DP2012-06

How Do Investors Trad e When Ac tual
Earnings A re Reportedwith M anagement Forecasts?
Katsuhiko MURAMIYA
Kazuhisa OTOGAWA
February 27, 2012

How Do Investors Trade When Actual Earnings Are Reportedwith Management Forecasts?∗
Katsuhiko Muramiya†and Kazuhisa Otogawa‡May 2011

Abstract: We use trade size to distinguish between individuals and institutions and then examine theirtrading behaviors around earnings announcements using data from the Tokyo Stock Exchange. Japaneselisted ﬁrms have a distinctive ﬁnancial reporting system in that they report actual earnings for prior andcurrent years, and in addition, almost all of them release management earnings forecasts for the next
year. Under this unique setting, we test whether individuals respond diﬀerently from institutions to thesame earnings news. We document the following results: (1) With regard to current earnings, individuals(institutions) strongly respond to simplistic random walk forecast errors (analyst forecast errors), while
do not always respond to analyst forecast errors (simplistic random walk forecast errors). (2) With regard
to management earnings forecasts, both individuals and institutions use them, but individuals react tothem literally. In contrast to naı̈ve trading by individuals, institutions rationally respond to them with
their predicted optimistic bias in mind. Overall, our results suggest that individuals’ trading is so naı̈ve
as if they use nothing other than the information released at the time of earning announcement, while
institutions’ trading is so sophisticated.

∗ We gratefully acknowledge the valuable comments and suggestions of Takashi Kamihigashi, Tomomi Takada, Takashi

Obinata, Koji Ota, Yoshitaka Fukui, Eiko Sakai, Takashi Asano, Satoshi Koibuchi, Yuji Honjo, Eiji Okuyama, Wataru Ohta,
Atsushi Shiiba, Yasuhiro Ohta, Shyam Sunder, Kunio Ito, Makoto Nakano, Tetsuyuki Kagaya, and seminar participantsat Kobe University, Musashi University, Chuo University, Osaka University, and Hitotsubashi University. This research
was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B),21730369, 2009–2011 (Muramiya) and Grant-in-Aid for Scientiﬁc Research (B), 21330110, 2009 (Otogawa).
† Corresponding author. Research Institute for Economics and Business Administration, Kobe University.

Address 2-1, Rokkodai, Nada, Kobe 657-8501 JAPAN.
Tel. & Fax +81 78-803-7039; E-mail address muramiya@rieb.kobe-u.ac.jp (Muramiya).
‡ Graduate School of Business Administration, Kobe University

1

Introduction

Prior research in accounting and ﬁnance tends to view individual and institutional traders diﬀerently; inparticular, institutions are regarded as more sophisticated traders than individuals on the basis of theexamination of their trading behavior. Earnings announcement is one of the events wherein researchers
have documented the diﬀerential trading behavior between individuals and institutions.
In a pioneering paper, Lee [1992] ﬁnds that individuals buy stocks with both positive and negative
surprises while institutions’ trading directions generally correspond to the directions of earnings surprises.
He suggests that the result is consistent with the hypothesis that individuals typically rely on a very
diﬀerent set of information sources from institutions and spend far less time on investment analysis, andtherefore, these two investor groups trade diﬀerently even when they receive identical earnings news.
We examine whether the trading behavior in response to identical earnings news is diﬀerent between
individuals and institutions using data from the Tokyo Stock Exchange (TSE).
Firms listed on the TSE have a unique practice of annual earnings announcements1 . The TSE requires
listed ﬁrms to report not only the actual earnings of the previous year and the current year but also the
management earnings forecasts for the following year at the earnings announcements so as to provide
information that is useful in investment decisions. Furthermore, these forecasts are released in theform of point estimates, because ﬁrms are recommended not to issue range or qualitative estimates(e.g., TSE [2006a]). Actually, almost all the ﬁrms report both the actual earnings for the two years
and the point estimates of the management earnings forecasts for the next year in accordance with
the TSE requirement. This fact implies that the contemporaneous discloses of the actual earnings andthe management earnings forecasts are eﬀectively mandated in Japan. This situation raises interestingquestions about what information do individuals and institutions respond to and how to. Thus, we address
this question by examining the investor-level reactions to simultaneously disclosed actual earnings andmanagement earnings forecasts.
Before examining the investor-level reactions, we begin by investigating the market-level reactions,
1 There are two reasons why we focus on not quarterly earnings announcements but annual earnings announcements.

The ﬁrst reason is that quarterly earnings announcements are not required by the TSE during the ﬁrst half of our sample
period (1999–2006). It was only in 2003 that the TSE made it mandatory for the listed ﬁrms to report quarterly earnings.
Another, more important reason is that the TSE does not require the listed ﬁrms to report management earnings forecasts
for the forthcoming quarter or the same quarter of the next year at the time of quarterly earnings announcements (e.g.,
TSE [2006a]). Since the purpose of this study is to examine the investor-level reactions to earnings announcements that
include both actual earnings and management earnings forecasts, we do not focus on quarterly earnings announcements.

1which are measured as abnormal stock returns, to earnings announcements. With regard to market-level
reactions to current earnings, we ﬁnd that the stock market reaction is strongly associated with analyst
earnings forecasts errors as if the stock market forms an earnings expectation of the current year’s
earnings based on the latest analyst earnings forecasts. Because of the timing advantage (i.e., accessto more recent information) and information advantage (i.e., access to a greater amount of available
information), analyst earnings forecasts are more accurate than time-series statistical models including
the random walk model (e.g., Brown and Rozeﬀ [1978]), that is, they are the best available estimate
at the earnings announcements. Therefore, such market reaction indicates that market participants, on
average, integrate costly predisclosure information—analyst earnings forecasts—when forming earnings
expectations.
However, we also ﬁnd that the stock market reacts to the simplistic random walk forecasts errors (i.e.,the diﬀerence between the actual earnings of the current year and the previous year), though the analyst
forecasts errors are more strongly associated with the stock market reaction than the simplistic random
walk forecasts errors. The ﬁrms listed on the TSE report not only the actual earnings of the current year
but also the actual earnings of the prior year at the earnings announcements, and therefore, investors
who only rely on the limited piece of information set that is easily available at the time are likely to holdnaı̈ve expectations based on the simplistic random walk model. The result suggests the possibility thatcertain groups of investors in the stock market depend on the readily available simplistic random walkmodel even though predictions based on it are signiﬁcantly less accurate than the latest analyst earnings
forecasts.
With regard to market-level reactions to the management earnings forecasts, we ﬁnd that the stock
market strongly reacts to the management earnings forecasts. It is well known that the management
earnings forecasts issued at the time of earnings announcement, on average, are systematically upward
biased and we can also observe such a tendency in our sample. Therefore, our ﬁnding indicates thatthe management earnings forecasts have information content for the stock market in spite of the general
optimistic bias, which is consistent with Kato, Skinner, and Kunimura [2009].
Another important feature of the management earnings forecasts in Japan is that the forecast bias
have a strongly positive autocorrelation structure: the forecast bias is positively associated with lag one.
As long as the stock price is set by rational investors who are aware of the autocorrelation structure of
the bias, the positive relationship between stock returns during the earnings announcement period andgood news forecasts (bad news forecasts) should be weaker (stronger) when the managers have issued

2

optimistic earnings forecasts in the previous year. Consistent with this prediction, we ﬁnd that thestock market discounts good news forecasts if the managers issued optimistic forecasts at the earnings
announcements of the previous year. This result suggests that for ﬁrms with good news forecasts, the stock
market responds less positively to the management earnings forecasts with higher anticipated optimism
by taking into account the autocorrelation structure of the forecast bias. In contrast, we ﬁnd no evidence
to support the prediction for ﬁrms with bad news forecasts. Since we ﬁnd that bad news forecasts areless optimistically biased than good news forecasts, the result for bad news forecasts indicates that thestock market recognizes bad news forecasts as being inherently credible, and is thus literally responsive
to bad news forecasts.
As the above analyses based on stock returns, which is an aggregate measure, implicitly assume thatmarket participants are homogeneous (e.g., Lee [1992]), what information do individuals and institutions
respond to and how to at the time of earnings announcement remain unsolved. Thus, we investigate
the investor-level reactions to the earnings announcements. Following Lee [1992] and other studies, we
use trade size to distinguish between individuals and institutions, and measure directional volume (i.e.,
signed order imbalance) in each investor group so as to capture their trading behavior. We suppose that
individuals (institutions) make small (large) trades more frequently2 .
Regarding current earnings, we ﬁnd that unusually the buying/selling activity of individuals during
the earnings announcement period is positively associated with the simplistic random walk forecast
errors, whereas has little association with the analyst forecast errors. The trading behavior in responseto the current earnings depends on the earnings expectation that each investor has prior to the earnings
announcements. Hence, this result suggests that individuals regard the actual earnings of the previous
year as prior earnings expectations even though signiﬁcantly better earnings expectations in the formof analyst earnings forecasts are available if they are willing to incur costs in acquiring the information.
They form less costly earnings expectation based on the last year’s earnings as though they only readthe earnings reports and/or the ﬁnancial press.
With regard to the management earnings forecasts, we ﬁnd that the relation with the unusual buying/selling activities of individuals during the earnings announcement period is a purely mechanical one:they respond to the management earnings forecasts literally without considering the positive autocorrelation structure of the management earnings forecasts bias. If they are ready to incur the two types of

2 Therefore, we use the term individuals (institutions) and small traders (large traders) interchangeably throughout thispaper.

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costs, that is, the information acquisition cost (obtaining the management earnings forecasts issued at
the earnings announcements of the previous year) and the information processing cost (analyzing the bias
in them), they could predict the bias in the forecasts issued at the current year’s announcement. Actually, however, they ignore the autocorrelation structure, and hence, trade on the management earnings
forecasts at face value. In sum, the individuals’ trading behavior in response to the contemporaneous
announcements of actual earnings and management earnings forecasts is so naı̈ve as if they use nothingother than the information released at the time of earnings announcement. Individuals seem to avoid
incurring the costs of gathering and processing information.
In contrast to naı̈ve trading by individuals, we ﬁnd that the institutional trading behavior is moresophisticated. For current earnings, the abnormal buy/sell order imbalance of institutions during theearnings announcement period is strongly positively associated with the analyst forecasts errors, and has arelatively modest positive association with the simplistic random walk forecast errors. This result suggeststhat institutions actively use the analyst earnings forecasts that are costly predisclosure information when
they form earnings expectations.
As far as the management earnings forecasts are concerned, consistent with the movement in stock
returns around earnings announcements, we ﬁnd that institutions adjust their trading in response to thegood news forecasts by taking into account the autocorrelation structure of the management forecasts bias:
the abnormal buy/sell order imbalance of institutions during the earnings announcement period is more(less) positively associated with the good news forecasts if the managers issued pessimistic (optimistic)
forecasts at the time of earnings announcement in the previous year. In other words, for the good news
forecasts, institutions discount them with higher anticipated optimism by reference to the previous year’s
management earnings forecasts bias. In contrast to the good news forecasts, we ﬁnd that the institutions’reaction to bad news forecasts does not depend on the prior management forecasts bias. This result isalso consistent with the movement in stock returns around the earnings announcements. Recall thatthe bad news forecasts are less optimistically biased than the good news forecasts. Therefore, the result
for the institutional trading behavior in response to the bad news forecasts indicates that institutions
recognize the bad news forecasts as being inherently credible, and thus, take the bad news forecasts at
face value even when the previous management forecasts are optimistically biased. Our results suggestthat institutions are more willing to incur additional costs for gathering and processing informationas compared to individuals: they tend to actively use costly predisclosure information such as analyst
earnings forecasts and prior management earnings forecasts, and then properly unscramble the earnings

4information released at the time of earnings announcement in combination with such costly information.
Finally, we provide the evidence that the individuals’ (institutions’) unusual buy/sell order imbalance during the earnings announcement period is positively (negatively) associated with the postannouncement returns over a sixty-day period. This result indicates that institutions earn positive
returns from their rational trading in response to earnings announcements, while individuals receive
negative returns from their naı̈ve trading.
Overall, our empirical results suggest that in the Japanese setting, institutions make better use of
the earnings announcement information including the management earnings forecasts. This is consistent
with prior research in the sense that institutions are more sophisticated traders than individuals.
Our study makes three contributions. First, our study contributes to the existing literature on management forecasts by providing new evidence of how individuals and institutions respond to the management
earnings forecasts when the information is mandatorily disclosed at the time of earnings announcement.
Although Patell [1976], which is one of the pioneering studies in management forecasts research, ﬁnds thata forecasts disclosure is accompanied by a signiﬁcant price adjustment and concludes that the management forecasts convey useful information to investors, he states the following limitation of the research,“... since I deal only with voluntary disclosure, the question of investor ... response to mandated forecasting procedures remain unresolved.” Kato, Skinner, and Kunimura [2009] answered this unsolved question
using data from the TSE in which disclosing management forecasts is eﬀectively mandatory. They ﬁndthat the management earnings forecasts are signiﬁcantly associated with the stock returns during theannouncement period, and then, conclude that the management forecasts are informative. They, however, only investigate the aggregate market reaction to eﬀectively mandated management forecasts, andhence, who responds to the information and how are still unresolved. Our research design allows us to answer this question and we reveal that individuals and institutions respond to the mandated management
earnings forecasts diﬀerently.
Second, we provide additional evidence on the stock market reactions to the predicted bias in the
management earnings forecasts. Rogers and Stocken [2005] investigate the stock market’s response to
the predicted bias and show that the stock market ﬁlters out the predictable bias in the management
earnings forecasts but this ﬁltering is more pronounced when the manager reports good news forecasts.
We measure the predicted bias using the autocorrelation structure of the management forecasts bias
and reveal that the stock market ﬁlters out the predicted bias in the good news forecasts but not in
the bad news forecasts, which is consistent with Rogers and Stocken [2005]. Our contribution is to5investigate not only the market-level reactions but also the investor-level reactions to the predicted bias
in the management earnings forecasts. We ﬁnd that the institutional trading behavior in response to thepredicted bias resembles the stock market reaction, while the individuals’ trading behavior does not. Ourﬁndings suggest that the ﬁltering behavior is observed for institutions but not for individuals.
Third, we corroborate and extend Lee’s [1992] ﬁndings regarding the diﬀerential trading behavior
between individuals and institutions in response to earnings news. Prior works provide some evidence
that individuals and institutions diﬀer systematically in their reactions to earnings news (e.g., Lee [1992];
Bhattacharya [2001]; Battalio and Mendenhall [2005]). However, as to why their responses vary is yet to
be properly answered. Our results suggest that the attitudinal diﬀerence for the cost of gathering andprocessing information appears to be the plausible reason. Individuals prefer to use easily available information at low cost and spend far less time on analyzing the earnings information. Therefore, they ignore
the analyst forecasts when they react to the current earnings and also ignore the autocorrelation structureof the management forecasts bias when they react to the management earnings forecasts. In contrast,
institutions tend to willingly use the costly predisclosure information and unscramble the earnings information released at the time of earnings announcement in combination with such these costly predisclosure
information, so that they use the analyst forecasts and anticipate the autocorrelation structure of the
management forecasts bias. Thus, we claim that the information cost might fully explain the diﬀerential
trading behavior between individuals and institutions under the Japanese earnings announcement setting.
The paper proceeds as follows. The next section explains the institutional background of the management earnings forecasts in Japan and develops our hypotheses. In Section 3, we present the research
design and data description. Section 4 reports the empirical results using the data from the TSE. Finally,
Section 5 presents the summary and conclusion.

