Informed trading before earnings shock
Tae-Jun Park
Sungkyunkwan University and KCMI
Email: [email protected]
Youngjoo Lee
Sogang University
Email: [email protected]
Kyojik “Roy” Song*
Sungkyunkwan University
Email: [email protected]
November 2013
Acknowledgement: This work was supported by the National Research Foundation of Korea Grant funded by the
Korean Government (NRF-2012S1A5A2A01015857). We thank seminar participants at Sungkyunkwan
University and 2012 Allied Korea Finance Associations Meetings for their helpful comments. All
remaining errors are our own.
*
Corresponding author:
Business school, SKKU
53 Myeongnyun-dong 3-ga, Jongno-gu, Seoul 110-745, Korea
Phone: 82-2-760-0497
-0-
Informed trading before earnings shock
Abstract
This paper investigates whether institutional investors trade profitably around positive or negative
earnings surprises. Using Korean data over the period of 2001-2010, we find that information asymmetry
is larger before negative earnings surprises among investors and that the trading volume decreases only
before the negative events due to the severe information asymmetry. We also find that institutions sell
their stocks prior to negative earnings surprises whereas individual and foreign investors do not anticipate
bad news. Finally we find that institutional trade imbalance is positively related to the announcement
abnormal returns of the negative events. This study complements and extends prior literature on informed
trading around earnings announcements by documenting evidence that domestic institutions exploit their
superior information around only the negative earnings surprises.
JEL classification: G10; G30
Keywords: Information Asymmetry; Earnings surprises; Trading volume; Trade imbalance; Institutional
investors
-1-
Informed trading before earnings shock
I. Introduction
Informed trading around earnings announcements has been one of the main topics in
finance and accounting literature. When assessing the value of a firm, investors should collect
and interpret information regarding the future earnings or cash flows of the firm. Since earnings
announcements convey new information to the market, investors would actively seek
information in the pre-disclosure period. However investors can differ in their information
acquisition abilities or resources, as well as in their information processing skills. Consequently,
information asymmetry among investors may increase around earnings announcements. A stream
of research establishes that information asymmetry increases around earnings announcements.
Lee, Mucklow and Ready (1993) find that bid-ask spreads increase and quoted depths decrease
before earnings announcements. Krinsky and Lee (1996) report that adverse selection component
of spreads increases around earnings announcements. Chae (2005) finds that the cumulative
trading volume decreases before earnings announcements. In general, prior research suggests
that investors widen their bid-ask spreads, decrease order size, and/or postpone trading around
earnings announcements.
We point out that prior research on information asymmetry around earnings
announcements does not distinguish the content of earnings announcements. That is, positive
earnings surprises are treated as a similar event to negative surprises. As a result, higher
information asymmetry around earnings announcements can be ascribed to both positive
surprises and negative surprises. However, the magnitude of information asymmetry may differ
with the different direction of surprises. Firms can voluntarily communicate with investors
-2-
through investor relations, media and other means, prior to earnings announcements. Verrecchia
(1983) argues that firms actively disclose only positive news whose informational value exceeds
the costs of disclosure, and withhold other information. Firms can avoid the negative impact of
withholding negative news on their stock prices because investors are unable to distinguish
whether the firms withhold negative news or positive news whose informational value is just
below a certain threshold. Supporting Verrecchia (1993), previous literature finds empirical
evidence that managers voluntarily disclose good earnings news and delay the disclosure of bad
news (Lev and Penman (1990); Kothari, et al. (2009)). In the survey literature of Graham,
Harvey and Rajgopal (2005), some of the interviewed executives claim that they delay in
releasing bad news in order to further study and interpret the information. Withholding the bad
news may also be related to managers’ concern over their careers. Moreover, underperforming
firms are more likely to abandon a project, sell some of their assets, change business strategy, or
even replace their management. These actions would generate more uncertainty and therefore
increase the likelihood of information asymmetry in trading stocks of bad-news firms. The
findings of prior studies imply that investors may face greater information asymmetry prior to
negative earnings surprises than positive earnings surprises.
In this study, we explore evidence that information asymmetry is higher before negative
earnings surprises than positive surprises. We also investigate whether sophisticated investors
aggressively exploit their informational advantage when information asymmetry is higher. We
argue that the trading patterns of informed and uninformed investors are more likely to be
distinctive when information asymmetry is higher since informed investors attempt to exploit
their informational advantage and uninformed investors try to avoid trades initiated by informed
investors. To test this conjecture, we examine the trading pattern of each type of investors around
-3-
positive versus negative earnings surprises. In our study, we employ Korean data since it has a
couple of advantages. First, the Korea Exchange provides daily trading volume by investor types
for all stocks traded on the Korean Stock Exchange (KSE) and on the Korea Securities Dealers
Automated Quotation (KOSDAQ). Also, the Korean stock market is one of the emerging markets
in which financial institutions from developed markets have actively invested. Foreign investors
are the largest investor group in the Korean stock market. Therefore, the Korean data is well
suited for investigating foreign investors’ informational advantage, relative to domestic investors.
In Figure 1, we exhibit the fraction of market capitalization owned by domestic institutional
investors, individual investors, and foreign investors. Foreign ownership has dramatically
increased in Korea after the ownership limit on foreign investors was lifted in 1998. Foreign
investors owned 18% of the capitalization of Korean stocks at the end of 1999; their ownership
increased to 40% in 2004 and then gradually decreased to 32% in 2012. Meanwhile domestic
institutional investors’ ownership marginally increased from 14% in 1999 and 16% in 2012.
Individual investors are the second largest group in the Korean market. Their ownership
decreased from 32% in 1999 and to 24% in 2012.
We first test whether firms with negative earnings surprises are more likely to have
higher information asymmetry than those with positive earnings surprises, using the proxies such
as size, analyst following, idiosyncratic risk, and Amihud’s (2002) illiquidity ratio. We find that
firms with negative earnings surprise are smaller in size, and have smaller analyst following,
higher idiosyncratic volatility, and higher illiquidity ratio. These findings indicate that firms
announcing negative earnings surprise are more likely to have higher information asymmetry.
-4-
Next, we examine the stock price returns and trading volume around earnings
announcement in order to test whether positive news is more likely to be leaked before the event.
We find that stock prices tend to increase starting one month before the announcements of
positive earnings surprises and do not decline before negative earnings surprises, indicating that
investors reflect on the upcoming positive news of stock prices during the pre-disclosure period.
These results suggest that, in general, investors anticipate positive earnings surprises, but do not
have much information regarding upcoming negative news. More evidence comes from trading
volume prior to earnings announcements. We find that abnormal trading volume is significantly
negative before the earnings announcements, which is consistent with Chae (2005). More
importantly, we document that abnormal trading volume is not statistically different from zero
before positive news, but it is significantly negative before negative news. This indicates that
Chae’s (2005) finding may be driven by abnormal trading volume prior to negative earnings
surprises. Chae (2005) argues that decrease in trading volume prior to scheduled events is
positively related to information asymmetry. Therefore, our finding supports the conjecture that
information asymmetry among investors is greater before negative earnings surprises than
positive surprises.
In order to further explore the link between trading volume and information asymmetry,
we investigate the trading patterns of each type of investors around earnings announcement. We
classify investors into three groups – domestic institutional investors, individual investors, and
foreign investors, and assume that institutional investors are informed investors. Unlike
individual investors, institutional investors frequently communicate with publicly traded firms as
well as with brokerage firms through their investment banking, lending and asset management
arms, in order to acquire information. Prior literature establishes that institutional investors are
-5-
informed traders. Hendershott, Livdan and Schürhoff (2012) provide evidence that institutional
investors’ order flow predicts the occurrence of news announcements, the sentiment of the news
and the stock market reaction on news announcement days; it also forecasts earnings
announcement surprises. Ke and Petroni (2004) argue that institutional investors obtain
information from private communication with management, which is used to predict negative
earnings. If firms tend to release positive news earlier and avoid leaking the negative news to the
markets, institutional investors would have more incentives to collect private information for
firms that withhold negative news. Therefore, institutions may have better private information on
the upcoming negative news, compared to other types of investors, and they can make substantial
short-term trading profits by moving ahead of the uninformed investors. In contrast, uninformed
investors such as individual investors who are at disadvantage in information gathering and
processing skills are more likely to postpone the trading of the firms’ stocks prior to negative
earnings surprises than positive ones. Supporting this conjecture, we find that abnormal turnover
by individual investors is significantly negative before the negative event, but is not significantly
different from zero. We also find that institutional investors’ abnormal trading volume is not
significantly different from zero before either positive or negative surprises.
