-5 0.007 0.16 -4 0.013 2.42 -3 0.010 2.22 -2 0.015 3.49 -1 0.028 6.56 1 -0.031 -2.84 2 -0.018 -4.15 3 -0.019 -5.12 4 -0.005 -1.26 5 -0.008 -2.30
Panel B: OLS regression of BHAR-6,1 on pre-announcement abnormal trade imbalance INTERCEPT 0.038 (t-statistic) (8.98) Abnormal order imbalance 0.502 (t-statistic) (15.42) Adjusted R2 0.119
TABLE 7 Abnormal returns in the lockup and post-lockup periods, January 1998 – August 2000
This table compares abnormal returns around the 618 earnings announcements made during the share lockup period with those of the 1,758 earnings announcements made post-lockup. The lockup period is assumed to be the first 180 days following the firm’s initial public offering. The buy-and-hold abnormal return (BHAR) on an individual stock for a given period is the cumulative return over the period, less the corresponding cumulative return on the Nasdaq Composite Index. The average BHAR is the equally-weighted average of the BHAR’s of the individual stocks. BHAR-6,1 (BHAR1,5) denotes the buy-and-hold abnormal return from the close on day –6 through the open on day 1 (from the open on day 1 through the close on day 5). Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. t-statistics in bold indicate statistical significance at the 5 percent level (two-tailed).
Lockup period Post-lockup period Average BHAR-6,1 0.067 0.041 (t-statistic) (7.95) (8.21) Average BHAR1,5 -0.074 -0.059 (t-statistic) (-12.25) (-14.80) Average close-to-open abnormal return on day 1
0.022
0.013
(t-statistic) (7.78) (6.34) Average open-to-close abnormal return on day 1
-0.038
-0.028
(t-statistic) (-11.54) (-11.68)
TABLE 8 Abnormal returns for technology (non-internet) and non-technology samples, January 1998 – June 2000
This table compares abnormal returns around the earnings announcements of technology (non-internet) firms with those of non-technology firms. The technology (non-internet) sample consists of firms with the “technology” industry classification on the I/B/E/S detailed earnings forecast database. The non-technology sample is comprised of firms classified as either “consumer durables”, “basic industries”, or “capital goods” on I/B/E/S. The buy-and-hold abnormal return (BHAR) on an individual stock for a given period is the cumulative return over the period, less the corresponding cumulative return on the Nasdaq Composite Index. The average BHAR is the equally-weighted average of the BHAR’s of the individual stocks. BHAR-6,1 (BHAR1,5) denotes the buy-and-hold abnormal return from the close on day –6 through the open on day 1 (from the open on day 1 through the close on day 5). Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. t-statistics in bold indicate statistical significance at the 5 percent level (two-tailed).
Technology
(non-internet)
Non-technology Internet
(from Tables 2 and 3) Average BHAR-6,1 0.032 0.007 0.049 (t-statistic) (11.21) (2.01) (11.37) Average BHAR1,5 -0.015 -0.013 -0.064 (t-statistic) (-6.51) (-4.63) (-19.17) Average close-to-open abnormal return on day 1
0.005
-0.001
0.016
(t-statistic) (4.16) (-0.86) (9.54) Average open-to-close abnormal return on day 1
-0.009
-0.002
-0.031
(t-statistic) (-6.74) (-0.90) (-16.14)
TABLE 9 Trading strategy daily returns, January 1998 – August 2000
As of each day’s close, the equal-weighted pre-announcement portfolio consists of every stock whose
earnings will be announced sometime before the open five trading days hence. At each day’s open any stock whose earnings were announced while the market was closed is dropped from the portfolio, and the portfolio is rebalanced. As of each day’s open, the equal-weighted post-announcement portfolio consists of every stock whose earnings were announced sometime between the close five trading days earlier and the current day’s open. At each day’s close, any stock whose earnings were announced after the market closed five trading days earlier is dropped from the portfolio, and the portfolio is rebalanced. The daily equal-weighted raw return is computed by compounding the close-to-open and open-to-close portfolio returns that day. The daily market-adjusted return subtracts the raw return on the Nasdaq Composite Index from the portfolio raw return. Jensen’s alpha is the estimated intercept from a time-series regression of the portfolio daily excess returns on the market daily excess returns. t-statistics in bold indicate statistical significance at the 5 percent level (two-tailed). Value t-statistic Average daily raw return (%): Pre-announcement portfolio 1.24 5.91 Post-announcement portfolio -0.57 -3.32 Average daily market-adjusted return (%): Pre-announcement portfolio 1.07 5.93 Post-announcement portfolio -0.73 -4.93 Jensen’s alpha (%): Pre-announcement portfolio 1.07 5.99 Post-announcement portfolio -0.73 -4.94
Figure 1: Average buy-and-hold abnormal returns: January 1998 – August 2000 The buy-and-hold abnormal return (BHAR) for trading day t, t 0 {-25,…,-1, 1,…,25}, is the cumulative return from the close on day –26 to the close on day t, less the cumulative return on the Nasdaq Composite Index during that period. The average BHAR is the equally-weighted average of the BHAR’s of the individual stocks. Trading day –1 (1) is the trading day immediately preceding (following) the earnings announcement. The BHAR through the open on day 1 (denoted by ‘1 (open)’ in the figure), is the cumulative return from the close on day –26 to the open on day 1, less the cumulative return on the Nasdaq Composite Index during that period.
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
6%
trading day relative to earnings announcement