2

Institutional Background and Hypotheses Development

2.1

Financial reporting system in Japan: Overview

In Japan, there is a distinctive ﬁnancial reporting system in that the listed ﬁrms simultaneously report
not only several current ﬁnancial items but also the management forecasts of these items at the annual
earnings announcement. The Securities Listing Regulations, promulgated by the TSE, have requested theﬁrms to disclose the ﬁnancial results for a ﬁscal year in a prescribed form. This earnings report is called
Kessan-Tanshin, and includes space for management forecasts. Therefore, the listed ﬁrms are expected
to report the management forecasts for the next ﬁscal year along with the current ﬁnancial performance.
6However, there are cases for not reporting the management forecasts. The TSE allows the ﬁrms that arevery uncertain about their future prospects to not report the management forecasts so that investors do
not make decisions based on misleading information (TSE [2006a])3 . Thus, there are some exceptions,but most ﬁrms report the management forecasts in accordance with this requirement. In fact, Kato,
Skinner, and Kunimura [2009] show that 93.7% of the ﬁrm-years in their sample period (1997–2007) havemanagement forecasts data. This result suggests that the contemporaneous announcements of the current
and future ﬁnancial performance are common, and there is no doubt that the practice of reporting boththe current performance and the management forecasts is eﬀectively mandated in Japan.
The listed ﬁrms are required by the TSE to submit a non-audited overview of some ﬁnancial measures(i.e., earnings report) as soon as possible4 . The submission deadline is 45 days after the end of the ﬁscal
year. The ﬁnancial information in the earnings report is disseminated through the disclosure network
system (Timely Disclosure network; TDnet) immediately after submission. The actual earnings for theprevious and current years and the management earnings forecasts for the following year are also publishedin the Nihon Keizai Shimbun (a major ﬁnancial press in Japan) in the next morning. Thus, investors
can easily receive the ﬁrms’ ﬁnancial information through TDnet, the newspaper, or other information
channels at the latest within one day after the earnings announcements.
Table 1 shows the typical format of an earnings report that Nintendo Co., Ltd (one of the most famousvideogame console and handheld device makers in Japan) announced as part of its annual ﬁnancial results
as of March 2009 on May 7, 2009. The document brieﬂy reports the sales, operating income, earnings
from continuing operation, net income, earnings per share, and dividend per share for the previous andcurrent years5 . Furthermore, the management’s point estimates of these ﬁnancial items for the following
year are also reported.
In the U.S., there are safe harbor rules for forward looking information so that the managements
cannot be easily sued for forecasts that did not materialize. As in the U.S., the managers will not be held
3 Indeed, most security ﬁrms do not provide management forecasts for the reason that their future ﬁnancial performanceis liable to volatility in the stock market. However, the TSE requires even these ﬁrms to report the management forecasts
during the ﬁscal year shortly after ambiguity is clariﬁed (TSE [2006a]).
4 After announcing this earnings report, the ﬁrms also have to submit audited ﬁnancial statements, such as the balance

sheet, income statement, and cash ﬂow statement, within three months after the ﬁscal year’s end. The investors can accessthese statements on the ﬁrms’ web sites or on the Electronic Disclosure for Investors’ NETwork (EDINET) system, which
is similar to the Electronic Data-Gathering, Analysis, and Retrieval (EDGAR) system in the U.S.
5 Since 2007, the TSE requires the listed ﬁrms to report the management forecasts of operating income because of its

growing importance for investors.

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liable for falling short of their forecasts in Japan. The Securities Listing Regulations require the managers
to publicize the updated forecasts during the ﬁscal year as soon as they realize the signiﬁcant revisions in
the initial management forecasts announced at the beginning of the ﬁscal year6 . In addition, once they
revise their forecasts, they should report the updated forecasts again if there are signiﬁcant revisions
in the previously announced revised forecasts. In other words, this regulation requests the managers toreport the updated forecasts immediately after they realize a wide divergence between the latest forecasts
and the updated forecasts7 . As long as they follow this rule, they will never be blamed for falling shortof their forecasts (e.g., Ota [2010]).
This environment gives the managers incentives to issue biased forecasts at the beginning of theﬁscal year, because they have only to issue updated forecasts during the ﬁscal year even if they issue
misrepresented forecasts at that time. Indeed, Kato, Skinner, and Kunimura [2009] ﬁnd that the managers
in Japan, on average, bias their initial forecasts upward but then revise their forecasts downward during
the ﬁscal year in order to avoid negative earnings surprises. Ota [2006] examines the association between
the speciﬁc ﬁrm characteristics and the initial forecasts bias. He shows that while the managers of smallﬁrms, OTC listed ﬁrms, and ﬁnancially distressed ﬁrms tend to issue optimistic forecasts, those of ﬁrms
in price-regulated industries (e.g., electricity and gas industries) tend to release pessimistic forecasts.
These studies suggest that the initial forecasts announced at the time of earnings announcement might
be intentionally distorted by some managers.

2.2

Trading behavior of small and large traders to current earnings information

In this section, we develop our hypotheses about the trade reaction to current earnings around theearnings announcement separately for small and large traders.
The trading behavior in response to the current earnings around that time depends on the priorearnings expectation of each investor. When the reported actual earnings are higher (lower) than theinvestors’ expectation prior to the earnings announcement, they revise their belief about the ﬁrm’s future
prospect upward (downward) and therefore buy (sell) shares of the stock following the announcement.
There may be two earnings expectations that investors are likely to have. One is the expectation
6 Similar regulations also exist in the Securities and Exchange Act (now called the Financial Instruments and ExchangeAct), imposed by the Japanese government.
7 The Securities Listing Regulations indicate that the diﬀerence between the latest forecasts and the updated forecasts issigniﬁcant when the updated sale (earnings) forecasts increase or decrease by more than 10% (30%) of the former forecasts,or the dividend per share forecasts are changed.

8

following a simplistic random walk model (i.e., the expected earnings are simply the earnings for theprevious year). As mentioned above, not only the current earnings but also the previous year’s earnings
are reported in the earnings report and the major ﬁnancial press. Therefore, investors who rely exclusively
on the information reported at the time of earnings announcement tend to hold the earnings expectationthat follows the random walk model. Such investors’ primary focus is on a comparison of the earnings ofthe current year with those of the previous year and they would buy (sell) stocks with positive (negative)
random walk forecasts error. In the example in Table 1, because the random walk forecasts error ispositive (22 billion yen, calculated by subtracting the previous year’s net income (257 billion yen) fromthe current year’s net income (279 billion yen)), such investors are expected to buy the stock8 .
The second is the expectation following the analysts’ forecasts. Financial analysts incorporate into
their earnings forecasts all the interim information reported by the ﬁrm and the economic news in atimely fashion. In addition, because they collect information not only from public but also from private
sources, their earnings forecasts also reﬂect private information (e.g., Healy and Palepu [2001]). Becauseof the timing advantage (i.e., access to more recent information) and information advantage (i.e., accessto a greater amount of available information), their earnings forecasts are more accurate than the timeseries statistical models including the random walk model (Brown and Rozeﬀ [1978]; Brown, Hagerman,
Griﬃn, and Zmijewski [1987])9 . Therefore, considering the forecasts accuracy, rational investors should
use the analysts forecasts that are the best available forecasts at the time of earnings announcement informing expected earnings. The major concern of such investors is whether the reported current earnings
exceed the latest analysts forecasts; they would buy (sell) stocks with positive (negative) analyst forecast
errors. Taking the case of Nintendo for example, if the latest analyst earnings forecast is 290 billion yen,
the analyst forecast error is negative (minus 11 billion yen, calculated by subtracting the latest analysts
earnings forecast (290 billion yen) from the current year’s net income (279 billion yen)). Consequently,
the reported earnings are lower than expectations—the latest analysts forecasts—and therefore, suchinvestors would sell the stock.
Given the analysts superiority in predicting the ﬁrm’s earnings to the simplistic random walk model,
most of the investors probably use the analysts earnings forecasts as their prior expectations. However,previous studies examining stock returns around the quarterly earnings announcements in the U.S. do
8 Some ﬁrms reporting losses do not report earnings per share, and hence, we use earnings on a total amount basis in

this example and also in our empirical analyses.
9 We can observe a similar result in Japan when we compare the accuracy of the analyst forecasts to that of the random

walk forecasts. See section 3.5 for details.

9not always support this notion. Several studies ﬁnd that some investors hold naı̈ve expectations basedon a seasonal random walk model. For example, Bernard and Thomas [1990] provide the evidence thatthe stock prices at least partially reﬂect the earnings expectations based on the seasonal random walkmodel.
A few recent studies investigate what type of investor tends to hold naı̈ve expectations more directly
using high frequency data. Bhattacharya [2001] reveals that small traders’ trading activity around theearnings announcements is increasing in the absolute seasonal random walk forecast errors. Battalio
and Mendenhall [2005] ﬁnd that small traders are likely to buy (sell) stocks when the current quarterly
earnings are higher (lower) than those of the same ﬁscal quarter of the previous year. Shanthikumar[2004] examines the association between the trading behavior of small traders around the earnings announcements and the seasonal random walk forecast errors10 . She shows that the buying activity of small
traders is not associated with the seasonal random forecast error in the few days before the earnings announcement, while it is positively associated with the error once the earnings are made pubic. Thesestudies reach the same conclusion that small traders, represented by individuals, appear to hold earnings expectations based on an inferior and unsophisticated model (i.e., a seasonal random walk model)
despite the availability of a more accurate and sophisticated forecast (i.e., the analysts forecast). Such
traders’ activity is consistent with the assumption that they only rely on limited piece of informationthat is available at lower cost; they behave as if they only read the “Digest of Earnings Reports” in theWall Street Journal including the year-to-year change in the quarterly earnings, and therefore, regard theearnings for the corresponding quarter of the previous year as the expected earnings (see also, Bernard
and Thomas [1990]; Bartov, Radhakrishnan, and Krinsky [2000]). Prior research in the U.S. suggeststhat small traders tend to ignore costly predisclosure information as represented by analyst forecasts anduse the seasonal random walk model in reference to the less costly incomplete information set available
at the time of earnings announcement.
Further in Japan, investors can access to both the current earnings and the previous year’s earnings
through the TDnet or the major ﬁnancial press at a low cost as described above. If the implication ofprior research in the U.S. is applicable to the annual earnings announcements in Japan, small traders
will ignore important predisclosure information and rely exclusively on less costly information in forming
expected earnings. In such a case, the earnings expectation of small traders will most likely reﬂect the

10 Shanthikumar [2004] uses both the simplistic seasonal random walk model and the seasonal random walk model withdrift. Her result is not sensitive to the choice of the expectation model.

10

random walk forecast, and therefore, the positive (negative) random walk forecast error triggers buying(selling) activity in the trades initiated by them. Hence, we formalize our ﬁrst hypothesis, in alternative
form, as follows:

Hypothesis 1:

The buying/selling activities of small traders around earnings announcements are
positively associated with the random walk forecast errors and are not positively
associated with the analyst forecast errors.

On the other hand, several studies in the U.S. ﬁnd that large traders, represented by institutions,tend to use a more accurate prediction, an analyst forecast, in forming their earnings expectations. For
example, Walther [1997] assumes that the institutional ownership captures the degree of sophistication of
the marginal investor and then examines the association between the institutional holding and the information used to form expected earnings. She shows that the stock returns around earnings announcements
are more strongly associated with the analyst forecast errors for stocks for which the marginal investor ismore likely to be sophisticated. Her result indicates that the institutions’ earnings expectations resemble
the analysts forecasts more closely than the naı̈ve forecasts.
By using high frequency data, Battalio and Mendenhall [2005] investigate what type of investors’
earnings expectation follows the analysts forecasts more directly. They ﬁnd that large traders, represented
by institutions, use the earnings prediction based on analyst forecasts. Shanthikumar [2004] shows thatlarge traders respond more strongly to earnings surprises based on analyst forecasts than do small traders
throughout the event period from 3 days before to 3 days after the earnings announcement date. These
results suggest that large traders actively incorporate even costly predisclosure information such as analyst
forecasts into their earnings expectations regardless of the cost of information acquisition. Therefore,
large traders rely much more on the analysts forecasts that are the best available estimates at the timeof earnings announcement than the seasonal random walk model forecasts in forming expected earnings,
and they behave as if they hold the earnings expectations that resemble analyst forecasts.
If the implication of the U.S. research is applicable to the Japanese setting, large traders will strongly
react to the analyst earnings forecast errors around the annual earnings announcements. In other words,
when the reported actual earnings are higher (lower) than the most recent analyst forecasts, large traders
should revise their beliefs upward (downward) and then buy (sell) the stocks following the announcement.
Hence, the second hypothesis (stated in alternative form) is developed as follows:11Hypothesis 2:

The buying/selling activities of large traders around earnings announcements are
positively associated with the analyst forecast errors and are not positively associatedwith the random walk forecast errors.

In the next section, we develop our hypotheses about the trade reaction to the management earnings
forecasts released under the unique earnings announcement setting in Japan separately for small andlarge traders.

2.3

Trading behavior of small and large traders in response to the management earnings forecasts

Numerous researchers have investigated the stock market reaction to the voluntarily disclosed management forecasts in the U.S. since the 1970s. For instance, early studies show that disclosing management
forecasts triggers signiﬁcant changes in the stock price and/or increases in the trading volume (e.g.,
Foster [1973], Patell [1976], Penman [1980]). In addition, the following studies ﬁnd that the stock returns around the management forecasts announcements are positively associated with the unexpectedcomponent of management forecasts in terms of both the sign and magnitude (Ajinkya and Gift [1984],
Waymire [1984]). These papers conclude that the management forecasts convey information to investors,
and that investors revise their expectations following the management forecasts. However, these results
are mostly based on the stand-alone voluntarily management forecasts, and these studies do not examine
the market reaction to the management earnings forecasts when market participants contemporaneously
receive information about the historical earnings and future earnings forecasts.
In contrast, several studies examine the Japanese stock market reaction to the management earnings
forecasts when the ﬁrms mandatorily and concurrently report not only their current performance but
also management forecasts at the earnings announcement. Goto and Sakurai [1993] report that theunexpected component of the management forecasts can explain the cross-sectional variation in the stock
returns around the earnings announcements, even after controlling for the current earnings surprises (seealso, Kato, Skinner, and Kunimura [2009]). Ota [2010] examines the value relevance of the book valuesof equity, current earnings, and management earnings forecasts, and then ﬁnds that the management
earnings forecasts contain more value-relevant information than the other two items. Overall, theseresults indicate that the management earnings forecasts are informative to investors even when the
managers simultaneously announce their current earnings and their earnings forecasts at the time ofearnings announcement.

12

These ﬁndings are not surprising, given that a stock is priced based on the ﬁrm’s future prospect.
Because the managers are the most familiar with the future prospects for their ﬁrms, the information
about the future earnings as reported by the managers should be relevant to any investor. However,while the management earnings forecasts are relevant, these forecasts are less reliable information for theinvestors as compared to the current earnings information. This is because the current earnings are basedon past transactions, but the management earnings forecasts are based on possible future transactions
and are discretionary (Atiase, Li, Supattarakul, and Tse [2005]).
It is well known that the initial management forecasts for the next year as reported at the earnings announcement are, on average, systematically upward biased (Herrmann, Inoue, and Thomas [2003]; Kato,
Skinner, and Kunimura [2009])11 . In addition, one of the most important features of the management
earnings forecasts in Japan is that the forecast errors are positively associated with lag one in terms ofsign and magnitude. Kato, Skinner, and Kunimura [2009] ﬁnd that the possibility of reporting optimistic
forecasts at the current year’s earnings announcements increases with that of reporting optimistic forecasts in the previous year. Shimizu [2007] shows that the management earnings forecast errors have aﬁrst-order autocorrelation coeﬃcient of 0.38 (p < 0.01) when are pooled all observations, and that such ahighly autocorrelated structure is observed for each sample year (1997–2006). These results suggest thatthe management forecast biases in Japan have a positive autocorrelation structure.
How do investors trade on the basis of these management earnings forecasts with such tendency?
Rational investors who are aware of the autocorrelation structure of management earnings forecasts will
adjust their trades for the expected optimistic bias, when they receive the next year’s earnings forecasts
reported by the managers who have reported very optimistically-biased forecasts in the previous year.
On the other hand, naı̈ve investors who do not understand the autocorrelation structure of the management earnings forecasts bias should trade on the information regardless of whether or not the managers
have issued optimistically biased forecasts. Such investors will systematically buy (sell) stocks with positive (negative) news in the management earnings forecasts around the earnings announcements. The
functional ﬁxation hypothesis predicts that this type of investors corresponds to individuals (e.g., Watts
and Zimmerman [1986]; Hand [1990]). This maintains that individuals do not properly unscramble theaccounting-related information, and hence, react to the information literally. Several researchers in the

11 Herrmann, Inoue, and Thomas [2003] show that 61.4% of observations for their sample (1993–1997) report optimisticallybiased management earnings forecasts at the earnings announcement, and Kato, Skinner, and Kunimura [2009] show that
57.6% of observations for their sample (1997–2006) do so.