Finally, we test whether informed investors successfully exploit their superior
information. If institutional investors have informational advantage prior to negative shocks,
their buying and selling activities around the time of the negative news would be more
distinctive than those of other investors. For the test, we estimate the standardized trade
imbalance (our proxy for buying pressure) by investor type before earnings announcements.
Over the trading days from -5 to -1 before negative earnings surprises, the trade imbalance by
domestic institutions is significantly negative, which suggests that domestic institutional
-6-
investors sell their stocks with the upcoming negative news. The trade imbalance by individuals
is significantly positive, while that of foreigners is not significantly different from zero. The
result indicates that individual investors’ trades are opposite to what the upcoming news implies;
moreover, it reveals that foreigners’ trades are not related to the upcoming earnings news. The
trade imbalance by any investor type is significantly different from those by other investor types.
Previous literature provides inconclusive evidence that foreigners perform better than domestic
investors in trading stocks. For instance, Grinblatt and Keloharju (2000) find that foreign
investors act like momentum investors, with high levels of sophistication, and that they
outperform local individual investors in the Finnish stock market. In contrast, Choe, Kho and
Stulz (2005) do not find evidence that foreign investors outperform domestic institutions in the
Korean market. Ekholm (2006) finds foreign investors react to earnings information in a very
similar way as do the majority of investors in the market. Our result is more consistent with Choe
et al. (2005) and Ekholm (2006), implying that foreign investors do not own informational
advantage regarding earnings releases.
To strengthen our argument that institutional investors exploit informational advantage
prior to negative earnings shocks, we test whether the institutions’ trade imbalance before
earnings announcements predicts stock returns after announcements. We find that the trade
imbalance of institutions is positively related to stock returns after negative earnings surprises,
but is not related to stock returns after positive events. We also find that the trade imbalances of
individual and foreign investors are not related to stock returns after positive or negative earnings
surprises. The result suggests that the selling pressures by institutional investors predict stock
returns upon the announcements of negative earnings surprises.
-7-
This paper adds new evidence to the extant literature on the relation between
information asymmetry and earnings announcements. While the prior literature provides
evidence of higher information asymmetry before earnings announcement, we differentiate the
level of information asymmetry before positive vs. negative surprises, and find that the
information asymmetry is higher before negative surprises. This result is consistent with the
theory and empirical findings that firms voluntarily disclose positive news, but hide negative
news. Our paper also complements prior research by providing evidence that institutions exploit
their short-term informational advantage over other investors, especially before negative
earnings surprises. We also contribute to the growing literature on foreign investors’ trading in
the emerging markets. We find that foreign institutions’ trading is not systematic around earnings
announcements and does not predict stock returns. This shows that foreign institutions do not
possess informational advantage around earnings announcements, compared to local institutions.
The rest of the paper is organized as follows. Section II reviews the related literature and
develops the hypotheses. Section III describes the data and Section IV explains the empirical
findings. Section V concludes the paper.
II. Related literature and hypothesis development
Previous literature has extensively examined information asymmetry around the
earnings announcements. For instance, Kim and Verrecchia (1991) show that investors acquire
private information for the opportunity to trade before and at the earnings announcements.
McNichols and Trueman (1994) also argue that information asymmetry should increase before
earnings announcement as there is a risk that trades are initiated by informed investors. Lee,
Mucklow and Ready (1993) find that bid-ask spreads increase and quoted depths decrease before
-8-
earnings announcements. Krinsky and Lee (1996) report that adverse selection component of
spreads increases around earnings announcements. Chae (2005) documents that prior to earnings
announcements, the cumulative trading volume decreases; which is correlated with the extent of
information asymmetry. He finds that decrease in trading volume prior to earnings
announcement is more prominent for small firms and firms with small analyst following and
interprets the results as evidence that decreasing trading volume is associated with information
asymmetry. Atiase (1985) provides empirical evidence that firm size (capitalization) is positively
related to the amount of pre-disclosure information dissemination. Several studies document the
positive relation between analyst following and market liquidity (Brennan and Subrahmanyam
(1995); Roulstone (2003)). The results imply that analysts reduce information asymmetry by
providing public information. Lang and Lundholm (1993) find that analyst following increases
with more informative disclosure. Roll (1988) argues that idiosyncratic price changes can be
attributable to informed trading rather than public information. Based on this notion, Ferreira and
Laux (2007) use idiosyncratic risk as the proxy for information asymmetry and find that antitakeover provisions are negatively related to idiosyncratic risk. They reason that investors may
have greater incentives to collect private information on firms with small anti-takeover
provisions because those firms are more likely to be targeted by other firms or investors. Amihud
(2002) argues that his measure defined as the ratio of absolute stock returns to trading volume
over long periods reflects the price impact of informed trading. Following this literature, we use
size, analyst following, idiosyncratic risk, and Amihud’s illiquidity ratio as the proxy for
information asymmetry.
-9-
In this study, we divide earnings announcements into positive and negative events. Lang
and Lundholm (1993) demonstrate that successful firms provide more information than
unsuccessful firms. Their analysis also indicates that successful firms have less information
asymmetry and less uncertainty than bad-news firms. Lev and Penman (1990) find that on
average, firms voluntarily disclose good earnings news to differentiate themselves from firms
with bad news and they do not find the evidence that the market punishes non-disclosing firms.
Kothari, Shu, and Wysocki (2009) document empirical evidence that managers delay the
disclosure of bad news, relative to good news. Specifically they find that the magnitude of the
negative stock price reaction to bad news disclosures is greater than the magnitude of the
positive stock price reaction to good news disclosure. If firms actively leak information before
positive news, the stock prices of firms would begin to reflect on the upcoming positive news in
the pre-disclosure period. In contrast, the stock prices would not reflect on the upcoming
negative earnings surprises in the pre-disclosure period because bad news is not leaked to the
markets. Therefore, we first test the following hypothesis:
H1: Stock prices increase prior to positive earnings surprises, but do not decrease prior
to negative earnings surprises.
If information leakage is more prevalent before positive earnings releases, there is little
incentive for investors to acquire private information in anticipation of positive announcements.
In contrast, investors have incentives to acquire private information prior to negative earnings
shocks. Thus, information asymmetry across investors becomes larger prior to negative earnings
surprises than positive earnings surprises. Foster and Viswanathan (1995) and Chae (2005) find
that the trading volume decreases before scheduled announcements, such as earnings
- 10 -
announcements, since uninformed investors try to avoid trades initiated by informed investors.
We conjecture that decrease in trading volume is more prominent prior to negative earnings
surprises than positive earnings surprises since information asymmetry is higher prior to negative
earnings surprise than positive earnings surprise. We test the following hypothesis:
H2: The decrease in trading volume prior to negative earnings surprises is larger than
that prior to positive earnings surprises.
A stream of research investigates informed trading of institutional investors since
institutions are generally viewed as better-informed, rational and sophisticated investors who
have better access to corporate information and better ability to analyze it. For instance, Yan and
Zhang (2009) find that stocks for which the quarterly short-term institutional ownership has
increased tend to have positive earnings surprises. To overcome the problem of low-frequency
data, some recent papers have used proprietary data with a small sample size or a short time
period. Griffin, Harris and Topaloglu (2003) use the trade data on Nasdaq100 stocks from
Nasdaq’s transaction confirmation service, and find that institutions purchase stocks that have
recently risen; however, they do not predict future daily returns. Froot and Teo (2008) use
custodial data from State Street Corporation, and find that institutions’ style investing is related
to future stock returns. Baker, Litov, Wachter and Wurgler (2010) find that the average mutual
fund displays the stock-picking skill in that subsequent earnings announcement returns on its
weight-increasing stocks are significantly higher than those on its weight-decreasing stocks.