13

U.S. investigate the trading behavior of individuals to well known biased information, analysts’ stock
recommendations, and then provide evidence supporting this notion.
It is well demonstrated that as the ﬁnancial analysts are unwilling to issue unfavorable recommendations, their recommendations exhibit a strong upward bias, and that the upward bias is more pronounced
for the analysts whose employer is aﬃliated with a ﬁrm through an underwriting relationship (e.g., Linand McNichols [1998]; Michaely and Womack [1999]). The rational investors who are aware of suchtendency of analyst recommendations are expected to adjust their trades for the general upward bias
as well as the additional distortion induced by the aﬃliated analysts. However, prior research showsthat individuals do not take such rational behaviors in response to the analyst recommendations. Malmendier and Shanthikumar [2007] ﬁnd that individuals react to recommendations literally; they exert
buy pressure following strong buy/buy recommendations and zero pressure following hold recommendations. They also ﬁnd that individuals fail to take into account the additional distortion arising fromthe underwriting aﬃliation (see also, Mikhail, Walther, and Willis [2007]). These ﬁndings are consistent
with the functional ﬁxation hypothesis suggesting that individuals take the information they receive atface value without deeply considering its context. That is, individuals tend to not bother incurring theinformation analyzing cost.
Applying the implication of Malmendier and Shanthikumar [2007] to the individuals’ behavior in
response to the management earnings forecasts, individuals are expected to ignore the autocorrelation
structure and react to the information literally. This is because individuals tend to avoid incurring theinformation analyzing cost, and hence, are reluctant to analyze the bias contained in the management
earnings forecasts. As in Kato, Skinner, and Kunimura [2009], assuming that the news in the management
earnings forecasts is an expected future earnings change based on the forecast, individuals are likely toreact to the expected future earnings change regardless of the prior managements’ forecast bias. In
the example in Table 1, it is expected that individuals react literally to the ﬁgure of 21 billion yen,which is calculated by subtracting the current year’s earnings (279 billion yen) from the next year’s
management forecast (300 billion yen) even if the manager of the ﬁrm has provided an overly-optimistic
earnings forecast in the last year12 . Therefore, we predict that small traders’ reaction to the management
12 Indeed, the manager of Nintendo issued an overly-optimistic earnings forecast in the last year. He estimated ﬁscal year

2009’s net income to be 325 billion yen at the earnings announcement of ﬁscal year 2008. Then, he revised his earnings
forecast upward to 410 billion yen at the earnings announcement of the ﬁrst quarter of 2009, and downward to 345 and230 billion yen at the earnings announcement of the second quarter and third quarter of 2009, respectively. Eventually,
the actual earnings of ﬁscal year 2009 was 279 billion yen and fell short of the initial management forecast (i.e., 325 billion

14earnings forecasts at the earnings announcements is not associated with the optimistic bias of the priormanagement earnings forecast. Thus, our third hypothesis, stated in alternative form, is as follows:

Hypothesis 3:

The buying/selling activities of small traders in response to the management earnings
forecasts around the earnings announcements are unrelated to the optimistic bias ofmanagement earnings forecasts in the previous year.

This hypothesis is consistent with the underlying notion of Hypothesis 1. As stated in the previous
section, individuals who tend to ignore the costly predisclosure information would also not pay attention
to the previous year’s management forecasts despite the fact that the lagged bias indicates the bias in
the management forecasts issued at the time of the current year’s earnings announcements. In otherwords, they would be unwilling to incur the information gathering cost (i.e., the cost for collecting themanagement earnings forecasts issued at the earnings announcement of the previous year) and hence donot use the lagged bias when they react to the current year’s management earnings forecasts.
In contrast to the naı̈ve trading by individuals in response to the analysts’ recommendations, priorresearch ﬁnds that institutions anticipate upward the bias in stock recommendations and the additional
distortion induced by the aﬃliated analysts, and then adjust their trades to these biases. Malmendierand Shanthikumar [2007] show that institutions tend to exhibit a positive abnormal trade reaction tostrong buy recommendations, no reaction to buy recommendations, and signiﬁcant selling pressure following hold recommendations. This result suggests that institutions adjust their trade to buy and holdrecommendations downwards, since they anticipate a general upward bias in the stock recommendations.
In addition, they ﬁnd that such discounting behavior of institutions to the stock recommendations ismore pronounced when the analyst is aﬃliated with an underwriter. This result implies that institutions
are aware of the tendency of aﬃliated analysts to issue upward biased recommendations, and more significantly, discount the favorable recommendations when they are provided by aﬃliated analysts. Overall,
the results of Malmendier and Shanthikumar [2007] indicate that the trading behavior of institutions
corresponds to that of a rational investor. Institutions are willing to incur the information analyzing
cost, and then, appropriately react to analysts’ recommendations.
If we apply the implication of Malmendier and Shanthikumar [2007] to the trading behavior of institutions in response to the management earnings forecasts, then they would anticipate the autocorrelationyen) made at the earnings announcement of ﬁscal year 2008. This suggests that the initial management forecast is overly
optimistic.

15

structure, and therefore, adjust their trade reactions to the forecast biases. This is because institutions
would be willing to incur the information analyzing cost, and therefore, ﬁlter out and appropriately react
to the bias contained in the management earnings forecasts issued at the earnings announcements of thecurrent year. In the example in Table 1, institutions would be unwilling to buy the stock even if the
expected future earnings change is positive (21 billion yen) when the managers of the ﬁrm have provided
a very optimistically-biased forecast in the last year. On the other hand, for bad news forecasts (i.e., theearnings forecasts for the next year fall below the actual earnings of the current year), institutions will
engage in vigorous selling activity if the managers have provided a very optimistic forecast in the previous
year. The reasoning behind this is that institutions anticipate that the managers who have issued optimistic forecasts will again deliver poorer performance than expected. These arguments suggest that forgood (bad) news in the management earnings forecasts, large traders, represented by institutions, would
respond less positively (more negatively) to the management earnings forecasts with more optimistic bias
in the last year. We therefore develop the following directional hypothesis:

Hypothesis 4:

The buying/selling activities of large traders in response to good news (bad news)
in the management earnings forecasts around the earnings announcements are lesspositive (more negative) when the managers have issued optimistic earnings forecasts
in the previous year.

This hypothesis reﬂects the fundamental concept of Hypothesis 2. Hypothesis 2 predicts that institutions actively use costly predisclosure information. Consistent with this prediction, when institutions
trade on the current year’s management forecasts, they who understand the autocorrelation structure of
the management forecast bias should pay attention to the last year’s management earnings forecasts. Institutions will be willing to incur the information gathering cost (i.e., cost for acquiring the management
earnings forecast issued at the earnings announcements of the previous year) and then use it for anticipating the bias contained in the management earnings forecasts issued at the earnings announcementsof the current year.

16

3

Method and Data

3.1

Distinguishing between individuals and institutions

The transaction data set used in this study does not contain any information on whether a trade isinitiated by individuals or institutions. To distinguish between individuals and institutions, we use tradesize in accordance with Lee and Radhakrishna [2000], because they show the usefulness of trade size asa proxy for separating the trades initiated by individuals from those initiated by institutions. Lee andRadhakrishna [2000] and other studies (e.g., Bhattacharya [2001]; Barber, Odean, and Zhu [2009a]) use
the following criterion: the trades less than $5,000 are used as a proxy for the individuals trading. Since$1 is roughly equal to 100 Japanese yen through our sample period, we deﬁne the trades less than orequal to 500,000 Japanese yen as the ones initiated by individuals (small trades)13 .
However, this criterion is somewhat problematic for stocks with a high price. In Japan, the roundlot—the lowest number of stocks that investors can buy or sell—is diﬀerent among listed ﬁrms. As many
Japanese ﬁrms adopt 1,000 shares as the round lot, the amount necessary for investors to buy or sell thelowest number of stocks is more than 500,000 Japanese yen if the stock price is higher than 500 Japaneseyen. Because it is assured that there can be small trades no matter how high the stock price, we set an
additional criterion to identify the small trades. In sum, we deﬁne (i) a trade less than or equal to 500,000
Japanese yen or (ii) a trade of one round lot as a small trade. These low value thresholds minimize thepossibility of including the institutional investors’ activity in the small trades (see Lee [1992]).
On the other hand, the large trades mainly initiated by institutions are the ones that meet the following
criteria: (i) a trade is more than or equal to 2,000,000 Japanese yen and (ii) a trade is more than one roundlot. Lee and Radhakrishna [2000] demonstrate that in separating the two investor groups, individuals
and institutions, by a single cut-oﬀ value, the result makes very noisy. Therefore, as in prior studies, we
eliminate a buﬀer zone of medium-sized trades to improve the accuracy of trade classiﬁcation14 .
13 This criterion is also consistent with the deﬁnition of individuals as the TSE suppose. In September 2001, ﬁve Japanesestock exchanges including the TSE and the Japan Securities Dealers Association issued a joint statement for the purposeof encouraging many more individuals to participate in stock trading. The statement said that the listed ﬁrms should keepa minimum trading size, that is, the amount necessary for the investors to buy or sell the lowest number of stocks, to beless than 500,000 Japanese yen. The statement assumes that individuals are likely to trade stocks in amounts of less than
500,000 Japanese yen.
14 As one thinks that these criteria to distinguish the trades between individuals and institutions might be rather arbitrary,

we repeat the following analyses by using another criteria: a trade less (more) than or equal to 1,000,000 (4,000,000) Japanese
yen is classiﬁed as a trade initiated by small (large) traders. The results will be discussed in Section 4.4.

17

3.2

Market microstructure of the TSE and measuring the trading reaction

As in Lee [1992], we use the directional volume, that is, the order imbalance (OIB ), to explain the
trading behavior of large and small traders around the earnings announcements. To measure the OIB
of each stock, we need to classify each trade as either a buyer- or seller-initiated trade. If the earnings
announcements have information content, then good (bad) news triggers heavy buying (selling) activities,
and buy/sell OIB is strongly associated with the news in the earnings.
The previous studies in the U.S. use the Lee and Ready [1991] algorithm, i.e., the combination ofquote and tick tests to classify the trades as buys or sells (e.g., Lee [1992]). The quote test is the method
where if a trade is executed at a price above (below) the quote midpoint, it is classiﬁed as a buy (sell).
The trades at the quote midpoint are classiﬁed using the tick test—if the last price change was positive(negative), then the trade is deemed as a buy (sell).
The mechanism for trading stocks in the TSE diﬀers from that in the NYSE and the AMEX that is
managed by exchange-designated specialists. The specialists collect the public limit orders (which aremaintained in a private limit order book that is not readily available to the public), match incoming buy
and sell orders, and purchase and sell securities for their own account (Lehmann and Modest [1994]).
Contrary to the U.S. stock markets, the TSE is an order-driven market. Within continuous double
auction trading called the “Zaraba,” any trader may submit limit orders or market orders15 . All liquidity
is supplied by the traders who submit the limit orders. The lowest limit order to sell becomes the best
ask price and the highest limit order to buy becomes the best bid price. As a result of this structure, it
is extremely improbable that the executed price is diﬀerent from the last quoted ask or bid price. We
therefore use the simple quote test, in which a trade at the ask (bid) price is classiﬁed as a buy (sell). In
fact, the percentage of trades unclassiﬁed by using the quote test is no more than 2 percent of the total
trades during our sample period.

15 TSE trading takes place in two diﬀerent trading sessions. The morning session, called the “Zemba,” begins at 9:00 a.m.

and ends at 11:00 a.m., while the afternoon session, called the “Goba,” begins at 12:30 p.m. and ends at 3:00 p.m. Tradeat the beginning of each session is initiated through a single-price auction called the “Itayose.” Following this, trades occur
under the Zaraba mechanism until the session closes, at which the orders are also executed through the Itayose mechanism.
For the opening and closing trades of each trading session executed through a single-price auction, we do not specify theirdirections because of the diﬀerence in mechanism. Lehmann and Modest [1994] report that 65 to 70 percent of the total
trading volume occurs under the Zaraba mechanism for all ﬁrms regardless of their size deciles for their sample period(1991–1993). Meanwhile, we ﬁnd that the ratio of total trading volume executed under the Zaraba mechanism is from 75
to 90 percent for our sample period (1999–2006).

18

After classifying each trade as either a buyer- or seller-initiated trade by the quote test, we develop
the buy/sell imbalance measure for large and small traders in each stock to explain the trading behaviorof each type of traders around the earnings announcements. If we set the earnings announcement period
as from days k to l relative to the earnings announcement date (day 0), then the buy/sell OIB during
the period is deﬁned as follows:
∑l
∑l
z
z
Bit
− t=k Sit
OIBiz [k, l] = ∑t=k
,
∑
l
l
z
z
t=k Bit +t=k Sitzzwhere Bit
(Sit
) is the number of shares under the buyer- (seller-) initiated trades for ﬁrm i and trader

type z (z ∈ {large, small}) at day t. However, this measure does not capture unusual buying/selling
activity in response to the earnings announcement. To examine the investor-level reaction to the earnings
announcements, we should examine the association between the abnormal component of OIB during theannouncement period and the unexpected components in the earnings news. This approach is essentiallysimilar to that in prior research analyzing the association between the abnormal return around theearnings announcements and the unexpected earnings to examine the market-level reaction to the earnings
announcements. To obtain an estimate of the magnitude of the abnormal OIB (AOIB ) during theannouncement period, we normalize this measure by subtracting the average of the daily OIB during thenon-earnings announcement period and dividing by its standard deviation, separately for each type oftraders. We set the non-earnings announcement period as 100 days from days −130 to −31. The daily
OIB is deﬁned as follows:
z
OIBit
=

z
z
Bit
− Sit
z + Sz .
Bit
itFinally, for each trader group, we calculate AOIB as follows:
AOIBiz [k, l] =z
OIBiz [k, l] − OIBi,−130:−31,z
SD(OIBi,−130:−31)zzwhere OIBi,−130:−31
and SD(OIBi,−130:−31) are the average and standard deviation of the daily OIB

during the non-earnings announcement period (t = −130 to −31) for ﬁrm i and trader type z.

3.3
3.3.1

Construction of the earnings-related variables
Current earnings-related variables

Figure 1 describes the timeline that underlies the measurement of the earnings-related variables used inthis study16 . Because the investors trade on the unexpected component of earnings around the earn16 In this study, the earnings refers to the bottom-line net income.

The TSE, however, requires the ﬁrms to report theearnings from continuing operation, which approximately corresponds to the earnings before special items in the U.S., as

19

ings announcements, positive (negative) earnings surprises should trigger high buying (selling) activity.
Regarding the actual current earnings, there are two possible measures of the unexpected earnings: (1)random walk forecast error and (2) analyst forecast error. We deﬁne the random walk forecast error
as the diﬀerence between the actual current earnings and the previous year’s earnings, deﬂated by themarket value of equity that is the stock price times the number of shares outstanding as of the end ofﬁscal year t:
RWFE i,t =

ei,t − ei,t−1,
MVE i,twhere RWFE i,t is the random walk forecast error for ﬁrm i in period t, ei,t is the actual earnings for ﬁrm
i in period t, and MVE i,t is the market value of equity at the end of ﬁscal year t. Hypothesis 1 predicts
that AOIB (i.e., unusual buying/selling activity) for small traders during the earnings announcement
period is positively associated with RWFE .
In addition to RWFE , we calculate another possible measure of the unexpected current earnings
surprise, the analyst forecast error. We deﬁne it as the diﬀerence between the actual current earnings
and the latest analyst forecast, deﬂated by the market value of equity as of the end of ﬁscal year t:
AFE i,t =ei,t − afi,t,
MVE i,twhere AFE i,t is the analyst forecast error for ﬁrm i in period t and afi,t is the most recent analyst forecast
for year t before the earnings announcement. If Hypothesis 2 is true, then AOIB for large traders during
the earnings announcement period would be positively associated with AFE .

3.3.2

Management earnings forecast-related variables

A questionnaire survey of individuals conducted by the TSE yields that more than two-thirds of individuals use the management earnings forecasts reported in earnings reports (TSE [2006c]). In addition,
an interview survey of institutions (TSE [2006b]) indicates that they regard the management earnings
forecasts as essential information among the several items in the earnings reports and use them as reference. These surveys suggest the importance of the management earnings forecasts for both individuals
and institutions. In other words, the management earnings forecasts are likely to be informative forboth types of traders. Our Hypotheses 3 and 4 predict that individuals and institutions use the manage-

well as the net income. Thus, we replicate all the following analyses by using the earnings from continuing operation as analternative deﬁnition of earnings, but our study’s inferences from the analyses remain unchanged. Therefore, our results
are not sensitive to the deﬁnition of earnings.

20

ment earnings forecasts diﬀerently, and therefore, trade diﬀerently; however, both use it at the earnings
announcement time.
Hypothesis 3 implies that small traders do not properly unscramble the management earnings forecasts, and therefore, ignore the autocorrelation structure of the management forecast bias. If this hypothesis is correct, we should observe a positive relation between AOIB for small traders and the unexpectedcomponent of management earnings forecast, irrespective of the bias of the previous year’s management
earnings forecast. Following Kato, Skinner, and Kunimura [2009], we deﬁne the expected future earnings
changes as unexpected component of management earnings forecasts. We calculate it as follows:f eti,t+1 − ei,t,
MVE i,t

EFEC i,t =where EFEC i,t is the expected future earnings change for ﬁrm i in period t and f eti,t+1 is the management
forecast of period t + 1 earnings made at the earnings announcement of period t. We deﬁne the previous
year’s management forecast bias in the initial earnings forecast (BIAS t−1 ) as the diﬀerence between theinitial management earnings forecast of period t made at the earnings announcement of period t − 1 andthe actual current earnings, deﬂated by the market value of equity as of the end of ﬁscal year t:
BIAS t−1 =f et−1i,t − ei,t.
MVEi,t

The positive (negative) value of this variable implies that the initial management earnings forecast
issued in the last year is optimistic (pessimistic). Hypothesis 3 predicts that AOIB for small traders during
the announcement period is positively associated with the expected future earnings changes regardless of
BIAS t−1 .
In contrast to naı̈ve trading by individuals in Hypothesis 3, Hypothesis 4 predicts that institutions
behave in a rational manner. They understand the autocorrelation structure of the management forecast
bias, and then, trade based on the management earnings forecasts after making the necessary adjustment
to the bias. It is expected that although AOIB for large traders is positively associated with the expected
future earnings change, such an association would be weaker (stronger) for the ﬁrms with a larger BIAS t−1
if EFEC is positive (negative).

3.4

Sample Selection

We obtained the trade and quote data from the Nikkei NEEDS TICK database. This database is basicallythe same as the Trade and Quote (TAQ), which is provided by the NYSE and used by several U.S. studies.
We also obtained several accounting data reported at the time of earnings announcement from the Nikkei
21

NEEDS-BULK management forecasts database, and other accounting data from the Nikkei NEEDSFinancialQUEST. Market data, such as market prices, number of shares outstanding, and stock returns,
is from the Nikkei daily returns database17 . To calculate the analyst forecast error, we use the Nikkei daily
ﬁnancials database. This database includes a history of earnings forecasts estimated by analysts aﬃliatedwith the Nihon Keizai Shimbun regarding the individual ﬁrms on a speciﬁc trading day18 . The analyst
earnings forecasts are revised frequently during the ﬁscal year so as to reﬂect the updated management
forecasts, changes in economic circumstances, and so on in a timely fashion19 .
This study includes annual earnings announcements from 1999 to 2006 for the ﬁrms listing on theTSE. The sample observation meets the following sample selection criteria:
1. The previous year’s and current year’s actual earnings data and management earnings forecasts
data for the next year are available. To calculate the previous year’s management forecast bias, theinitial management earnings forecasts data made at the announcement of the last year’s ﬁnancial
results is also required.
2. The sample includes the observations that allow us to estimate the abnormal returns around theearnings announcements to examine not only the investor-level reactions but also the marketlevel reactions to the earnings announcement. We use the size-adjusted abnormal returns for ouranalyses20 .
3. We require a minimum of 20-day trading data during the non-earnings announcements period to
17 The Nikkei daily returns database basically corresponds to the CRSP in the U.S.
18 It may not be appropriate to use the I/B/E/S or the Value Line database for calculating the analyst forecast errors,

because they tend to include the forecasts only for larger ﬁrms. In contrast, the Nikkei daily ﬁnancials database includes
the forecasts for not only large ﬁrms but also small ﬁrms, because the analysts aﬃliated with the Nihon Keizai Shimbun
cover more Japanese listed ﬁrms. To minimize the sample selection bias arising from the availability of analyst forecasts,
we use the Nikkei daily ﬁnancials database.
19 In the case of Nintendo, for example, the analyst forecast was updated six times during the ﬁscal year 2009 so that it

reﬂects the most recent management earnings forecast or other information.
20 We calculate the abnormal returns using the following procedure.

We begin by constructing 10 portfolios. In the

September of each year t, all TSE stocks on the Nikkei daily stock return database are ranked on the basis of the market
capitalization, which is measured as stock price times the number of shares outstanding for a stock. These decile breakpoints
for the market capitalization are used to allocate all stocks listed on the TSE and other stock exchanges to the market
capitalization deciles. In this way, we construct the 10 portfolios and calculate the value-weighted returns on the portfolios
from the October of year t to the September of year t + 1. The abnormal return on a particular stock is then calculated bysubtracting the value-weighted portfolio’s return from the individual stock’s return.

22

avoid the problems arising from thinly traded issues and to calculate the appropriate abnormal
order imbalance around the earnings announcements.
4. We also exclude the observations that do not have several ﬁrm characteristic-related variables
required in the later analysis. We describe these variables and their measurements in more detailin Section 4.2.
These criteria yield the ﬁnal sample of 10,258 annual earnings announcements from 1999 to 2006 with
the number of observations per year ranging from 978 in 1999 to 1,734 in 2005.

3.5

Descriptive statistics

Panel A of Table 2 reports the descriptive statistics on the news in the current earnings and management
earnings forecast and the previous year’s management forecast bias in the initial earnings forecast. The
25th percentile of EFEC is non-negative. This suggests that less than 25% of the managers estimate
negative future earnings changes, and that the managers in Japanese ﬁrms are unwilling to report badnews forecasts at the time of earnings announcement. The mean and median of BIAS t−1 are both positive.
This evidence indicates that the management earnings forecasts, on average, have a systematically upwardbias, which is consistent with Herrmann, Inoue, and Thomas [2003] and Kato, Skinner, and Kunimura[2009]. In our sample, 55.0% of the observations have optimistically biased earnings forecasts (i.e.,
0 < BIAS t−1 ).
Panel B of Table 2 reports the mean and median absolute value of RWFE and AFE to conﬁrm theanalyst forecast’s superiority relative to the simplistic time-series model forecast in our sample. The mean
(median) absolute AFE is 0.014 (0.002) while the absolute RWFE is 0.092 (0.015). The absolute RWFE
is signiﬁcantly larger than the absolute AFE (t = 19.2; z = 79.2). The untabulated results yields thatthe analyst forecast tends to superior for each sample year (1999–2006). This result indicates that theanalyst earnings forecast is a more superior predictor than the expected earnings based on the random
walk model in Japan, which is also consistent with the U.S. evidence (e.g., Brown and Rozeﬀ [1978];Brown, Hagerman, Griﬃn, and Zmijewski [1987]).

3.6

Autocorrelation structure of the management forecast bias

Kato, Skinner, and Kunimura [2009] ﬁnd that the sign of the bias in the initial management earnings
forecast is dependent on the sign of the lagged bias. Shimizu [2007] shows that the magnitude of the bias23

is positively autocorrelated with the magnitude of the lagged bias. These studies indicate that in Japan,
the bias in the initial management earnings forecast has a strong autocorrelation structure in term ofboth its sign and magnitude. In this section, we conﬁrm the autocorrelation structure for our sample. For
this analysis, we use 9,945 observations for which BIASt is available in our sample, and then examine theautocorrelation between the previous year’s bias (i.e., BIAS t−1 ) and the current year’s bias (i.e., BIAS t ).
Panel A of Table 3 reports the contingency table of the signs of BIAS t−1 and BIAS t . The tableindicates that 58.5% of the ﬁrms with a negative forecast error (i.e., a pessimistic forecast) in the lastyear issue a pessimistic forecast again this year, and that 63.3% of the ﬁrms with a positive forecast error(i.e., an optimistic forecast) in the last year issue an optimistic forecast again this year. We can easily
reject the null hypothesis that the signs of BIAS t−1 and BIAS t are independent (χ2 = 472.74, p < 0.01).
This means that the sign of the forecast bias in this year depends on that in the last year.
Panel B of Table 3 shows the mean and median of BIAS t as well as the percentage of observations
with a positive forecast error for the decile portfolios formed by BIAS t−1 to examine whether the bias
has an autocorrelation structure in terms of its magnitude. To construct this table, we sort the sample
ﬁrms into deciles based on their annual BAIS t−1 rank. The mean of BIAS t for all deciles is positive,
which reﬂects that the managers in Japan, on average, bias their forecasts upward. The means of BIAS t
are 0.041 and 0.190 for the lowest decile and highest decile, respectively. The diﬀerence is statisticallysigniﬁcant (t-stat. = 5.09, p < 0.01), while the mean value does not increase monotonically between thelowest and highest deciles. On the other hand, the median is monotonically increasing across the deciles.
The median of BIAS t for the highest decile is 0.015 compared to −0.003 in the lowest decile, and a
Wilcoxon rank-sum test rejects the null of equal medians at the 1% level. These results suggest that thedegree of the previous year’s forecast bias can be a signal for the bias in the current year’s management
earnings forecast.
Overall, the results in Table 3 indicate that the bias in the initial management forecast has an
autocorrelation structure in terms of both its sign and magnitude for our sample. Therefore, if institutions
realize that BAIS t is positively associated with the lagged bias (i.e., BIAS t−1 ), they would take into
account the degree of BIAS t−1 and adjust their trading behavior in response to the management earnings
forecast made at the time of earnings announcement of ﬁscal year t. On the other hand, individuals who
are unlikely to realize such an autocorrelation structure would trade on the initial management forecast
literally.

24

4

Empirical Results

4.1

Market-level reaction to the earnings announcement

We start by specifying the earnings announcement period for our empirical analyses. To specify theperiod, we compare the abnormal returns around the earnings announcement date between the ﬁrms
releasing extremely good news and the ones releasing extremely bad news. We deﬁne the extremely
good (bad) news as the earnings announcements in which RWFE , AFE , and EFEC are all non-negative(negative). In all, 2,903 (257) observations are extracted as extremely good (bad) news sample. Figure
2 shows the behavior of the mean size-adjusted return for each extreme sample over the period from 5days before to 5 days after the earnings announcement date (day 0). Obviously, the signiﬁcant responseto the earnings announcements concentrates on days 0 and +1 (i.e., the day on which the summaryof the earnings report is published in a major ﬁnancial press). We observe strongly positive (negative)
abnormal returns for extremely good (bad) news sample on both days. The diﬀerence in the abnormal
returns between these two samples is statistically signiﬁcant at the 1% level or better on both days,
while it is not signiﬁcant on the other days. These results suggest that the stock market reacts strongly
to the earnings announcements on days 0 and +1. Therefore, we set days 0 and +1 as the earnings
announcement period, and then, examine the trading behavior of individuals and institutions in thisperiod.
Next, we investigate in detail the market-level reaction to the simultaneously announced current
earnings and management earnings forecasts before examining the investor-level reaction. The marketlevel reaction is measured as the size-adjusted abnormal return during the earnings announcement period.
Table 4 reports the results of the univariate relationship between the news variables (i.e., the two news
variables for current earnings, RWFE and AF E, and one news variable for the management earnings
forecasts, EFEC ) and the cumulative abnormal return (CAR) over the earnings announcement period.
To construct this table, we independently sort the sample ﬁrms into quintiles based on the annual rank ofeach variable, and then, compute the average CAR[0, +1] for each portfolio. Panel A of Table 4 indicatesthat the average CAR[0, +1] does not relate to RWFE . On the other hand, Panel B of Table 4 shows that
CAR[0, +1] is monotonically increasing across AFE quintiles. These results suggest that the market, on
average, tends to respond more strongly to AFE than RWFE . With regard to the market reaction to thenews in the management forecasts presented in Panel C of Table 4, the average CAR[0, +1] of the lowest
EFEC quintiles is −1.40% while that of the highest EFEC quintiles is 1.95%. The diﬀerence of 3.35% is25statistically signiﬁcant at the 1% level. This result suggests that the managers’ earnings forecasts convey
useful information to the stock market.
In sum, both AFE and EFEC are the unexpected components of the current earnings and management earnings forecasts, respectively, for the market, and thus, the market reaction to the earnings
announcements is signiﬁcantly associated with these variables. However, this analysis ignores the potential impact of the correlations among the three news variables. Indeed, the correlation matrix documented
in Panel A of Table 5 indicates that EFEC is strongly negatively correlated with both RWFE and AFE .
More importantly, the univariate analysis is not able to examine whether the stock market response to
the management earnings forecasts anticipates the autocorrelation structure of the management forecasts
bias. Therefore, we estimate the following multivariate regression to address these issues:
CAR[0, +1]i,t= α0 +

2006∑

ατ −1999 YD τ,i,t + β1 RWFE i,t + β2 AFE i,t + β3 EFEC i,t + β4 BIAS deci,t−1τ =2000dec+β5 EFEC i,t × GN i,t × BIAS deci,t−1 + β6 EFEC i,t × BN i,t × BIAS i,t−1 + µi,t ,(1)

where YD τ represents the annual indicator variable for year τ . We include an interaction term of
the EFEC and BIAS t−1 in this model to examine whether the market anticipates the autocorrelation
structure of management forecasts bias. To allow the coeﬃcients on the good and bad news forecasts todec
be separately estimated, two interaction terms, EFEC × GN × BIAS dec
t−1 and EFEC × BN × BIAS t−1 , areincluded in the model. GN (BN ) is an indicator variable set equal to one for the ﬁrm with 0 ≤ EFEC(EFEC < 0), and zero otherwise. For BIAS t−1 , we do not use the raw value, but the annual decile rank,scaled to range between 0 and 1, to mitigate the eﬀect of outliers and to facilitate the interpretation of
the coeﬃcients on the interaction terms. In our regression model, this coding scheme has the advantagethat the coeﬃcient on EFEC (i.e., β3 ) can be interpreted as the slope coeﬃcient for the ﬁrms whose
management issued the most pessimistic earnings forecasts at the earnings announcement of last year.
The sum of the coeﬃcients on EFEC and EFEC × GN × BIAS dec
t−1 (i.e., β3 + β5 ) can be interpreted
as the slope coeﬃcient for the ﬁrms that release good news forecasts (i.e., 0 ≤ EFEC ) at the earnings
announcement of current year and issued the most optimistic earnings forecasts at the announcement oflast year. β3 + β6 can also be interpreted as the slope coeﬃcient for the ﬁrms with bad news forecasts
for the next year (EFEC < 0) and issuing the most optimistic forecasts at the announcement of lastyear. As long as the stock prices reﬂect the autocorrelation structure of the management forecast bias,
the coeﬃcient on EFEC × GN × BIAS dec
t−1 should be negative, because the stock market anticipates the
optimistically biased forecasts to be issued again, and discounts it. On the other hand, the coeﬃcient on