Recently, Griffin, Shu and Topaloglu (2011) find that most institutional and individual investor
groups are uninformed prior to takeovers and earnings announcements; yet, a group of large
hedge fund traders sells prior to negative earnings announcements. These studies, which are
- 11 -
based on proprietary data, have disadvantages since their samples are restricted in their coverage
of institutional investors, and the time span they investigate is relatively short. Other papers use
the TAQ database in order to identify trading by institutional and individual investors based on
trade size. Lee and Radhakrishna (2000) evaluate the performance of several alternative cutoff
rules and find that a $20,000 cutoff most effectively classifies institutional trades in small stocks.
However, Campbell, Ramadorai and Schwartz (2009) find that Lee and Radhakrishna’s approach
performs poorly when benchmarked against the quarterly institutional holdings in 13F filings. In
this study, we test whether domestic or foreign institutions can take advantage of their private
information before the earnings announcements using the Korean data that includes the daily net
buys grouped by individuals, institutions and foreigners. Many previous studies have examined
the behavior of institutions and individuals around earnings announcements. It has been argued
that institutional trading is driven by their superior information gathering and processing skills.
Hendershott, Livdan and Schürhoff (2012) present that the institutional order flow (buy volume
minus sell volume) increases during a period of more than five days prior to the announcement
of good news and decreases prior to bad news announcement. We argue that institutions have
more incentive to acquire private information before negative earnings surprises than before
positive earnings surprises since more private information can be acquired prior to the negative
event. Accordingly, informed investors, institutions, may trade stocks with anticipated negative
earnings surprises more actively in order to exploit their private information. To test this
argument, we measure a standardized trade imbalance by investor types as a proxy for buying
pressure before earnings announcements. We then test the following hypotheses:
- 12 -
H3: The trade imbalance by informed investors, institutions, is negative before negative
earnings surprises.
H3-1: The trade imbalance by informed or uninformed investors should be close to zero
before positive earnings surprises since information asymmetry is small before the positive event.
The extant literature provides evidence that foreign investors follow momentum trading
strategies unlike local individual investors. For instance, Choe, Kho and Stulz (1999) find such
evidence in the Korean market, and Kalev, Nguyen and Oh (2008) also find similar evidence for
stocks traded on the Helsinki stock exchange. Brennan and Cao (1997) argue that foreign
investors’ momentum strategies reflect their informational disadvantage in the local markets. In
this paper, we investigate whether foreign investors have informational advantage on the
upcoming earnings announcements, relative to local institutional or individual investors. We
expect that the trade imbalance by foreign institutions would not be directional if foreigners do
not have informational advantage relative to local investors. We test the following hypothesis:
H3-2: The trade imbalance by foreign institutions should be close to zero before negative
or positive earnings surprises.
If domestic institutions trade profitably using their superior information over the short
term, their trade imbalance before earnings announcement should be positively related to stock
returns after the announcement. This relation should hold for negative earnings surprises since
we expect that domestic institutions have informational advantage only on the upcoming
negative news. We also expect that trade imbalances by domestic individuals or foreigners are
not related to the stock returns. This conjecture is not consistent with Kaniel et al.’s (2012)
- 13 -
finding, in which the individuals trade profitably ahead of earnings announcements. We test the
following hypotheses:
H4: The trade imbalance by domestic institutions before negative earnings surprises is
positively related to stock returns after the events.
H4-1: The trade imbalance by domestic institutions does not forecast positive earnings
surprises.
H4-2: The trade imbalance by individuals or foreign institutions is not related to stock
returns after negative or positive earnings surprises.
One might think that institutional investors can use options to take advantage of their
superior information. In such a case, institutional investors’ trading activities may not be
incorporated into the stock market. However, the Korea Exchange provides a limited number of
options to investors. Currently only 33 individual stock options are traded in the derivative
market, while the total number of listed companies in the Korea Exchange is 1,789, as of the end
of 2012. Therefore, institutional investors are not able to take full advantage of their superior
information through option trading.
III. Data
To test our hypotheses, we first obtain the analysts’ reports on South Korean companies
from a database, FnConsensus of FnGuide.1 The South Korean financial data provider, FnGuide,
1
We also collect analysts’ forecast data from IBES. The dates of earnings announcements from IBES tend to be
later than the actual announcement dates. Therefore, we use the data from the local data provider, FnGuide.
- 14 -
has collected data on analysts’ reports since 2000. Due to this time limitation, our sample
includes analysts’ forecast data for South Korean companies over the fiscal years of 2001-2010.
We exclude earnings forecast data for the upcoming quarters and only include forecast data for
the fiscal year-end EPS. We collect each analyst’s last earnings forecast prior to earnings
announcements as well as the actual earnings of each company from the FnConsensus. We then
combine accounting data, trading volume by investor types, and the stock return data with
analysts’ forecast data. We obtain the annual accounting data over the sample period of 20012010 from Total Solution 2000 (TS 2000), a database compiled by the Korean Listed Companies’
Association. We obtain the daily trading volume of each stock by domestic individuals, domestic
institutions and foreigners from the Korea Exchange. We also obtain stock return data and the
Korean market index from a database (KIS-value) of Korean Information Service (KIS). The KIS
is affiliated with Moody’s and is a leading provider of credit-related information and services in
South Korea. We exclude financial companies since such companies are heavily regulated and
are less subject to information asymmetry compared to industrial companies. Our final sample
consists of 1,978 firm-years (or earnings announcements) representing 590 distinctive firms,
which have been traded for some period over the years 2001-2010.
Table 1 reports the number of firms over our sample period and the descriptive statistics
of firm characteristics. Since we assume that information asymmetry is not the same before
positive vs. negative earnings surprises, we divide our sample into firms announcing positive
earnings surprises and firms announcing negative earnings surprises based on the following
analyst’ earnings forecast error (AEFE).
- 15 -
AEFEi,t 
Actuali,t - Forecasti,t
Actuali,t
(1)
where AEFEi,t denotes the analyst earnings forecast error for firm i in fiscal year t,
Actuali,t is the reported EPS (earnings per share) of firm i for fiscal year t and Forecasti,t is the
consensus EPS forecasts of firm i for the same year. The consensus EPS forecast for a firm is the
average of the last forecasts of analysts covering the firm for the fiscal year-end EPS. If AEFEi,t
is positive (negative), the firm announces a positive (negative) earnings surprise. Panel A of
Table 1 exhibits the number of firms announcing positive and negative earnings surprises by year.
For the year 2001, our sample includes only 39 firms since a small number of firms were covered
by analysts in the early 2000s. The number of firms has increased to 275 in 2005, and following
2005, remains at a figure greater than 200. Out of 1,978 firm-years in the total sample, 590 firmyears (29.8%) announced positive earnings surprises while 1,388 firm-years (70.2%) announced
negative earnings surprises.
[Insert Table I about here]
Panel B of Table 1 summarizes the variables that represent the characteristics of the
sample firms. The mean total assets and market capitalization are approximately US$1.7 billion
and US$1.3 billion, respectively at the end of the fiscal year. The mean actual earnings per share
(EPS) is about US$3.4, with a standard deviation of US$7.9. The mean profitability (EBIT/total
assets) is 8.7% and the mean leverage (total debt/total assets) is 40.5%. Also, the mean dividend
yield is 2.1% and the mean market to book ratio of equity is 1.7.
We then divide the stocks with negative earnings surprises into three portfolios (P1, P2,
and P3) with equal number of stocks, and also divide stocks with positive earnings surprises into
- 16 -
three portfolios (P4, P5, and P6) with equal number of stocks based on analysts’ EPS forecast
errors (AEFE). On average, P1 has the smallest AEFE and P6 has the largest AEFE. To test our
hypotheses from H1 to H4, we use the sample firms in P1, P2, P5 and P6 since firms in P3 and
P4 announce actual earnings close to market expectation. The number of firm-year observations
in P1, P2, P5, and P6 is 1,319 (394 firm-year observations with positive surprises and 925 firmyear observations with negative surprises). Our main empirical tests are based on the sub-sample
of 1,319 firm-years, hereafter.