26

EFEC × BN × BIAS dec
t−1 should be positive, as the ﬁrms which issued overly optimistic forecasts in thelast year will repeatedly exhibit poorer performance than the initial management forecasts.
Panel B of Table 5 reports the OLS coeﬃcient estimates and White [1980] heteroscedasticity consistent t-statistics in parentheses21 . Regression 1 shows the market reaction to the earnings announcements
without considering the prior management forecast bias. The coeﬃcient on EFEC is positive and signiﬁcant at the 1% level. The result of this simple model conﬁrms the ﬁnding of Kato, Skinner, and Kunimura[2009] that the initial management earnings forecast is informative to the market in spite of the general
optimistic bias.
The conditional test is reported in Regression 2. For the market reaction to the current earnings,
AFE is positively associated with CAR[0, +1] (coeﬃcient = 0.142, t-stat. = 3.61), which is consistent
with the above univariate analysis. Surprisingly, we also ﬁnd that RWFE is positively associated with
CAR[0, +1] (coeﬃcient = 0.020, t-stat. = 3.70), although its magnitude is statistically smaller than thecoeﬃcient on AFE (F -stat. = 9.57, p < 0.01). This result indicates that the stock prices partially reﬂect
the naı̈ve earnings expectations and that some investors trade on “stale” earnings news.
In regard to the market reaction to the management earnings forecast, we ﬁnd that the coeﬃcienton EFEC is positive (= 0.293) and statistically signiﬁcant (p < 0.01) while the coeﬃcient on EFEC ×
GN × BIAS dec
t−1 is negative (= −0.262) and statistically signiﬁcant (p < 0.01). This result indicates thatfor the ﬁrms with good news forecasts, the market responds more positively to the management forecastswith higher anticipated pessimism and reponds less positively to the management forecasts with higher
anticipated optimism. On the other hand, the coeﬃcient on EFEC × BN × BIAS dec
t−1 is positive as
expected, but not signiﬁcantly diﬀerent from zero. At the ﬁrst glance, this result suggests that themarket fails to reﬂect the autocorrelation structure of the management forecast bias for the ﬁrms withbad news forecasts. However, this market reaction might be rational given the high credibility of bad
news forecasts. Several studies in the U.S. argue that the bad news forecasts are inherently more credible
than the good news forecasts (e.g., Jennings [1987]; Hutton, Miller, and Skinner [2003]). Rogers and

21 We use the following outlier treatment for the regression estimate throughout this paper:

we estimate the regression

models within the 1st and 99th percentiles of the annual distribution for the respective earnings-related variables (i.e.,
RWFE , AFE , and EFEC ) and the return-related variable (i.e., CAR) each year to mitigate the inﬂuence of outliers. On
the other hand, we do not care about the extreme value of AOIB, because it is a standardized variable. As a robustness
check, we reestimate each regression by using an alternative outlier treatment, that is, the earnings-related variables andthe return-related variable are winsorized at the 1% and 99% levels, respectively. The pattern of the estimated coeﬃcients
is similar in all analyses under both outlier treatments. Therefore, our results are robust to the outlier treatment.

27

Stocken [2005] provide weak evidence that the stock market reacts to the bad news forecasts regardless of
how predicted the optimistic bias in them is, because the market reckons that the bad news forecasts are
more credible (i.e., less optimistically biased) than the good news forecasts22 . As such, we next examine
whether the bad news forecasts are less optimistically biased than the good news forecasts to conﬁrm
that the interpretation proposed in the U.S. research is applicable to the market reaction to the bad news
forecasts in Japan.
Panel A of Table 6 reports a contingency table of the management forecast news (EFEC ) and theactual forecast errors in the management earnings forecast (i.e., BIAS t ) based on the 9,945 observations
with BIAS t in our sample. According to the table, the null hypothesis that the bias in the forecast is
independent of the management forecast news is rejected (χ2 = 67.22, p < 0.01). This means that thesign of BIAS t is associated with the content of news. Panel B of Table 6 reports the mean and medianof BIAS t for the portfolios formed by the sign of the management forecast news to test whether themagnitude of BIAS t is associated with the content of the news. Although the mean diﬀerence between
the ﬁrms with good news forecasts and the ones with bad news forecasts is insigniﬁcant, the median forthe ﬁrms with bad news is signiﬁcantly smaller than that for the ﬁrms with good news (p < 0.01)23 .
Overall, the results of Table 6 indicate that the management forecast news are associated with both thesign and magnitude of the optimistic bias in the forecast. Stated diﬀerently, the bad news forecasts areless optimistically biased than the good news forecasts. Therefore, our result that the market reaction to
the bad news forecasts is unrelated to the prior management forecast bias holds, not because the market
does not ignore the autocorrelation structure of the management forecast bias but because the market
recognizes the bad news forecasts as being inherently credible. As the bad news forecasts are likely to beless optimistically biased than the good news forecasts, the market takes the bad news forecasts at facevalue even when the previous year’s management forecasts are optimistically distorted.

22 Actually, however, Rogers and Stocken [2005] do not ﬁnd that the bad news forecasts is less biased than the good news

forecasts for their sample. In addition, extant literatures in the U.S. (e.g., Jennings [1987]), claiming that the bad news
forecast is more credible than the good news forecast, have not established whether the bad news forecast is less biased
than the good news forecast.
23 The reason why the mean for the ﬁrms with good news forecasts is smaller than the mean for the ones with badnews forecasts, although the diﬀerence is insigniﬁcant, is due to the small number of extreme observations for which the
management forecast errors exceed the market value of equity (i.e., 1 < BIAS t ). The mean for the ﬁrms with bad news
forecasts is smaller than the mean for the ones with good news forecasts (t-stat. = 2.92, p < 0.01) when we exclude suchextreme observations for this analysis.

28

4.2

Abnormal order imbalance measure and firm characteristics

To capture the unusual buying/selling activity in response to the earnings announcements, we construct
an abnormal order imbalance measure (i.e., AOIB ). If we observe the correlation between our AOIB
measure and certain ﬁrm characteristics, not controlling them in our analyses could lead to erroneous
conclusions. Thus, before testing the investor-level reaction to the earnings announcements, it is useful
to examine whether or not the AOIB measure is correlated with certain ﬁrm characteristics.
We choose the following six ﬁrm characteristics that are likely to be linked to the trading behavior ofsmall and large traders.
• Market value of equity (MVE ), book-to-market ratio (B /M ), and standard deviation of daily
returns (STDRET )—if the trading behavior of individuals and/or institutions is dependent of theirrisk preferences, AOIB should be correlated with these three variables. MVE and B /M are wellknown risk factors. STDRET captures the total risk of the stock. MVE is deﬁned as the market
value of equity as of the end of ﬁscal year t. We deﬁne B /M as the book value of equity at theend of ﬁscal year t − 1 divided by the market value of equity six months after the end of ﬁscal yeart − 1. STDRET is deﬁned as the standard deviation of the daily returns during the ﬁscal year t.
• Turnover (TURNOVER) and bid-ask spread (SPREAD)—Several studies ﬁnd that institutions prefer to hold liquid stocks (e.g., Gompers and Metrick [2001]). In addition, Amihud and Mendelson[1986] demonstrate that the transaction costs lead to an investor clientele eﬀect whereby the investors with longer holding periods select more illiquid stocks. Investors’ demand, therefore, might
be correlated with the liquidity of a stock. We use TURNOVER and SPREAD as the proxy for
liquidity. We deﬁne TURNOVER as the average of daily turnover, which is deﬁned as the daily
number of shares traded dividend by daily number of shares outstanding, over the ﬁscal year t.
SPREAD is deﬁned as average daily equal-weighted relative spread over the ﬁscal year t24 . The
relative bid-ask spread is deﬁned as the diﬀerence between the ask and bid price in eﬀect at thetime of the transaction, divided by the mid point of the ask and bid prices.
• Dividend yield (YIELD)—Allen, Bernardo, and Welch [2000] provide a theoretical basis for theprediction that high dividends attract institutions, although Grinstein and Michaely [2005] do notsupport this prediction in the U.S. Graham and Kumar [2006] ﬁnd that the low-income investors
24 We require a minimum of 50-trading day data for calculating STDRET , TURNOVER, and SPREAD to obtain reliablemeasures. As described in section 3.4, we exclude the observations not meeting this criterion from our sample.

29

among the retail investors disproportionally own stocks with high-dividend yield. These studies
suggest that the dividend payments could aﬀect the trading behavior of individuals and/or institutions. We deﬁne YIELD as the average dividend yield at the end of month over the ﬁscal yeart.
Panel A of Table 7 reports the results of the univariate relationship between AOIB for small tradersduring the earnings announcement period and the various ﬁrm characteristics. To construct this table,
we form ﬁve portfolios based on the annual AOIB small [0, +1] rank. The lowest (highest) quintile of
AOIB small [0, +1] consist of stocks with strong small-trader’s selling (buying) pressure. We ﬁnd that
all characteristics, except for B /M , are signiﬁcantly correlated with AOIB small [0, +1]. The diﬀerence
between the two extreme quintiles (Q5−Q1) is signiﬁcant at the 1% level for these characteristics. Small
traders tend to abnormally buy stocks with larger MVE , higher STDRET , higher TURNOVER, higher
SPREAD, and lower YIELD during the announcement period.
The results of univariate relationship between AOIB for large traders and the various ﬁrm characteristics are reported in Panel B of Table 7. Contrary to the trading behavior of small traders, that oflarge traders has not much to do with the ﬁrm characteristics. Only B/M is signiﬁcantly correlated with
AOIB large [0, +1] (p < 0.05). This implies that large traders exhibit more abnormal buying for growthstocks than for value stocks during the announcement period.
Overall, the results in Table 7 suggest that our AOIB measure may partially reﬂect the speciﬁc ﬁrm
characteristics. In other words, it remains possible that the AOIB measure captures not only the unusual
buy/sell activity in response to the earnings announcements but also the ﬁrm characteristics. Therefore,
we control for these six characteristics described when we examine the investor-level reaction to theearnings announcements in the multivariate setting.

4.3

Investor-level reaction to the earnings announcement

In this section, we examine the trading behavior of individuals and institutions around the earnings
announcements. Table 8 shows the results of the univariate relationship between the news variables and
AOIB for each type of traders during the announcement period. Panel A of Table 8 reports the meanof AOIB for each portfolios classiﬁed by RWFE . We can point out that there is a positive relationshipbetween AOIB small [0, +1] and RWFE . The average AOIB small [0, +1] is −0.016 for the lowest RWFE
quintile (Q1) and 0.075 for the highest RWFE quintile (Q5). The mean diﬀerence between Q5 andQ1 (Q5−Q1) is signiﬁcantly positive (p < 0.01). Moreover, the sign of AOIB small [0, +1] appears to30

correspond to the sign of RWFE . The average AOIB small [0, +1] for Q1 and Q2 (Q3 to Q5) portfolios,
which have a negative (positive) mean RWFE , is also negative (positive). This result suggests that
individuals tend to abnormally buy (sell) stocks with positive (negative) RWFE . Contrary to the trading
activity of individuals in response to RWFE , there is no signiﬁcant relationship between AOIB large [0, +1]
and RWFE .
Panel B of Table 8 reports the mean AOIB for each portfolio classiﬁed by another news variable forcurrent earnings, AFE . The average AOIB small [0, +1] is not related to AFE quintiles. On the other hand,
there is a positive relationship between AOIB large [0, +1] and AFE while the sign of AOIB large [0, +1] is
not consistent with the sign of AFE . The mean of AOIB large [0, +1] for the lowest and highest quintiles
is 0.058 and 0.163, respectively. The diﬀerence in AOIB large [0, +1] between the two extreme portfolios(Q5−Q1) is positive and signiﬁcant (p < 0.01). This result indicates that institutions tend to exhibit
more (less) positively unusual buying activity to a positive (negative) AFE during the announcement
period.
In sum, regarding the investor-level reaction to the current earnings, Panels A and B of Table 8
indicate that there is a diﬀerence in the trading behavior in response to the current earnings between
individuals and institutions. We ﬁnd in the univariate test that the individuals’ (institutions’) trading
behavior is more highly sensitive to RWFE (AFE ) than AFE (RWFE ), which is consistent with our
Hypothesis 1 (Hypothesis 2).
With regard to their trading behavior in response to the news in the management earnings forecasts(i.e., EFEC ), the relationship between AOIB for each trade stratum and EFEC is reported in Panel
C of Table 8. The mean of AOIB small [0, +1] does not increase monotonically across EFEC quintiles.
The diﬀerence between the two extreme portfolios is positive (0.016) but insigniﬁcant. This means that
abnormal buy/sell trading activity of individuals is not correlated with EFEC in the univariate analysis.
In contrast, AOIB large [0, +1] is strongly related to EFEC in terms of both the sign and the magnitude.
In keeping with the sign of average EFEC , the average AOIB large [0, +1] for the Q1 quintile (Q2 to Q5
quintiles) is negative (positive). In addition, the mean of AOIB large [0, +1] increases monotonically across
EFEC quintiles, with the diﬀerence between the lowest and highest quintiles being 0.200 (signiﬁcantly
diﬀerent from zero at the 1% level). This result indicates that the institutions’ abnormal buying pressureduring the announcement period increases in EFEC .
Overall, our result in Panel C of Table 8 shows that sorting by EFEC have signiﬁcant explanatory
power for the unusual buy/sell trading behavior of institutions, but has no explanatory power for that of31

individuals. However, these univariate tests do not accurately describe how individuals and institutions
respond to the news in the earnings announcements. The result that AOIB small [0, +1] is not correlatedwith EFEC might be due to EFEC being highly negatively correlated with RWFE . (see the correlation
matrix in Table 5). More importantly, our concern whether or not individuals and institutions respondto EFEC anticipating the autocorrelation structure of management forecast bias remains unsolved in theabove univariate analysis. Therefore, we next conduct a multivariate analysis to test our hypotheses.
We estimate the following regressions separately for individuals and institutions to examine theirtrading behavior in response to the earnings announcements:
AOIB small[0, +1]
i,t

=

α0 +

2006
X

ατ −1999 Y Dτ,i,t + β1 RWFE i,t + β2 AFE i,t + β3 EFEC i,tτ =2000
dec
dec+β4 BIAS deci,t−1 + β5 EFEC i,t × GN i,t × BIAS i,t−1 + β6 EFEC i,t × BN i,t × BIAS i,t−1+β7 AOIB small[−30, −1] + β8 AOIB large[−30, −1] + Control variables + εi,ti,ti,tAOIB large[0, +1]
i,t

=

δ0 +

2006
X

(2a)

δτ −1999 Y Dτ,i,t + γ1 RWFE i,t + γ2 AFE i,t + γ3 EFEC i,tτ =2000dec
dec+γ4 BIAS deci,t−1 + γ5 EFEC i,t × GN i,t × BIAS i,t−1 + γ6 EFEC i,t × BN i,t × BIAS i,t−1+γ7 AOIB small[−30, −1] + γ8 AOIB large[−30, −1] + Control variables + υi,t .
i,t
i,t

(2b)