IV. Empirical findings
4.1. Information asymmetry prior to earnings announcements
In this section, we first examine whether firms with negative earnings surprises are more
likely to have higher information asymmetry, using the proxies such as firm size, analyst
following, idiosyncratic risk and Amihud’s illiquidity ratio. Similar to Ferreira and Laux (2007),
we first estimate the market model using daily returns over the two periods, from -51 to -26 and
from -25 to -1, respectively, based on Equation 2, and measure the idiosyncratic risk for each
stock based on Equation 3.
𝑟𝑖,𝑡 = 𝛼𝑖 + 𝛽𝑖 𝑟𝑚,𝑡 + 𝑒𝑖,𝑡
2
𝜎𝑖,𝑒
= 𝜎𝑖2 −
2
𝜎𝑖,𝑚
2
𝜎𝑚
(2)
(3)
where 𝑟𝑖,𝑡 is the excess return of stock i on day t, 𝑟𝑚,𝑡 is the value-weighted excess
market index return on day t, 𝜎𝑖2 is the variance of stock i, 𝜎𝑖,𝑚 is the covariance between 𝑟𝑖,𝑡
2
and 𝑟𝑚,𝑡 , and 𝜎𝑚
is the variance of 𝑟𝑚,𝑡 . We also measure illiquidity of stocks following
Amihud (2002) and use the Amihud’s illiquidity ratio as our proxy for information asymmetry
- 17 -
since this measure reflects the price impact of informed trading. Amihud’s illiquidity ratio is
defined as a ratio of absolute stock returns to trading volume. Using daily stock returns and
trading volume, we measure Amihud’s illiquidity ratio over the two periods, from -50 to -26 and
from -25 to -1, respectively, based on Equation 4.
Amihudi,[t1 ,𝑡2 ] =
|Ri,d |
Voli,d
∑td=1
Di,[t1 ,𝑡2]
× 109
(4)
where R i,d is the return of stock i on day d, Voli,d is the trading volume of stock i on
day d in Korean won, and Di,[t1 ,t2 ] is the number of days over the event period from t1 to t2.
In Table 2, we report that firms with negative earnings surprise are smaller in size and
have smaller analyst following, higher idiosyncratic risk and higher illiquidity ratio than firms
with positive earnings surprises. The result on firm size suggests that small firms may announce
negative earnings surprises more frequently. The finding that analyst following is larger for
positive earnings surprises indicates that public information is more prevalent before positive
earnings surprises than negative earnings surprises. The result on idiosyncratic risk implies that
negative-surprise firms induce more informed trading based on private information than do
positive-surprise firms. Higher illiquidity ratio for negative-surprise firms implies that the price
impact of informed trading is higher for negative-surprise firms than positive-surprise firms in
the pre-disclosure period. In general, our findings indicate that firms announcing negative
earnings surprise are more likely to have higher information asymmetry.
4.2. Stock return movement around earnings announcements
Next we investigate how stock prices move prior to earnings announcements. We
conjecture that stock prices increase prior to positive earnings surprises due to information
- 18 -
leakage but do not decrease prior to negative earnings surprises (H1). To test this hypothesis, we
measure the abnormal return (AR) and the cumulative abnormal return (CAR) around earnings
announcements using a standard event study.2 We measure the abnormal return based on the
market model. We use value-weighted market index of all listed companies as a proxy for the
market portfolio and one-year period prior to each event (trading days of -275 to -26) as an
estimation period. Then we calculate CAR over the event period from day t1 to t2 as below:
t2
CAR(t1 ,t2 )   ARt
(5)
t=t1
Table III reports the mean CARs over several event periods around positive and negative
earnings surprises. The results show that stock prices begin increasing before the announcements
of positive earnings surprises. The mean CAR of the pre-disclosure period, trading days from -25
to -1, is 1.19% and is significantly different from zero at the 5% confidence level. The results
also show that the stock prices continue to increase after the announcement of the positive event.
However, stock prices do not decline before the announcements of negative earnings surprises.
The mean CAR over the period of days from 0 to 5 is -1.51% and statistically significant at the 1%
confidence level, which means that stock prices reflect the negative information only after the
announcement. The results of the mean difference tests show that CARs are significantly
different over all event periods around positive vs. negative earnings surprises.
[Insert Table III about here]
As mentioned in the previous section, we focus on the abnormal returns of the stocks in
P1, P2, P5 and P6 since firms in P3 and P4 announce actual earnings that are close to market
2
The returns are calculated using a logarithmic return.
- 19 -
expectation. We report the stock returns of the firms in P1, P2, P5 and P6 in Table IV. We find
that stock prices of the firms in P5 and P6 start to rise before the announcements and continue to
rise after positive news releases over some period, which is consistent with the post-earningsannouncement drift (PEAD).3 In contrast, stock prices of the firms in P1 and P2 do not decline
before the announcements and start to decline only after the announcements of the negative news
releases. The results in Tables III and IV are consistent with our first hypothesis (H1), that
information on upcoming positive news is more likely to be leaked to the market before the event
than information on upcoming negative news.
[Insert Table IV about here]
4.3. Abnormal turnover around earnings announcements
We investigate the trading volume around earnings announcements following Foster and
Viswanathan (1995) and Chae (2005). They find that the trading volume decreases before the
scheduled announcements since uninformed investors try to avoid trades initiated by informed
investors. We calculate a turnover (TO) of firm i on a specific day t as follows:
TOi,t  log(
Trading Volumei,t
Outstandingi,t
3
 0.00000255) ,
(6)
Since Ball and Brown (1968) reported the phenomenon of PEAD, the subsequent literature has extensively
investigated the phenomenon.
- 20 -
where Trading Volume
i,t
means trading volume of firm i on day t and Outstandingi,t
means the number of the firm’s outstanding stock on the same day.4 We then calculate the
abnormal trading volume (ATO) of firm i on a specific day t as follows:
ATOi,t  TOi,t - TOi
,
(7)
where the average turnover in the pre-event period (days -55 to -26) is calculated as
-55
TOi 
 TO
i,t
t=-26
30
.
(8)
Table V reports the cumulative abnormal turnovers (ATO) over different event periods
around the announcements of earnings surprises. Panel A reports the ATO by aggregate investors.
We find in the total sample that the ATO is significantly negative before earnings announcements,
but is significantly positive over the event period of days 0 to 5. The result indicates that
investors tend to decrease trading activities due to information asymmetry before the event, but
increase trading activities after the event, which is consistent with Chae’s (2005) result.
Moreover, we discover that ATO is not statistically different from zero before the announcements
of the positive earnings surprises, whereas ATO is negative and is statistically significant at the 1%
confidence level before the announcements of the negative events. This finding is consistent with
our conjecture that information asymmetry is more severe before negative events. The finding
also indicates that Chae’s result might be driven by information asymmetry prior to the negative
event, even though our sample is different from Chae’s.
[Insert Table V about here]
4
We add a small constant of 0.0000255 in Equation 4 in order to handle the problem of log transformation of a zero
trading volume. The value of a constant is chosen following Llorente, Michaely, Gideon, and Wang (2002).
- 21 -
Panel B of Table V reports abnormal turnover by institutions around earnings
announcements. We find that ATO is not statistically different from zero around positive or
negative earnings surprises. Panel C shows that abnormal turnovers by individual investors
decrease around earnings announcements. We specifically find that the ATO over the event
period of days -5 to -1 is not statistically significant before positive earnings surprises, while the
ATO over the same period is significantly negative at the 5% level prior to negative earnings
surprises. Panel D reports ATO by foreigners and shows that the pattern of their trading activities
is similar to that of individual investors’ trading activities. These results suggest that domestic
institutions are more informed on upcoming earnings news, compared to individuals and
foreigners; and that the information asymmetry problem is more severe before negative earnings
surprises.
4.4. Trade imbalance around earnings announcements
The trading volume does not show who buys (or sells) the stock with upcoming positive
or negative earnings surprises. Following Malmendier and Shanthikumar (2006) and Lai and Teo
(2008), we utilize a standardized trading imbalance in order to measure the direction of trading
by each type of investor before the earnings news. We first calculate the trade imbalance (TI) on
firm i by investor type x on day t as follows:
TI i,x,t 
Buy Volumei,x,t - Sell Volumei,x,t
Buy Volumei,x,t  Sell Volumei,x,t
(9)
Using the standard deviation of the TI over the year, we then normalize TI in order to attain the
standardized trade imbalance (STI) as follows:
- 22 -
STI i,x,t 
TI i,x,t - TI i,x,year(t)
(10)
std(TI i,x,year(t) )
Panel A of Table VI shows the cumulative STI by individuals, institutions and foreigners
around the announcements of positive earnings surprises. We find that the STIs by any type of
investors are not different from zero over the event period of days from -5 to -1, from 0 to 1 or
from 0 to 5 around the positive earnings surprises. The ANOVA test also shows that no STI is
different from those by other investor types. The result indicates that no investors, regardless of
individuals, institutions, or foreigners, directionally trade over short-term periods around the
announcements of positive earnings surprises. Panel B shows the STI by investor type around the
announcements of negative earnings surprises. We find that the STI by individuals is positive and
statistically significant before and after the negative news, suggesting that individual investors
trade stocks in the opposite direction to the news. The STI by institutions over the period of days
from -5 to -1 is negative and statistically significant at the 1% level, implying that domestic
institutions sell stocks anticipating negative earnings surprises. We also find that trading by
foreign investors is not directional around the negative news. The ANOVA test shows that each
STI is significantly different from those by other investor types. Our finding that individual
investors are net buyers around negative earnings surprises, and foreign investors do not show
systematic response to earnings surprises, is consistent with the findings of Ekholm (2006). He
finds that Finnish households are more likely to increase shareholdings at negative earnings news
and that the trading activities of foreign investors are not significantly related to earnings
surprises. As pointed out in Ekholm (2006), overconfidence of less sophisticated investors can be
a possible explanation for individual investors’ trading behavior that is opposite to earnings
surprises. Our result on foreign investors supports the prior literature on the Korean stock market
- 23 -
(e.g. Choe, Kho, and Stulz 1999, 2005), which does not find informational advantage of foreign
investors over domestic investors.
[Insert Table VI about here]
In Table VII, we report the STI by investor type around the earnings news for each
portfolio formed by analysts’ EPS forecast errors. In P1, the STI by individual investors over the
period of days from -5 to -1 is 0.36 and significantly different from zero; while that by
institutional investors is -0.41 and significantly different from zero. The result indicates that
domestic institutions sell stocks anticipating large negative surprises, whereas individual
investors buy those stocks. In P1, the STI by any investor type is not different from zero after the
earnings news for the stocks. Institutional investors also sell the stocks in P2 before the negative
news.
[Insert Table VII about here]
The result in Table VII also shows that the STI by any investor type is not statistically
different from zero for the stocks in P5 and P6 before and after positive news. To summarize: 1)
domestic institutions make informed trading before negative earnings surprises, 2) individual
investors trade in the opposite direction to the negative news, 3) foreign investors do not
directionally trade around negative earnings surprises, and 4) investors do not show any
directional trading behaviors around positive earnings surprises. These results are generally
consistent with our hypotheses, H3, H3-1 and H3-2.
4.5. Trade imbalance and after-announcement stock returns
To further investigate institutions’ informed trading, we investigate whether their trading
predicts the abnormal return in multivariate regressions. Specifically, we attempt to find the
- 24 -
relation between cumulative abnormal return (CAR) over the period of days from 0 to 5 and the
STI by each investor type over the period of days from -5 to -1. We primarily estimate the
following equation.
𝑪𝑨𝑹[𝟎, 𝟓]𝒊𝒕 = 𝜷𝟎 + 𝜷𝟏 𝑺𝑻𝑰[−𝟓, −𝟏]𝒊𝒕 + 𝜷𝟐 𝑬𝑺𝒊𝒕 + 𝜷𝟑 𝑪𝑨𝑹[−𝟓, −𝟏]𝒊𝒕 + 𝜷𝟒 𝑭𝒊𝒓𝒎 𝑺𝒊𝒛𝒆𝒊𝒕 +
𝜷𝟓 𝑴𝑻𝑩𝒊𝒕 + 𝜷𝟔 𝑵𝒖𝒎𝑨𝒏𝒂𝒍𝒊𝒕 + 𝜺𝒊𝒕
(11)
where CAR[0,5] is the cumulative abnormal returns over the event period of days from 0
to 5; STI[-5,-1] is the standardized trade imbalance over the event period of days from -5 to -1;
ES is the earnings surprise dummy, which is one if analysts’ EPS forecast errors are larger than
the median and zero otherwise; CAR[-5,-1] is the cumulative abnormal returns over the event
period of days from -5 to -1; Firm Size is the natural log of market capitalization; MTB is the
market to book ratio; and NumAnal is the number of analysts covering each firm for the fiscal
year. We estimate the equation using OLS with clustered standard errors and OLS with time
fixed effects; the results of OLS with clustered standard errors (OLS with time fixed effects) are
reported in Panel A (Panel B) of Table VII.5 Models 1 to 3 (Models 4 to 6) show the regression
results for stocks with positive (negative) earnings surprises. The results in Panel A and those in
Panel B are qualitatively similar. In Models 1 to 3, the coefficients on the STIs are not
statistically significant. This result suggests that investors either do not trade stocks anticipating
positive earnings surprises, or they cannot exploit other types of investors since there is little
information asymmetry due to information leakage prior to the announcements. In Models 4 to 6,
the coefficient on STI[-5,-1] by institutional investors is significantly positive at the 5%
confidence level; the coefficients on STI[-5,-1] by individuals and foreigners are not statistically
5
Refer to Petersen (2009) for an explanation on OLS with clustered standard errors and OLS with time fixed effects.
- 25 -
significant. The result implies that institutions sell their stocks anticipating negative returns after
announcements. The results in Table VIII support H4 and H4-1 which state that trade imbalance
by institutions is positively related to stock returns only after the announcements of negative
earnings surprises. They also support H4-2 that trade imbalances by individual or foreign
investors do not predict stock returns after the announcements of earnings news.
[Insert Table VIII about here]
In summary, we find that stock prices begin to rise before the announcements of positive
earnings surprises due to information leakage. We then find that the trading volume decreases
only before negative earnings surprises; however, the decrease is driven by individuals and
foreign investors. We also find that institutional investors directionally trade only before the
negative news since they have informational advantage on the upcoming announcements of
negative earnings surprises. Finally, we document that foreign investors do not have superior
information on the most important corporate event, earnings announcements.
4.6. Robustness check
In the previous sections, we use the analysts’ earnings forecasts error (AEFE) to classify
earnings news into positive and negative earnings surprises. This approach assumes that analysts’
forecasts reflect the true market expectations about upcoming earnings news. However, analysts’
forecasts can be a noisy measure for market expectation if analysts failed to reflect all available
information into their forecasts and/or they were biased in some ways. Therefore, in this
subsection, we use market reactions to earning announcements as an alternative measure for
market expectation, assuming that positive (negative) market reaction after announcements is the
reflection of positive (negative) market shocks. Specifically, we first measure the cumulative
- 26 -
abnormal returns over the event period of 0 to 5. Abnormal return is estimated based on the
market model. We define the firms with a positive (negative) CAR as the firms with a positive
(negative) earnings surprise. Then we investigate investors’ trading activities prior to positive vs.
negative earnings surprise and report the results in Table IX. Panel A shows that individual
investors and foreigners decrease their trading volume prior to negative earnings surprises while
they do not decrease it prior to positive surprises. There are no significant changes in institutional
investors’ trading volume prior to both positive and negative surprise. Panel B shows that only
institutions sell stocks with negative surprises in the pre-disclosure period. The result of Panel A
(Panel B) is qualitatively similar to the results of Table V (Table VI) in which earnings news are
classified based on the analysts’ earnings forecasts error. In general, the results of Table IX are
consistent with our conjecture that information asymmetry is higher prior to negative earnings
surprises and institutional investors exploit their superior information in the pre-disclosure period
of negative news.