According to the previous studies (e.g., Bhattacharya [2001]; Mikhail, Walther, and Willis [2007]), we set
up two separate equations for small and large traders. In estimating these equations, we use seemingly
unrelated regression (SUR) to improve the estimation eﬃciency, and compare the parameter estimatesacross the two equations.
The idea of these equations is basically similar to Eq. (1) for examining the market-level reaction to
the earnings announcements, but there are three diﬀerences between Eq. (1) and these equations. The
ﬁrst is the dependent variable: we use AOIB small [0, +1] and AOIB large [0, +1] as the dependent variables
for each equation, respectively, to examine the investor-level reaction to the earnings announcements.
The second is that the abnormal order imbalance during the pre-announcement period (i.e., day −30 today −1) for both the trade strata are included in these equations. Barber, Odean, and Zhu [2009a,b]ﬁnd that the individuals’ trading is highly persistent and that individuals tend to buy or sell the same
stocks one month as they did the previous month. Campbell, Ramadorai, and Schwartz [2009] showthat the daily institutional trades are highly persistent. In addition, Chordia and Subrahmanyam [2004]
ﬁnd that the daily total OIB of individual stocks is autocorrelated with the lagged total OIB . We
then add AOIB small [−30, −1] and AOIB large [−30, −1] in these equations to control for their eﬀects on
AOIB during the announcement period. The third is that the other control variables are also includedin these equations. As shown in Section 4.2, our AOIB measure may partially reﬂect the speciﬁc ﬁrm
32characteristics. Thus, we include the six variables (i.e., MVE , B /M , STDRET , TURNOVER, SPREAD,
and YIELD) described in Section 4.2 in these equations. To minimize the inﬂuence of the extreme values,
we use the natural log transformation of these variables except for YIELD.
Table 9 reports the estimation results of these equations. Regression 1 shows the investor-level reaction to the earnings announcements without considering the prior management forecast bias. For small
traders, the coeﬃcient on RWFE is positive (= 0.348) and statistically signiﬁcant (p < 0.01) while thecoeﬃcient on AFE is positive (= 0.329) but insigniﬁcant. In contrast, for large traders, the coeﬃcient on
AFE is positive (= 1.107) and statistically signiﬁcant (p < 0.01) while the coeﬃcient on RWFE is positive(= 0.106) but insigniﬁcant. These results indicate that, as Hypotheses 1 and 2 predict, the individuals’ (institutions’) abnormal buying activity during the announcement period increases with RWFE (AFE ), butdoes not increase with AFE (RWFE ). Regarding their trading behavior in response to management earnings forecasts, AOIB large [0, +1] is positively associated with EFEC , which is consistent with the resultof the univariate analysis. On the other hand, inconsistent with the univariate result, AOIB small [0, +1]
is also positively associated with EFEC after controlling for the news in the current earnings and AOIB
during the pre-announcement period. These results, being consistent with Hypotheses 3 and 4, indicate that the management earnings forecast is useful information for both individuals and institutionsin the sense that the information leads both traders to revise their expectations, and thus, triggers theirtrading activity. Turning to control variables, we ﬁnd that there is a signiﬁcantly positive coeﬃcient on
AOIB small[−30, −1] (= 0.537, p < 0.01) and a signiﬁcantly negative coeﬃcient on AOIB large[−30, −1]i,ti,t(= −0.094, p < 0.01) in the AOIB small [0, +1] equation. In the AOIB large [0, +1] equation, we ﬁnd thatthe coeﬃcient on AOIB large[−30, −1] is positive (= 0.391) and signiﬁcant (p < 0.01) while the coeﬃcienti,ton AOIB small[−30, −1] is not signiﬁcantly diﬀerent from zero. Overall, these results suggest that duri,t
ing the announcement period, small traders tend to unusually buy (sell) stocks with high small-traders’(large-traders’) buying pressure during the pre-announcement period. On the other hand, during theannouncement period, large traders are likely to unusually heavily buy the stocks they bought during the
pre-announcement period.
The investor-level reaction to the earnings announcements considering the previous year’s management
forecast bias is shown in the Regression 2 of Table 9. With regard to the investor-level reaction to thecurrent earnings, the pattern of the estimated coeﬃcients in Regression 2 is similar to that in Regression
1. The coeﬃcient on RWFE (AFE ) in the AOIB small [0, +1] (AOIB large [0, +1]) equation is statistically
positive while the coeﬃcient on AFE (RWFE ) in the AOIB small [0, +1] (AOIB large [0, +1]) equation is33

not signiﬁcantly diﬀerent from zero25 . These results for small and large traders are consistent with
Hypotheses 1 and 2, respectively.
With regard to the investor-level reaction to the management earnings forecasts, we observe thatthe coeﬃcients on EFEC in the AOIB small [0, +1] and AOIB large [0, +1] equations are 0.802 (p < 0.01)
and 1.736 (p < 0.01), respectively. These results indicate that the abnormal buying activity of bothindividuals and institutions increases with EFEC for the ﬁrms that issued the most pessimistic earnings
forecasts in the last year. As expected, the results of the interaction terms of EFEC × GN × BIAS dec
t−1
small
and EFEC × BN × BIAS dec[0, +1] equation show that both are insigniﬁcantly diﬀerent
t−1 in the AOIB

from zero. These results imply that individuals do not change their trading behavior depending on theprior management forecast bias, which is consistent with Hypothesis 3.
large
On the other hand, we ﬁnd that the interaction term of EFEC ×GN ×BIAS dec[0, +1]t−1 in the AOIB

equation is negative (= −1.634) and signiﬁcant (p < 0.01). The coeﬃcients on EFEC and EFEC × GN ×
BIAS dec
t−1 imply that for the good news forecasts, a positive association between the management earnings
forecasts and the abnormal buying/selling activity of institutions is weaker for the ﬁrms that issued more
optimistic forecasts at the earnings announcement of the last year, which is consistent with Hypothesis
4. For the bad news forecasts, however, Hypothesis 4 is not always true. Hypothesis 4 predicts that thecoeﬃcient of the interaction term, EFEC ×BN ×BIAS dec
t−1 , should be signiﬁcantly positive, but the resultshows that the coeﬃcient is positive (= 1.045) but insigniﬁcant. Therefore, for the bad news forecasts,
the abnormal buying/selling activity of institutions is positively associated with EFEC independent of
the prior management forecast bias. These institutions’ trading patterns in response to the management
earnings forecasts correspond to the movement in the stock returns during the announcement period
described in Section 4.1. A possible explanation is that the bad news forecasts are likely to be less
optimistically biased than the good news forecasts. Large traders realize such a tendency of the bad news
forecasts, and therefore, do not change their trading behavior in response to the management earnings
forecasts regardless of whether or not the managers issued optimistically biased forecasts last year. In
sum, whether or not institutions adjust for the autocorrelation structure of the forecast bias depends onthe content of the management forecasts news; for the good news forecasts, they adjust their trading
25 We can also point out that the coeﬃcient on RWFE in the AOIB small [0, +1] equation is signiﬁcantly larger than thatin the AOIB large [0, +1] equation (χ2 = 5.68, p < 0.05). This indicates that individuals tend to respond more strongly to
RWFE than institutions. In contrast, the coeﬃcient on AFE in the AOIB large [0, +1] equation appears to be much larger
than that in the AOIB small [0, +1] equation, but the null of the equality of the two coeﬃcients is not rejected (χ2 = 1.50,
p > 0.10).

34

behavior in response to the management earnings forecasts by taking into account its autocorrelation
structure, while for the bad news forecasts, they do not.
Regression 3 of Table 9 includes the additional six variables to control for the potential eﬀect of thevarious ﬁrm characteristics on the trading behavior of individuals and institutions during the announcement period. The conclusion drawn from Regression 2 remains basically unchanged by controlling forthese ﬁrm characteristics. The only major diﬀerence between Regressions 2 and 3 is that the coeﬃcienton RWFE in the AOIB large [0, +1] equation is insigniﬁcantly positive (= 0.094, t = 1.23) in Regression
2 while it is signiﬁcantly positive (= 0.164, t = 2.09) in Regression 3. Because Hypothesis 2 predicts
that in the AOIB large [0, +1] equation, the coeﬃcient on AFE should be positive and signiﬁcant but thecoeﬃcient on RWFE should be insigniﬁcantly diﬀerent from zero, the result in Regression 3 partiallyconﬂicts with the hypothesis. However, we do not believe that this evidence is inconsistent with theunderlying notion of the hypothesis. Institutions seem to react somewhat to RWFE but the fact remainsthat they use not the random walk model but the analyst forecast in forming the prior expectation forthe current earnings. In fact, the coeﬃcient on AFE appears to be much larger than the coeﬃcient on
RWFE although the null of equality of the two coeﬃcients (i.e., 0.164 vs. 1.071) cannot be rejected at
the conventional levels (χ2 = 2.11, p = 0.147). In addition, it seems unlikely that institutions hold naı̈ve
expectations for the current earnings from the results in the univariate analysis (see Table 8). Hence, we
conclude that Hypothesis 2 is basically supported even in Regression 3.
Turning to control variables, we observe that the unusual buying/selling activity of small traders during the announcement period is positively associated with MVE and SPREAD. This means that during
the announcement period, individuals tend to actively buy stocks with a larger market cap and higher
bid-ask spread even after controlling for the news in the earnings announcement. In the AOIB large [0, +1]
equation, the coeﬃcients on MVE , B /M , and STDRET are negative and signiﬁcant at the conventional
levels, and the coeﬃcient on YIELD is positive and signiﬁcant at the 1% level. These ﬁndings indicatethat during the announcement period, institutions tend to unusually buy stocks with a smaller market
cap, lower book-to-market ratio, lower standard deviation of returns, and higher dividend yield even after
controlling for the news in the earnings announcement.
The results in this section strongly support our Hypotheses 1, 2, and 3, respectively, and partially
support Hypothesis 4; the fourth hypothesis is supported for the good news forecasts, but not for thebad news forecasts. Overall, our results suggest that the individuals’ trading behavior in response to theearnings announcements is so naı̈ve as if they use nothing other than the information released at the time35

of the earnings announcement—for the current earnings, they are interested only in the earnings changes
from the previous year, and for the management earnings forecast, they take it at face value despite theanticipated optimistic bias. On the other hand, the institutions’ behavior is more sophisticated.

4.4

Robustness check

We conduct several robustness tests. First, we check whether or not our results are sensitive to thedeﬁnition of event period. Battalio and Mendenhall [2005] investigate the investor-level reaction to theearnings announcements during the three days centered on the announcement date. We then set days
−1 to +1 as the alternative event period and reestimate Eqs. (2a) and (2b). The results (not reported
in tables) are essentially unchanged from the ones in Table 9, except that the individuals’ reaction to the
management earnings forecasts weakens. Therefore, our inferences are unaﬀected by the deﬁnition of theevent period.
Second, we investigate whether or not our results are sensitive to the measurement of order imbalance.
We use the OIB measure based on the number of traded shares in the main analyses. As a robustness
check, we use the alternative measure, that is, the OIB on the basis of the Japanese yen paid by buyerand seller-initiators, and then reestimate Eqs. (2a) and (2b). We ﬁnd that the pattern of the estimatedcoeﬃcients in terms of the sign and magnitude is similar to that in Table 9. Hence, our results are robust
to an alternative measurement of the OIB .
Finally, the criteria to distinguish between individuals and institutions rely much on our intuition andone may have doubts about the robustness of our results. Thus, we reestimate Eqs. (2a) and (2b) using
the following alternative criterion: a trade less (more) than or equal to 1,000,000 (4,000,000) Japanese yen
as the trades initiated by individuals (institutions). The untabulated results show that the pattern of theestimated coeﬃcients in terms of the sign and magnitude is barely aﬀected by this switch. However, weobserve a signiﬁcant diﬀerence between this robustness test and Table 9. Surprisingly, we ﬁnd that for thegood news forecasts, both individuals and institutions trade on the management earnings forecasts taking
into account the prior management forecast bias under this alternative criteria. The untabulated results
show that the coeﬃcient on EFEC is signiﬁcantly positive (= 1.181, t-stat. = 2.34) and the coeﬃcientsmall[0, +1]on EFEC × GN × BIAS dec
t−1 is signiﬁcantly negative (= −0.927, t-stat. = 1.80) in the AOIB

equation. This is consistent with the pattern of estimated coeﬃcients in the AOIB large [0, +1] equation.
These ﬁndings indicate that for the good news forecasts, both individuals and institutions trade on
the management earnings forecasts with taking into account the last year’s management forecast bias.

36

In other words, among individuals, only the traders with a relatively smaller amount of investment fundsnaı̈vely react to the management earnings forecasts. The stealth-trading hypothesis could provide one
possible explanation for this. Barclay and Warner [1993] and Chakravarty [2001] argue that the informed
traders prefer to break into more than one medium-sized trades to avoid revealing private information andlarge price concessions. According to the suggestions in the previous studies, we exclude the medium-sized
trades to discriminate accurately between individuals and institutions. By raising the upper thresholdfor small trades from 500,000 to 1,000,000 Japanese yen, small trades based on the alternative criterion
might include more informed trades by the institutions’ stealth trading. However, we can again obtainthe similar results otherwise. Overall, we conclude that our inferences are basically robust to using analternative criterion.

4.5

Additional analysis

Malmendier and Shanthikumar [2007] ﬁnd that large traders rationally react to the biased analyst recommendations, and then, enjoy a positive return during the period after more sophisticated trading. In
contrast, they ﬁnd that by naı̈vely following the recommendations, small traders incur losses from less sophisticated trading (see also Mikhail, Walther, and Willis [2007]). These results suggest that the rational(naı̈ve) trading activity in response to the public information yields a positive (negative) return after theannouncement period. In the previous section, we show that individuals naı̈vely trade on both the current
earnings and management earnings forecasts, while institutions rationally trade on such information. The
evidence raises an interesting question as to whether or not by more sophisticated (less sophisticated)
trading to the earnings announcements, institutions (individuals) can enjoy a positive (negative) return
after the announcement period. To examine this prediction, we estimate the following equation:
CAR[k, l] = α0 +2006∑

ατ −1999 Y Dτ,i,t + β1 AOIB small [0, +1] + β2 AOIB large [0, +1] + µi,t ,(3)

τ =2000

where [k, l] is time interval from day k to l after the earnings announcement date (day = 0). We set
two diﬀerent time intervals, [+2, +30] and [+2, +60], for this analysis26 . If institutions (individuals) earn
positive (negative) future returns from their trading during the announcement period, the coeﬃcient on
AOIB large [0, +1] (AOIB small [0, +1]) is expected to be positive (negative).

26 In this test, we exclude the observations that disappear from the stock returns database during the return cumulation

period. For this reason, we exclude 15 observations if we use the time interval [+2, +60], while no observations are excluded
if we set the time interval [+2, +30].

37

Table 10 reports the OLS coeﬃcient estimatesm and White [1980] heteroscedasticity consistent tstatistics given in the parentheses. In the CAR[+2, +30] equation, we observe that the coeﬃcient on
AOIB large [0, +1] is positive (= 0.003) and statistically signiﬁcant (p < 0.05), while the coeﬃcient on
AOIB small [0, +1] is negative (= −0.002) but insigniﬁcantly diﬀerent from zero. These results indicatethat the stocks with unusually buying (selling) by institutions during the announcement period experience
more positive (negative) returns after the announcement period. For another time interval, [+2, +60],
the coeﬃcient on AOIB large [0, +1] remains positive (= 0.006) and statistically signiﬁcant (p < 0.01).
In addition, the coeﬃcient on AOIB small [0, +1] becomes signiﬁcantly negative (= −0.004, p < 0.10)27 .
These results suggest that if we assume a holding period of sixty days, institutions can obtain positive
returns from their trading during the announcement period. In contrast, individuals have signiﬁcantly
negative future returns on their trading during the announcement period.
Overall, our results indicate that compared with individuals, institutions make better use of the earnings announcement in which the current earnings and management earnings forecasts are simultaneously
released. Therefore, institutions (individuals) can have signiﬁcantly positive (negative) future returns
from their trading during the announcement period.

5

Conclusion

We examine how individuals and institutions respond to the earnings announcements by focusing on
the unique Japanese disclosure setting wherein the ﬁrms simultaneously report the actual earnings forthe previous and current years along with the management earnings forecast for the next year. The
TSE requires the listed ﬁrms to release the management earnings forecasts so as to help the investors
in assessing future prospects. Using this unique setting, we test whether or not the trading behavioraround the earnings announcements is signiﬁcantly diﬀerent between individuals and institutions. Our
main ﬁndings are as follows.