[Insert Table IX about here]
V. Conclusion
In this study, we investigate informed trading around earnings announcements.
Specifically, we examine 1) whether information asymmetry prior to earnings announcement is
related to the content of earnings information (positive vs. negative surprise to the market), and 2)
whether informed traders exploit their informational advantage over the short-term period around
earnings announcements. We conjecture that information asymmetry is higher before negative
earnings surprises, compared to positive surprises, because firms are more likely to leak positive
news and delay the disclosure of negative news. In such a case, investors would have greater
- 27 -
incentives to collect private information on upcoming negative earnings news rather than
positive news. Hence, sophisticated investors, such as institutional investors, would take
advantage of their superior information around the negative news.
Using Korean data over the period of 2001-2010, we document that information
asymmetry proxied by firm size, analyst following, idiosyncratic risk, and Amihud’s illiquidity
ratio is higher for firms with negative earnings surprises than for those with positive surprises.
Stock price movement and trading volume also indicate higher information asymmetry prior to
negative earnings surprises. Specifically, we find that 1) stock prices begin increasing before the
announcements of positive earnings surprises, but do not decline before the announcements of
negative surprises; and 2) trading volume decreases prior to negative earnings surprises, but does
not decrease prior to positive surprises. We then find that domestic institutions sell their stock
anticipating negative news; however, individual investors trade their stocks in the opposite
direction to the news. We do not find evidence that foreign investors directionally trade stocks
around the earnings news. The results imply that institutional investors anticipate negative events
and move ahead of other investors to exploit their informational advantage. To strengthen the
results, we find that trade imbalance by institutions predicts stock returns upon the negative news.
This paper adds new evidence to the extant literature on the relation between
information asymmetry and earnings announcements. While the prior literature provides
evidence of higher information asymmetry before earnings announcement, we differentiate the
level of information asymmetry before positive vs. negative surprises, and find that the
information asymmetry is higher before negative surprises. This result is consistent with the
theory and empirical findings that firms voluntarily disclose positive news, but hide negative
news. Our paper also complements prior research, by providing evidence of short-term
- 28 -
informational advantage of institutional investors over other investors around earnings
announcement. We find that institutions exploit their informational advantage especially before
negative earnings surprises. We also contribute to the growing literature on foreign investors’
trading in emerging markets. We find that foreign institutions’ trading is not systematic around
earnings announcements and does not predict stock returns. This shows that foreign institutions
do not possess informational advantage, compared to local institutions around the specific
corporate event.
- 29 -
REFERENCES
Atiase, Rowland K., 1985, Predisclosure information, firm capitalization, and security price
behavior around earnings announcements, Journal of Accounting Research 23, 21-36.
Amihud, Yakov, 2002, Illiquidity and stock returns: Cross-section and time series effects,
Journal of Financial Markets 5, 31-56.
Baker, Malcolm, Lubomir Litov, Jessica A. Wachter, and Jeffrey Wurgler, 2010, Can mutual
fund managers pick stocks? Evidence from their trades prior to earnings announcements,
Journal of Financial and Quantitative Analysis 45, 1111-1131.
Ball, Ray and Philip Brown, 1968, An empirical evaluation of accounting income numbers,
Journal of Accounting Research 6, 159-178.
Brennan, Michael J., and H. Henry Cao, 1997, International portfolio investment flows, Journal
of Finance 52, 1851-1880.
Brennan, Michael J. and Avanidhar Subrahmanyam, 1995. Investment analysis and price
formation in securities markets, Journal of Financial Economics 38, 361-381.
Campbell, John Y., Tarun Ramadorai, and Allie Schwartz, 2009, Caught on tape: Institutional
trading, stock returns, and earnings announcements, Journal of Financial Economics 92, 6691.
Chae, Joon, 2005, Trading volume, information asymmetry, and timing information, Journal of
Finance 60, 413-442.
Choe, Hyuk, Bong-Chan Kho, and René M. Stulz, 1999, Do foreign investors destabilize stock
markets? The Korean experience in 1997, Journal of Financial Economics 54, 227-264.
Choe, Hyuk, Bong-Chan Kho, and René M. Stulz, 2005, Do domestic investors have an edge?
The trading experience of foreign investors in Korea, Review of Financial Studies 18, 795829.
Ekholm, Anders, 2006. How Do Different Types of Investors React to New Earnings
Information?, Journal of Business Finance & Accounting 33, 127-144.
Ferreira, Miguel A. and Paul A. Laux, 2007, Corporate governance, idiosyncratic risk, and
information flow, Journal of Finance 62, 951~989.
Foster, F. Douglas, and S. Viswanathan, 1995, Can speculative trading explain the volumevolatility relation? Journal of Business and Economic Statistics 13, 379-396.
Froot, Kenneth, and Melvyn Teo, 2008, Style investing and institutional investors, Journal of
Financial and Quantitative Analysis 43, 883-906.
Graham, John R., Campbell R. Harvey, and Shiva Rajgopal, 2005, The economic implications of
corporate financial reporting, Journal of Accounting and Economics 40, 3-73.
- 30 -
Griffin, John M., Jeffrey H. Harris, and Selim Topaloglu, 2003, The dynamics of institutional
and individual trading, Journal of Finance 58, 2285-2320.
Griffin, John M., Tao Shu, and Selim Topaloglu, 2011, Examining the dark side of financial
markets: Who trades ahead of major announcements? University of Texas working paper.
Grinblatt, Mark, and Matti Keloharju, 2000, The investment behavior and performance of
various investor types: a study of Finland's unique data set, Journal of Financial Economics
55, 43-67.
Hendershott, Terrence, Dmitry Livdan, and Norman Schürhoff, 2012, Are Institutional Informed
About News? University of California, Berkeley working paper.
Kalev, Petko S., Anh H. Nguyen, and Natalie Y. Oh, 2008, Foreign versus local investors: Who
knows more? Who makes more? Journal of Banking and Finance 32, 2376-2389.
Kaniel, Ron, Shuming Liu, Gideon Saar, and Sheridan Titman, 2012, Individual investor trading
and return patterns around earnings announcements, Journal of Finance, forthcoming.
Ke, B. I. N., and Kathy Petroni, 2004, How Informed Are Actively Trading Institutional
Investors? Evidence from Their Trading Behavior before a Break in a String of Consecutive
Earnings Increases, Journal of Accounting Research 42, 895-927.
Kim, Oliver, and Robert E. Verrecchia, 1991, Trading volume and price reactions to public
announcements, Journal of Accounting Research 29, 302-321.
Kothari, S. P., Susan Shu, and Peter D. Wysocki, 2009, Do Managers Withhold Bad News?,
Journal of Accounting Research 47, 241-276.
Krinsky, Itzhak and Jason Lee, 1996. Earnings Announcements and the Components of the BidAsk Spread, Journal of Finance 51, 1523-1535.
Lang, Mark, and Russell Lundholm, 1993, Cross-sectional determinants of analysts ratings of
corporate disclosures, Journal of Accounting Research 31, 246-271.
Lai, Sandy, and Melvyn Teo, 2008, Home-biased analysts in emerging markets, Journal of
Financial and Quantitative Analysis 2008, 685-716.
Lee, Charles M. C., and Balkrishna Radhakrishna, 2000, Inferring investor behavior: Evidence
from TORQ data, Journal of Financial Markets 3, 83-111.
Lee, Charles M. C., Belinda Mucklow, and Mark J. Ready, 1993. Spreads, depths, and the
impact of earnings information: an intraday analysis, Review of Financial Studies 6, 345-374.
Lev, Baruch and Stephen H. Penman, 1990, Voluntary forecast disclosure, nondisclosure, and
stock prices, Journal of Accounting Research 28, 49-76.
Llorente, Guillermo, Roni Michaely, Gideon Saar, and Jiang Wang, 2002, Dynamic volumereturn relation of individual stocks, Review of Financial Studies 15, 1005-1047.
- 31 -
Malmendier, U. and D. Shanthikumar, 2006, Are investors naïve about incentives? NBER
working paper.
McNichols, Maureen, and Brett Trueman, 1994, Public disclosure, private information collection,
and short-term trading, Journal of Accounting and Economics 17, 69-94.
Petersen, Mitchell A., 2009, Estimating standard errors in finance panel data sets: comparing
approaches, Review of Financial Studies 22, 435-480.
Roulstone, Darren T., 2003. Analyst Following and Market Liquidity, Contemporary Accounting
Research 20, 551-578.
Verrecchia, Robert E., 1983, Voluntary disclosure, Journal of Accounting and Economics 5, 179194
Yan, Xumin and Zhe Zhang, 2009, Institutional investors and equity returns: Are short-term
institutions better informed? Review of Financial Studies 22, 893-924.
- 32 -
Table I
Descriptive Statistics of Earnings Announcements
Panel A: Number of Positive Earnings Announcements and Negative Earnings Announcements
Year
Positive Earnings Surprises
Negative Earnings Surprises
Total
2001
10
32
42
2002
29
61
90
2003
55
120
175
2004
59
135
194
2005
80
195
275
2006
76
177
253
2007
53
171
224
2008
64
156
220
2009
86
160
246
2010
78
181
259
Total
590
1,388
1,978
Panel B: Descriptive Statistics for Earning Announcement Firms
Variables
Mean
Median
Max.
Min.
Std. Dev.
Total Assets
1,710.06
343.60
58,426.20
14.36
4,157.77
Market Capitalization
1,330.08
203.13
73,361.37
1.40
3,971.05
Actual EPS
3.36
1.27
112.23
-23.72
7.89
NumAnal
7.01
4.00
30.00
1.00
6.58
EBIT/Total Assets (%)
8.74
7.66
49.00
-22.21
7.35
Debt/Total Assets (%)
40.52
40.74
97.32
1.69
18.91
Dividend Yield (%)
2.06
1.64
45.52
0.00
2.23
MTB
1.72
1.16
27.13
0.03
2.49
Panel A reports the number of firms announcing positive and negative earnings surprises each year. If the
actual EPS (earnings per share) for a fiscal year is larger (smaller) than the average of the last EPS
forecasts made by analysts, earnings announcements are classified as positive (negative) earnings
surprises. Panel B reports the mean, median, maximum, minimum and standard deviation of variables
representing firm characteristics. Total assets and market capitalization are measured at the end of the
fiscal year in U.S. million dollars. Actual EPS is the reported EPS by the firm and the value is in U.S.
dollar. NumAnal is the number of analysts covering each firm for the fiscal year. EBIT/total assets is the
earnings before interest and taxes divided by total assets; debt/total assets is a percentage of total debt
divided by total assets. Dividend yield is the ratio of cash dividend paid per share over the fiscal year to
the stock price. MTB is the ratio of market value of equity to book value of equity at the end of the fiscal
year.
33
Table II
Information Asymmetry before Earnings Announcements
Positive Earnings
Negative Earnings
Surprises
Surprises
Difference Test
Variables
Mean
Median
Mean
Mean
Median
(t-value)
(z-value)
Median
Firm Attributes
Market Capitalization
NumAnal
1,388.73
7.20
213.63
903.41
173.19
2.68**
1.46
4.00
5.98
3.00
3.08**
2.10*
Idiosyncratic risk
[-50, -26]
0.0777
0.0682
0.0850
0.0730
-2.65**
-2.63**
[-25, -1]
0.0770
0.0687
0.0842
0.0730
-2.69**
-2.51**
[-50, -26]
0.0991
0.0124
0.1809
0.0178
-1.89*
-1.99*
[-25, -1]
0.1138
0.0132
0.2605
0.0197
-2.36**
-2.47**
Amihud’s illiquidity ratio
Market capitalization is measured at the end of the fiscal year in U.S. million dolloars. NumAnal is the
number of analysts covering each firm for the fiscal year. Idiosyncratic risk is measured following
Ferreira and Laux (2007) over the event period from -50 to -1 and from -25 to -1. Amihud’s illiquidity
ratio is measured following Amihud (2002) over the event period from -50 to -26 and from -25 to -1. *
and ** indicate statistical significance at the 5% and 1% level, respectively.
34
Table III
Cumulative Abnormal Returns around Earnings Announcements
Positive Earnings Surprises
Negative Earnings Surprises
Mean
(N=394)
(N=925)
Difference Test
Event
Period
CAR
t-value
CAR
t-value
t-value
[-25,-1]
0.0119
1.90*
-0.0073
-1.61
2.49**
[-10,-1]
0.0113
2.62**
-0.0027
-0.96
2.74**
[-5,-1]
0.0083
2.94**
-0.0021
-1.02
2.99**
[0,1]
0.0109
5.01**
-0.0108
-7.21**
8.04**
[0,5]
0.0135
3.99**
-0.0151
-6.73**
6.99**
[0,10]
0.0164
3.69**
-0.0202
-7.16**
7.03**
This Table reports the mean cumulative abnormal returns (CARs) over different event periods and the
results of mean difference tests of the CARs between positive vs. negative earnings surprises. CAR is
measured based on abnormal returns from the market model. Day 0 is the earnings announcement date. *,
and ** indicate statistical significance at the 5% and 1% level, respectively.
35
Table IV
Cumulative Abnormal Returns around Earnings Announcements: Portfolio Sorting by
AEFE
Portfolios
Event
Period
P6-P1
P1
P2
P5
P6
-0.0037
-0.0005
0.0076
0.0091
(-1.32)
(-0.17)
(2.01)*
(2.15)*
-0.0158
-0.0058
0.0051
0.0166
(-7.55)**
(-2.73)**
(1.96)*
(4.85)**
-0.0190
-0.0113
0.0099
0.0171
(-5.81)**
(-3.66)**
(2.31)*
(3.27)**
[-5,-1]
t-value
2.52**
[0,1]
8.30**
[0,5]
5.95**
The Table reports the cumulative abnormal returns (CARs) of each portfolio sorted by analysts’ EPS
forecast errors (AEFE). We divide the stocks with negative earnings surprises into three portfolios (P1, P2,
P3) with equal number of stocks and also divide the stocks with positive earnings surprises into three
portfolios (P4, P5, P6) with equal number of stocks based on AEFE. P1 has the smallest AEFE and P6 has
the largest AEFE on average. Day 0 is the earnings announcement date. The numbers in parentheses are tvalues. * and ** indicate statistical significance at the 5% and 1% level, respectively.
36
Table V
Cumulative Abnormal Turnover around Earnings Announcements by Investor Type
Panel A: Aggregate Investors
Total sample
Event
Positive Earnings Surprises
Period
ATO
[-5,-1]
-0.3189
-2.95**
-0.1511
[0,1]
0.3403
7.37**
0.5123
[0,5]
0.3456
2.66**
0.7475
t-value
ATO
t-value
Negative Earnings Surprises
ATO
t-value
-0.3904
-2.95**
6.08**
0.2670
4.86**
3.27**
0.1744
1.11
-0.81
Panel B: Institutions
Total sample
Event
Positive Earnings Surprises Negative Earnings Surprises
Period
ATO
[-5,-1]
0.2255
1.27
-0.0472
-0.15
0.3438
1.59
[0,1]
-0.0204
-0.24
-0.1335
-1.93
0.0258
0.25
[0,5]
-0.0575
-0.30
-0.2118
-0.65
0.0096
0.04
t-value
ATO
t-value
ATO
t-value
Panel C: Individuals
Total sample
Event
Positive Earnings Surprises Negative Earnings Surprises
Period
ATO
[-5,-1]
-0.4233
-2.54**
-0.3611
-1.13
-0.4492
-2.30*
[0,1]
-0.2145
-2.46**
-0.2298
-1.59
-0.2081
-2.25*
[0,5]
-0.5000
-2.34**
-0.4826
-1.25
-0.5073
-1.97*
t-value
ATO
t-value
ATO
t-value
Panel D: Foreigners
Event
Total sample
Positive Earnings Surprises Negative Earnings Surprises
Period
ATO
[-5,-1]
-0.7447
-3.75**
-0.2314
-0.59
-0.9627
-4.21**
[0,1]
-0.2970
-3.02**
-0.0782
-0.42
-0.3926
-3.42**
[0,5]
-0.7458
-3.10**
-0.2845
-0.61
-0.9493
-3.42**
t-value
ATO
t-value
ATO
t-value
Panel A, B, C, and D report cumulative abnormal turnover (ATO) by aggregate investors, institutions,
individuals, and foreigners over different event periods, respectively. Day 0 is the earnings announcement
date. * and ** indicate statistical significance at the 5% and 1% level, respectively.