27 To evaluate the economic impact of these two coeﬃcients, we also estimate Eq. (3) using the annual decile rank, scaled

to range between 0 and 1, for AOIB small [0, +1] and AOIB large [0, +1]. By this coding scheme, the coeﬃcient on eachvariable can be interpreted as the diﬀerence in the abnormal return between the portfolio for stocks with the strongest
selling pressure and that for the stocks with the strongest buying pressure by each type of traders. The untabulated
results show that the coeﬃcient on AOIB small [0, +1] is −0.008 and signiﬁcant (p < 0.01) while the the coeﬃcient on
AOIB large [0, +1] is 0.015 and signiﬁcant (p < 0.01). The results imply that the diﬀerence in the abnormal return between
the lowest and highest AOIB small [0, +1] (AOIB large [0, +1]) deciles is −0.8% (1.5%), or −3.3% (6.3%) on an annualizedbasis. We believe that these diﬀerences are also economically signiﬁcant.

38

First, we ﬁnd that individuals respond diﬀerently from institutions to the the current earnings—theindividuals’ (institutions’) trading behavior around the earning announcements is strongly associatedwith the simplistic random walk forecast errors (analyst forecast errors), whereas has little relationshipwith analyst forecast errors (simplistic random walk forecast errors). The diﬀerential trading behavior
in response to the current earnings between individuals and institutions reﬂects the divergent earnings
expectations of the current earnings just prior to the earnings announcements. Individuals behave as if
they only read the earnings report and/or the ﬁnancial press, and then regard earnings of the previous
year as earnings expectation. Their earnings expectations are not so informed. They are likely to ignore
the costly information such as analyst forecasts and use the simplistic random walk model in reference tothe incomplete information set that is easily available at the time of earnings announcement. On the other
hand, institutions behave as if they in advance hold the earnings expectation based on the prediction of
the more accurate model—analyst forecasts. They appear to actively incorporate analyst forecast, whichis costly information in terms of acquisition, into their earnings expectation, and therefore, their trading
behavior depends on the analyst forecast error.
Second, we ﬁnd that individuals also respond diﬀerently from institutions to the management earnings
forecasts. Our results show that both traders use them, but trade on them diﬀerently. Individuals tradeon the management earnings forecasts literally in spite of the fact that the bias in the management
earnings forecast issued at the time of earnings announcement has a positive autocorrelation structure.
They behave as if they only see the management earnings forecasts included in the earnings report
and/or the ﬁnancial press to make an investment decision, and therefore, naı̈vely trade on the forecast.
They are not likely to properly unscramble the implication of the management forecasts, and seem to be
misled by biased forecasts. In contrast, our results indicate that institutions respond to the management
forecasts with the predictable optimistic bias in mind. For good news forecasts, we ﬁnd that they
discount the management forecasts with higher anticipated optimism by judging it from the previous
year’s forecast bias. On the other hand, for the bad news forecasts, they do not take into account the
autocorrelation structure of the forecast bias. Instead, they realize that bad news forecasts are likely
to be less optimistically biased than the good news forecast and then react to the bad news forecasts
literally even when the management earnings forecast of the previous year is optimistically distorted.
Finally, we ﬁnd that the individuals’ and institutions’ unusual buy/sell order imbalances during theannouncement period exhibit a predictive power for the post-announcement returns over a sixty-day
period: the former (the latter) is negatively (positively) associated with the size-adjusted abnormal

39

returns. These ﬁndings indicate that by relatively naı̈ve (rational) trading during the announcement
period, individuals (institutions) have negative (positive) returns after the announcement.
Overall, we conclude that individuals respond diﬀerently from institutions to identical earnings news
under the unique Japanese setting. Our results suggest that the individuals’ trading is so naı̈ve as if theyuse nothing other than the information released at the time of earnings announcement. They prefer to use
easily available information at a low cost and spend far less time on analyzing the earnings information.
Stated diﬀerently, they tend to avoid incurring the additional costs of gathering and processing information. In contrast, institutions tend to actively use costly predisclosure information and unscramble theinformation released at the time of earnings announcement in combination with such costly predisclosure
information. In other words, they willingly incur the additional costs of gathering and processing information. As a result, institutions make better use of the earnings announcement information including themanagement earnings forecasts and their trading behavior around the earnings announcement is much
more sophisticated than that of individuals.

40

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44

Fiscal year t − 1

Fiscal year t

Fiscal year t + 1

tEarnings announcement
day for fiscal year t − 1

Actual earnings, ei,t−2 and ei,t−1 ,and initial management
forecast of year t earnings,
f et−1
i,t , is announced

One day before earnings
announcement day

Earnings announcement
day for fiscal year t

The latest analyst forecastof year t earnings, afi,t ,prior to announcementof ei,t is issued

Actual earnings, ei,t−1 and ei,t ,and initial management
forecast of year t + 1 earnings,
f eti,t+1 , is announced

Our focus is trading behavior ofindividuals and institutions at this timein response to

Two possible current earnings surprises

Management earnings forecast surpriseRandom walk forecast error = ei,t − ei,t−1
Analyst forecast error = ei,t − afi,tExpected future earnings change = f eti,t+1 − ei,tManagement forecast bias in initial earnings
forecast in fiscal year t − 1

Forecast bias in year t − 1 = ei,t − f et−1i,tFigure 1: Timeline for measurement of earnings-related variables

45

day
-5
-4
-3
-2
-1
0
+1
+2
+3
+4
+5

0
-0.0001166
0.0001122
0.0005213-0.0009809
0.0004826-0.0067873-0.0205837-0.0005462
0.0003415-0.0002657-0.000739

111
0.0007147
0.0005145
0.0007237
0.0012718
0.002576
0.0083432
0.010059
0.0007401
0.0002631
0.0005058
0.0011097

0.0002524
0.0000731
0.0007728
0.0009296
0.0021504
0.0041674
0.0021011
0.0003922
0.0001329
0.0001041
0.0005567

グラフ 4

1.5%
1.0%
0.5%
0%
-0.5%
-1.0%
-1.5%
-2.0%
-2.5%

-5

-4

-3

-2

-1

0

Bad news sample

+1

+2

+3

+4

+5

Good news sample

Figure 2: Behavior of mean size-adjusted return around earnings announcements
This ﬁgure shows mean size-adjusted return for portfolios of ﬁrms that release extremely good and bad news over the period
from 5 days before to 5 days after the earnings announcement date (day 0). Good news (bad news) sample consists of
2,903 (257) observations where RWFE , AF E, and EF EC are all non-negative (negative). RWFE (AFE ) is random walk
forecast error (analyst forecast error) and is deﬁned as the diﬀerence between actual earnings for year t and actual earnings
for year t − 1 (the most recent analyst forecast for year t before earnings announcement), deﬂated by the market value ofequity at the end of ﬁscal year t. EFEC is expected future earnings change based on management earnings forecast and isdeﬁned as the diﬀerence between management earnings forecast for year t + 1 made at the earnings announcement of year
t and actual earnings for year t, deﬂated by the market value of equity at the end of ﬁscal year t.

46

47

1,672
1,838
1,800

03/08 (actual)

03/09 (actual)

03/10 (management forecast)

490

555

487

(billions of Yen)

Operating income

500

448

440

(billions of Yen)continuing operation

Earnings from

300

279

257

(billions of Yen)

Net income

2,345

2,182

2,012

(Yen)

per share

Earnings

1,270

1,440

1,260

(Yen)

per share

Dividend

share, and dividend per share.

listed ﬁrms to report the management forecasts for the next year (2010 in this example) of sales, operating income, earnings from continuing operation, net income, earnings per

This table shows an example of earnings report released by Nintendo Co., Ltd, that lists on the TSE. This is the earnings report with regard to ﬁscal year 2009. The TSE requires

Table 1: A typical example of the earnings report listing on the TSE(billions of Yen)

Sales

Fiscal year-end

Nintendo Co., Ltd.

May 7, 2009

Panel A: Summary statistics
RWFE
Mean
Standard deviation
P10
P25
P50
P75
P90

−0.011
0.531−0.070−0.011
0.004
0.018
0.069

AFE

EFEC

BIAS t−1−0.0090.322−0.007−0.001
0.000
0.002
0.006

0.063
0.792
−0.012
0.000
0.005
0.021
0.099

0.052
0.496
−0.015−0.005
0.002
0.019
0.089

Panel B: Comparison of accuracy of random walk forecast with that of analyst forecast
MeanMedianabs(RWFE )abs(AFE )

0.092
0.015

0.014
0.002

t-stat.

z-stat.

19.27∗∗∗
79.23∗∗∗

Table 2: Descriptive statistics for earnings-related variables
The sample consists of 10,258 observations during the period from 1999 to 2006. Panel A of this table reports summary
statistics for news in current earnings and management earnings forecast and the previous year’s management forecast bias
in initial earnings forecast. Px is the value of the xth percentile for each variable. RWFE (AFE ) is random walk forecast
error (analyst forecast error) and is deﬁned as the diﬀerence between actual earnings for year t and actual earnings for yeart − 1 (the most recent analyst forecast for year t before earnings announcement), deﬂated by the market value of equity atthe end of ﬁscal year t. EFEC is expected future earnings change based on management earnings forecast and is deﬁned
as the diﬀerence between management earnings forecast for year t + 1 made at the earnings announcement of year t andactual earnings for year t, deﬂated by the market value of equity at the end of ﬁscal year t. BIAS t−1 is the previous year’s
management forecast bias in initial earnings forecast and is deﬁned as the diﬀerence between initial management earnings
forecast of year t earnings made at the earnings announcement of year t − 1 and actual current earnings, deﬂated by themarket value of equity as of the end of ﬁscal year t. Panel B of this table shows the mean and median of absolute values of
RWFE and AFE to compare accuracy of the two measures. Mean (median) diﬀerences are tested using paired two-sample
t-test (Wilcoxon matched-pairs signed-ranks test). *** indicates signiﬁcance at the 1% levels (two-tailed).

48

Panel A: Contingency table of the sign of BIAS t−1 and BIASt
BIAS t < 0
0 ≤ BIAS t
Total
BIAS t−1 < 0
2,617
1,858
4,475
(58.5%)(41.5%)(45.0%)
0 ≤ BIAS t−1
2,007
3,463
5,470
(36.7%)(63.3%)(55.0%)
Total
4,624
5,321
9,945
(46.5%)(53.5%)(100.0%)χ2 = 472.74 (p < 0.01)
Panel B: Summary statistics of BIAS t for portfolios formed by BIAS t−1
Obs.
D1 (Low BIAS t−1 )
D2
D3
D4
D5
D6
D7
D8
D9
D10 (High BIAS t−1 )All firms
D10−D1t-stat.
z-stat.

990
995
994
995
996
994
994
995
994
998
9,945

Mean of BIAS t

Median of BIAS t

Percentage of 0 ≤ BIASt(% of Optimistic forecast)

0.041
0.040
0.015
0.019
0.016
0.029
0.036
0.040
0.112
0.190
0.054
0.149
5.09***−0.003−0.002−0.001−0.001
0.000
0.002
0.003
0.004
0.008
0.015
0.001
0.018

40.9%
42.3%
45.9%
44.6%
50.2%
56.2%
60.6%
63.1%
66.2%
64.9%
53.5%

12.91***

Table 3: Autocorrelation structure of management forecast bias in initial earnings forecast
Panel A of this table reports a contingency table of the sign of BIAS t−1 and BIASt . BIAS t−1 (BIAS t ) is the previous
year’s (current year’s) management forecast bias in initial earnings forecast and is deﬁned as the diﬀerence between initialmanagement earnings forecast of year t (t + 1) earnings made at the earnings announcement of year t − 1 (t) and actual
current earnings of year t (t + 1), deﬂated by the market value of equity as of the end of ﬁscal year t (t + 1). Panel B of thistable reports mean and median of BIAS t and percentage of observations with 0 ≤ BIAS t for portfolios formed by BIAS t−1 .
To construct this table, we sort sample ﬁrms into deciles based on annual BIAS t−1 rank. D10−D1 represents the diﬀerence
in means between the top (D10) and bottom (D1) deciles. Mean (median) diﬀerences are tested using t-test (Wilcoxon
rank-sum test). *** indicates signiﬁcance at the 1% levels (two-tailed). For this analysis, we use 9,945 observations forwhich BIASt is available in our sample.

49

Panel A: Mean value of RWFE and CAR[0, +1] for portfolios formed by RWFE
Obs.

RWFE

CAR[0, +1]

Q1 (Low RWFE )Q2Q3Q4Q5 (High RWFE )

2,047
2,052
2,052
2,052
2,055

− 0.248−0.009
0.004
0.015
0.184

0.69%
0.08%
0.43%
1.01%
0.92%

All firms
Q5−Q1

10,258−0.011

0.63%
0.22%

Panel B: Mean value of AFE and CAR[0, +1] for portfolios formed by AFE
Obs.

AFE

CAR[0, +1]

Q1 (Low AFE )Q2Q3Q4Q5 (High AFE )

2,047
2,052
2,052
2,052
2,055

−0.055−0.001
0.000
0.002
0.010

0.12%
0.21%
0.45%
0.87%
1.48%

All firms
Q5−Q1

10,258−0.0090.63%
1.37%∗∗∗

Panel C: Mean value of EFEC and CAR[0, +1] for portfolios formed by EFEC
Obs.

EFEC

Q1 (Low EFEC )Q2Q3Q4Q5 (High EFEC )

2,047
2,052
2,052
2,052
2,055

−0.035
0.001
0.006
0.019
0.320

All firms
Q5−Q1

10,2580.063

CAR[0, +1]−1.40%
0.00%
0.87%
1.71%
1.95%
0.63%
3.35%∗∗∗

Table 4: Mean value of CAR[0, +1] for portfolios formed by each news variable
The sample consists of 10,258 observations during the period from 1999 to 2006. This table reports the mean size-adjusted
abnormal returns for portfolios formed by RWFE , AFE , or EFEC . The abnormal return (CAR) is cumulated over days
0 through +1 relative to the earnings announcement date (day 0). RWFE (AFE ) is random walk forecast error (analyst
forecast error) and is deﬁned as the diﬀerence between actual earnings for year t and actual earnings for year t − 1 (the mostrecent analyst forecast for year t before earnings announcement), deﬂated by the market value of equity at the end of ﬁscal
year t. EFEC is expected future earnings change based on management earnings forecast and is deﬁned as the diﬀerence
between management earnings forecast for year t + 1 made at the earnings announcement of year t and actual earnings
for year t, deﬂated by the market value of equity at the end of ﬁscal year t. To construct this table, we independently
sort sample ﬁrms into quintiles based on annual RWFE , AFE , or EFEC rank. Q5−Q1 represents the diﬀerence in means
between the top (Q5) and bottom (Q1) quintiles. Mean diﬀerences are tested using t-test. *** indicates signiﬁcance at the
1% levels (two-tailed).

50Panel A: Spearman Correlation Matrix
CAR[0, +1]
CAR[0, +1]
RWFE
AFE
EFEC

1.000

RWFE
0.026∗∗∗
1.000

AFE

EFEC

0.100∗∗∗
0.191∗∗∗
1.000

0.214∗∗∗−0.366∗∗∗−0.178∗∗∗1.000

Panel B: Regression results of CAR[0, +1] on news variables and the previous year’s management forecast bias
Regression 1
Constant

Regression 2

Year dummy

Included

0.008∗∗∗(4.52)
0.020∗∗∗(3.70)
0.142∗∗∗(3.61)
0.293∗∗∗(4.90)−0.000(−0.097)−0.262∗∗∗(−4.36)0.074(0.66)
Included

Observations
Adjusted R2

9, 6600.01289, 6600.0280

RWFE
AFE
EFEC

0.008∗∗∗(5.00)
0.022∗∗∗(4.23)
0.178∗∗∗(4.44)
0.049∗∗∗(6.99)

dec
BIASt−1
dec
EFEC × GN × BIASt−1
dec
EFEC × BN × BIASt−1

Table 5: Market reaction to concurrent announcements of current earnings and management earnings forecast
Panel A of this table provides Spearman correlation coeﬃcients. CAR[0, +1] is the abnormal return cumulated over days
0 through +1 relative to the earnings announcement date (day 0). RWFE (AFE ) is random walk forecast error (analyst
forecast error) and is deﬁned as the diﬀerence between actual earnings for year t and actual earnings for year t − 1 (the mostrecent analyst forecast for year t before earnings announcement), deﬂated by the market value of equity at the end of ﬁscal
year t. EFEC is expected future earnings change based on management earnings forecast and is deﬁned as the diﬀerence
between management earnings forecast for year t + 1 made at the earnings announcement of year t and actual earnings
for year t, deﬂated by the market value of equity at the end of ﬁscal year t. The sample consists of 10,258 observations
during the period from 1999 to 2006. Panel B of this table shows OLS pooled regression results of CAR[0, +1] on news incurrent earnings and management earnings forecast and the previous year’s management forecast bias in initial earnings
dec is annual decile rank, scaled to range between 0 and 1, for BIAS
forecast. BIASt−1
t−1 . BIAS t−1 is the previous year’s

management forecast bias in initial earnings forecast and is deﬁned as the diﬀerence between initial management earnings
forecast of year t earnings made at the earnings announcement of year t − 1 and actual current earnings, deﬂated by themarket value of equity as of the end of ﬁscal year t. GN (BN ) is an indicator variable that is set equal to one for ﬁrms
with 0 ≤ EFEC (EFEC < 0). To mitigate the inﬂuence of outliers, we estimate each regression using the observations
within the 1st and 99th of the annual distributions for respective earnings-related variables (i.e., RWFE , AFE , and EFEC )and return-related variable (i.e., CAR) across year. The t-statistics, reported in parentheses, are based on White [1980]
heteroscedasticity-consistent standard errors. *** indicates signiﬁcance at the 1% levels (two-tailed).

51

Panel A: Contingency table of the sign of EFEC and BIASt
BIAS t < 0 0 ≤ BIAS t
Total
EFEC < 0
1,338
1,159
2,497
(53.6%)(46.4%)(25.1%)
0 ≤ EFEC
3,286
4,162
7,448
(44.1%)(55.9%)(74.9%)
Total
4,624
5,321
9,945
(46.5%)(53.5%) (100.0%)
χ2 = 67.22 (p < 0.01)
Panel B: Summary statistics of BIAS t for portfolios formed by the sign of EFEC
Obs.
Mean
MedianEFEC < 00 ≤ EFEC

2,497
0.063
−0.001

7,448
0.051
0.002

Total
9,945
0.054
0.001

t-stat.

z-stat.

−0.71
9.10***

Table 6: Association between management earnings forecast and the forecast bias
Panel A of this table reports a contingency table of sign of EFEC and BIASt . EFEC is expected future earnings change
based on management earnings forecast and is deﬁned as the diﬀerence between management earnings forecast for year t + 1
made at the earnings announcement of year t and actual earnings for year t, deﬂated by the market value of equity at theend of ﬁscal year t. BIAS t is forecast bias of the management earnings forecast and is deﬁned as the diﬀerence between
initial management earnings forecast of year t + 1 earnings made at the earnings announcement of year t and actual earningsof year t + 1, deﬂated by the market value of equity as of the end of ﬁscal year t + 1. Panel B of this table reports mean andmedian of BIAS t for portfolios formed by the sign of EFEC . Mean (median) diﬀerences are tested using t-test (Wilcoxon
rank-sum test). *** indicates signiﬁcance at the 1% levels (two-tailed). For this analysis, we use 9,945 observations forwhich BIASt is available in our sample.

52Panel A: AOIB small [0, +1] and firm characteristics
Q1(Net seller)
MVE (billion yen)
B /M
STDRET (%)
TURNOVER (%)
SPREAD (%)
YIELD (%)

228
0.872
2.56%
0.33%
0.55%
1.23%

Q2

Q3

Q4

198
0.905
2.64%
0.34%
0.61%
1.22%

197
0.945
2.69%
0.35%
0.63%
1.24%

225
0.906
2.83%
0.39%
0.68%
1.16%

Q2

Q3

Q4

332
0.894
2.73%
0.37%
0.58%
1.13%

271
0.874
2.67%
0.36%
0.58%
1.16%

219
0.884
2.71%
0.37%
0.61%
1.17%

Q5
(Net buyer)

All firms

Q5-Q1

380
0.849
2.75%
0.41%
0.62%
1.08%

246
0.895
2.69%
0.36%
0.62%
1.19%

152∗∗∗−0.022
0.19%∗∗∗
0.08%∗∗∗
0.07%∗∗∗−0.15%∗∗∗

Q5(Net buyer)

All firms

Q5-Q1

196
0.891
2.67%
0.36%
0.66%
1.25%

246
0.895
2.69%
0.36%
0.62%
1.19%

−15
−0.042∗∗−0.02%
0.01%
0.00%
0.03%

Panel B: AOIB large [0, +1] and firm characteristics
Q1(Net seller)
MVE (billion yen)
B /M
STDRET (%)
TURNOVER (%)
SPREAD (%)
YIELD (%)

211
0.934
2.69%
0.35%
0.66%
1.22%

Table 7: AOIB measure and ﬁrm characteristics
The sample consists of 10,258 observations during the period from 1999 to 2006. Panel A (Panel B) of this table reports
the result of univariate relationship between AOIB for small traders (large traders) in interval [0, +1] and various ﬁrm
characteristics. To construct this table, we independently form ﬁve portfolios based on annual AOIB[0, +1] rank for small
and large traders, and compute the average for each ﬁrm characteristics in each of the portfolios. MVE is market valueof equity deﬁned as market value of equity as of the end of ﬁscal year t. B /M is market-to-book ratio computed as bookvalue of equity at the end of ﬁscal year t − 1 divided by market value of equity six months after the end of ﬁscal year t − 1.
STDRET is standard deviation of daily returns during the ﬁscal year t. TURNOVER is average daily turnover (deﬁned
as daily number of shares traded dividend by daily number of shares outstanding) over ﬁscal year t. SPREAD is average
daily equal-weighted relative spread over ﬁscal year t. YIELD is average dividend yield at the end of month over ﬁscal year
t. Q5−Q1 represents the diﬀerence in means between the top (Q5) and bottom (Q1) quintiles. Mean diﬀerences are tested
using t-test. *** and ** indicate signiﬁcance at the 1% and 5% levels (two-tailed), respectively.

53

Panel A: Mean value of AOIB small [0, +1] and AOIB large [0, +1] for portfolios formed by RWFE
Obs.

RWFE

AOIB small [0, +1]

AOIB large [0, +1]

Q1 (Low RWFE )Q2Q3Q4Q5 (High RWFE )

2,047
2,052
2,052
2,052
2,055

− 0.248−0.009
0.004
0.015
0.184

−0.016−0.018
0.015
0.042
0.075

0.130
0.073
0.057
0.115
0.098

All firms
Q5−Q1

10,258−0.0110.020
0.091∗∗∗

0.095−0.032

Panel B: Mean value of AOIB small [0, +1] and AOIB large [0, +1] for portfolios formed by AFE
Obs.

AFE

AOIB small [0, +1]

AOIB large [0, +1]

Q1 (Low AFE )Q2Q3Q4Q5 (High AFE )

2,047
2,052
2,052
2,052
2,055

−0.055−0.001
0.000
0.002
0.010

0.023
0.030
0.014
0.012
0.021

0.058
0.047
0.087
0.117
0.163

All firms
Q5−Q1

10,258−0.0090.020−0.0020.095
0.104∗∗∗

Panel C: Mean value of AOIB small [0, +1] and AOIB large [0, +1] for portfolios formed by EFEC
Obs.

EFEC

AOIB small [0, +1]

AOIB large [0, +1]

Q1 (Low EFEC )Q2Q3Q4Q5 (High EFEC )

2,047
2,052
2,052
2,052
2,055

−0.035
0.001
0.006
0.019
0.320

0.015
0.018
0.004
0.033
0.030

−0.018
0.014
0.122
0.171
0.183

All firms
Q5−Q1

10,258

0.063

0.020
0.016

0.095
0.200∗∗∗

Table 8: Mean value of AOIB small [0, +1] and AOIB large [0, +1] for portfolios formed by each news variable
The sample consists of 10,258 observations during the period from 1999 to 2006. This table reports the average AOIB
for small and large traders during the announcement period from days 0 to +1 relative to the earnings announcement
date (day 0) for portfolios formed by RWFE , AFE , or EFFE . AOIB z [0, +1] is abnormal order imbalance for trader
type (z ∈ small, large) and time interval [0, +1]. RWFE (AFE ) is random walk forecast error (analyst forecast error)
and is deﬁned as the diﬀerence between actual earnings for year t and actual earnings for year t − 1 (the most recent
analyst forecast for year t before earnings announcement), deﬂated by the market value of equity at the end of ﬁscal year t.
EFEC is expected future earnings change based on management earnings forecast and is deﬁned as the diﬀerence between
management earnings forecast for year t + 1 made at the earnings announcement of year t and actual earnings for year t,
deﬂated by the market value of equity at the end of ﬁscal year t. To construct this table, we independently sort sample
ﬁrms into quintiles based on annual RWFE , AFE , or EFEC rank. Q5−Q1 represents the diﬀerence in means between the
top (Q5) and bottom (Q1) quintiles. Mean diﬀerences are tested using t-test. *** indicates signiﬁcance at the 1% levels(two-tailed).

54

55

(Continued)

Observations
Adjusted R2

Year DummyYIELDln(SPREAD)

9, 8270.0761

Included

0.537∗∗∗(26.8)−0.094∗∗∗(−5.02)−0.052∗∗(−2.22)
0.348∗∗∗(4.96)
0.329(0.58)
0.248∗∗∗(2.94)

9, 8270.0639Included−0.001(−0.03)0.392∗∗∗(19.4)−0.002(−0.07)0.106(1.39)
1.107∗(1.82)
0.276∗∗∗(3.02)

9, 8270.0766

Included−0.026(−1.02)0.337∗∗∗(4.79)
0.061(0.11)
0.802∗∗(2.06)−0.065∗∗(−2.53)−0.502(−1.27)−1.400(−1.29)0.536∗∗∗(26.8)−0.093∗∗∗(−5.00)9, 827
0.0663

Included−0.026(−0.92)0.094(1.23)
1.079∗(1.74)
1.736∗∗∗(4.13)
0.056∗∗(2.02)−1.634∗∗∗(−3.84)1.045(0.89)−0.000(−0.02)0.391∗∗∗(19.3)

Regression 2
AOIB small [0, +1] AOIB large [0, +1]

9, 8270.0802−0.144(−0.89)0.275∗∗∗(3.80)
0.208(0.36)
0.723∗(1.86)−0.046∗(−1.80)−0.518(−1.32)−1.358(−1.25)0.521∗∗∗(25.9)−0.096∗∗∗(−5.10)0.030∗∗∗(3.54)−0.006(−0.46)0.042(1.29)
0.009(0.78)
0.082∗∗∗(4.00)−1.290(−1.25)
Included
9, 8270.06900.199(1.13)
0.164∗∗(2.09)
1.071∗(1.73)
1.809∗∗∗(4.30)
0.043(1.55)−1.594∗∗∗(−3.74)0.839(0.71)
0.009(0.41)
0.388∗∗∗(19.1)−0.024∗∗∗(−2.65)−0.046∗∗∗(−3.57)−0.075∗∗(−2.11)−0.003(−0.27)−0.008(−0.38)3.290∗∗∗(2.95)
Included

Regression 3
AOIB small [0, +1] AOIB large [0, +1]

Table 9: Regression results of AOIB [0, +1] on news variables and the previous year’s management forecast biasln(TURNOVER)

ln(STDRET )ln(B/M )ln(MVE )AOIB large [−30, −1]

AOIB small [−30, −1]

dec
EFEC × BN × BIASt−1

dec
EFEC × GN × BIASt−1

dec
BIASt−1

EFEC

AFE

RWFE

Constant

Regression 1
AOIB small [0, +1] AOIB large [0, +1]

56[0, +1][0, +1]

large

=

=
dec

ατ −1999 Y Dτ,i,t + β1 RWFE i,t + β2 AFE i,t + β3 EFEC i,tdecτ =2000

2006
X
dec

[−30, −1] + Control variables + εi,tdec

δτ −1999 Y Dτ,i,t + γ1 RWFE i,t + γ2 AFE i,t + γ3 EFEC i,tdecτ =2000large[−30, −1] + β8 AOIB i,tdec+γ7 AOIB i,t

small

large

[−30, −1] + γ8 AOIB i,t[−30, −1] + Control variables + υi,t .

+γ4 BIAS i,t−1 + γ5 EFEC i,t × GN i,t × BIAS i,t−1 + γ6 EFEC i,t × BN i,t × BIAS i,t−1

δ0 +

2006
X

+β7 AOIB i,tsmall+β4 BIAS i,t−1 + β5 EFEC i,t × GN i,t × BIAS i,t−1 + β6 EFEC i,t × BN i,t × BIAS i,t−1

α0 +(i.e., RWFE , AFE , and EFEC ) and return-related variable (i.e., CAR) across year. ***, **, and * indicate signiﬁcance at the 1%, 5%, and 10% levels (two-tailed), respectively.

to 2006, to mitigate the inﬂuence of outliers, we estimate using the observation within 1st and 99th percentiles of the annual distribution for respective earnings-related variables

We simultaneously estimate these two equations using seemingly unrelated regression techniques. Although initial sample consists of 10,258 observations during the period from 1999

variables are natural logarithm of MVE , B /M , STDRET , TURNOVER, and SP READ and YIELD. These control variables are deﬁned as in Table 7. εi,t and υi,t are error terms.

deﬂated by the market value of equity as of the end of ﬁscal year t. GN (BN ) is an indicator variable that is set equal to one for ﬁrms with 0 ≤ EFEC (EFEC < 0). The control

forecast and is deﬁned as the diﬀerence between actual current earnings and initial management earnings forecast of year t earnings made at the earnings announcement of year t − 1,

dec is annual decile rank, scaled to range between 0 and 1, for BIAS
at the end of ﬁscal year t. BIASt−1
t−1 . BIAS t−1 is the previous year’s management forecast bias in initial earnings

the diﬀerence between management earnings forecast for year t + 1 made at the earnings announcement of year t and actual earnings for year t, deﬂated by the market value of equity

announcement), deﬂated by the market value of equity at the end of ﬁscal year t. EFEC is expected future earnings change based on management earnings forecast and is deﬁned as

forecast error) and is deﬁned as the diﬀerence between actual earnings for year t and actual earnings for year t − 1 (the most recent analyst forecast for year t before earnings

where AOIB zi,t [0, +1] is abnormal order imbalance for ﬁrm i, year t, trader type z (z ∈ small, large), and time intervals [0, +1]. RWFE (AFE ) is random walk forecast error (analyst

AOIB i,t

AOIB i,t

small(—Continued) This table reports the estimation results of the following regressions:Dependent variable

CAR[+2, +30]

−0.016∗∗∗(−4.83)−0.002(−1.21)0.003∗∗(2.30)

Constant
AOIB small [0, +1]
AOIB large [0, +1]

Year dummy
Observations
Adjusted R2

CAR[+2, +60]

Included
10, 0620.0139−0.019∗∗∗(−4.11)−0.004∗(−1.96)0.006∗∗∗(3.29)
Included
10, 0470.0090

Table 10: Cross-sectional regression of future CAR on AOIB around earnings announcements
This table reports OLS pooled regression result of future CAR on AOIB for small and large traders in interval [0, +1].
Although initial sample consists of 10,258 observations during the period from 1999 to 2006, to mitigate the inﬂuenceof outliers, we estimate using the observation within 1st and 99th percentiles of the annual distribution for dependent
variables (i.e., CAR[+2, +30] or CAR[+2, +60]) across year. CAR[k, l] is cumulative size-adjusted abnormal returns overdays k through l relative to the earnings announcement date (day 0). AOIB z [0, +1] is abnormal order imbalance fortrader type (z ∈ small, large) and time interval [0, +1]. The t-statistics, reported in parentheses, are based on White [1980]
heteroscedasticity-consistent standard errors. ***, **, and * indicate signiﬁcance at the 1%, 5%, and 10% levels (two-tailed),
respectively.

57