37
Table VI
Cumulative Standardized Trade Imbalance (STI) around Earnings Announcements
Panel A: Positive Earnings Surprises
Event
Period
Individuals
STI
t-value
Institutions
STI
t-value
Foreigners
STI
t-value
ANOVA
(F-value)
[-5,-1]
-0.1188
-0.76
-0.0814
-0.50
0.1378
0.86
0.75
[0,1]
0.0427
0.55
-0.0981
-1.23
-0.0229
-0.29
0.82
[0,5]
-0.1189
-0.68
-0.2277
-1.42
0.2057
1.25
1.78
Panel B: Negative Earnings Surprises
Event
Individuals
Institutions
Period
STI
t-value
[-5,-1]
0.3089
2.70**
-0.3361
-3.46**
[0,1]
0.1235
2.38**
-0.0804
[0,5]
0.1975
1.65
-0.1879
STI
t-value
Foreigners
ANOVA
(F-value)
STI
t-value
0.0528
0.53
9.58**
-1.47
-0.0363
-0.67
4.12**
-1.62
-0.0882
-0.82
3.09**
Panel A (Panel B) reports cumulative standardized trade imbalance by three investor types, individuals,
institutions, and foreigners over different event periods for the firms announcing positive (negative)
earnings surprises. Day 0 is the earnings announcement date. If standardized trade imbalance positive
(negative), buying volume is larger (smaller) than selling volume. *and ** indicate statistical significance
at the 5% and 1% level, respectively.
38
Table VII
Cumulative Standardized Trade Imbalance around Earnings Announcements by Investor
Type: Portfolio Sorting by Analysts’ EPS Forecast Error
Event Period
Investor
Portfolio
[-5,-1]
[0,1]
[0,5]
Type
STI
t-value
STI
t-value
STI
t-value
P1
Individuals
0.3639
2.30*
0.0992
1.35
0.1218
0.73
CAR[0,5]
Institutions
-0.4105
-3.02**
-0.0676
-0.88
-0.1873
-1.19
-0.0190
Foreigners
0.0735
0.53
-0.0878
-1.17
-0.1683
-1.14
P2
Individuals
0.2540
1.54
0.1478
0.2732
1.60
CAR[0,5]
Institutions
-0.2662
-1.92*
-0.0922
-1.18
-0.1884
-1.11
-0.0113
Foreigners
0.0329
0.23
0.0124
0.16
-0.0099
-0.06
P5
Individuals
-0.1360
-0.60
0.0876
0.79
0.1158
0.46
CAR[0,5]
Institutions
0.2004
0.88
-0.0497
-0.44
-0.2651
-1.13
0.0099
Foreigners
0.1030
0.45
-0.1081
-0.98
0.0226
0.09
P6
Individuals
-0.1016
-0.48
-0.0023
-0.02
-0.3536
-1.44
CAR[0,5]
Institutions
-0.3740
-1.63
-0.1478
-1.33
-0.1901
-0.87
0.0171
Foreigners
0.1730
0.76
0.0629
0.57
0.3918
1.75
2.01*
The Table reports cumulative standardized trade imbalance (STI) by three investor types, individuals,
institutions, and foreigners over different event periods for each portfolio (P1, P2, P5 and P6). Day 0 is
the earnings announcement date. We divide the stocks with negative earnings surprises into three
portfolios (P1, P2, P3) with equal number of stocks and also divide the stocks with positive earnings
surprises into three portfolios (P4, P5, P6) with equal number of stocks based on analysts’ EPS forecast
errors (AEFE). P1 has the smallest AEFE and P6 has the largest AEFE on average. * and ** indicate
statistical significance at the 5%, and 1% level, respectively.
39
Table VIII
Regression on Cumulative Abnormal Returns (CARs)
Panel A: OLS with Clustered Standard Errors on Cumulative Abnormal Returns (CARs)
Institutions STI[-5,-1]
Individuals STI[-5,-1]
Foreigners STI[-5,-1]
Earning Surprise dummy
CAR[-5,-1]
Firm Size
Market to Book ratio of Equity
Number of Analysts following
Intercept
Adj. R2(%)
N
Dependent Variable CAR[0,5]
Positive Earnings Surprises
Negative Earnings Surprises
1
2
3
4
5
6
0.0174
0.0227
(1.42)
(2.40)**
-0.0117
-0.0132
(-0.87)
(-1.53)
0.0011
-0.0017
(0.08)
(-0.18)
0.0124
0.0105
0.0328
0.0073
-0.0209
-0.0119
(0.16)
(0.13)
(0.41)
(0.14)
(-0.40)
(-0.22)
0.2202
-0.3438
0.4766
-1.6938
-1.6348
-1.4686
(0.30)
(-0.45)
(0.63)
(-2.39)** (-2.23)*
(-2.11)*
-0.0290
-0.0681
-0.0280
0.0037
-0.0035
0.0132
(-1.10)
(-2.15)*
(-1.00)
(0.16)
(-0.16)
(0.56)
0.0015
0.0064
0.0092
-0.0303
-0.0163
-0.0179
(0.06)
(0.26)
(0.36)
(-2.80)**
(-1.52)
(-1.49)
0.0066
0.0518
0.0288
-0.0299
-0.0330
-0.0311
(0.09)
(0.67)
(0.38)
(-0.53)
(-0.57)
(-0.53)
0.8409
1.8601
0.7612
-0.1615
0.0093
-0.4531
(1.30)
(2.34)*
(1.10)
(-0.29)
(0.02)
(-0.76)
1.54
2.70
0.63
4.48
2.81
2.55
284
299
274
645
698
646
Panel B:OLS with Time Fixed Effects on Cumulative Abnormal Returns (CARs)
Institutions STI[-5,-1]
Individuals STI[-5,-1]
Foreigners STI[-5,-1]
ES
CAR[-5,-1]
Firm Size
MTB
NumAnal
Intercept
Year Dummy
Adj. R2(%)
N
Dependent Variable CAR[0,5]
Positive Earnings Surprises
Negative Earnings Surprises
1
2
3
4
5
6
0.0163
0.0207
(1.27)
(2.10)*
-0.0156
-0.0134
(-1.18)
(-1.74)
0.0052
0.0008
(0.38)
(0.09)
-0.0060
-0.0107
0.0240
0.0109
-0.0190
-0.0063
(-0.08)
(-0.13)
(0.30)
(0.20)
(-0.36)
(-0.12)
0.1878
-0.5913
0.4237
-1.7854
-1.7234
-1.5675
(0.26)
(-0.77)
(0.56)
(-3.99)** (-3.80)** (-3.54)**
-0.0239
-0.0611
-0.0220
0.0023
-0.0016
0.0116
(-0.74)
(-1.92)*
(-0.67)
(0.10)
(-0.07)
(0.48)
0.0101
0.0179
0.0203
-0.0281
-0.0137
-0.0154
(0.36)
(0.62)
(0.71)
(-2.89)** (-1.65)
(-1.78)
-0.0142
0.0380
0.0172
-0.0363
-0.0433
-0.0323
(-0.17)
(0.45)
(0.21)
(-0.57)
(-0.68)
(-0.50)
1.1673
2.4662
1.0925
0.1037
0.1827
-0.1676
(1.42)
(3.11)**
(1.31)
(0.17)
(0.31)
(-0.28)
yes
yes
yes
yes
yes
yes
0.02
3.34
0.18
4.15
2.46
2.54
284
299
274
645
698
646
40
Panel A (Panel B) reports the results of OLS with clustered standard errors (OLS with time fixed effect)
on cumulative abnormal returns (CAR) over the period of days, 0 to 5. Models 1 to 3 is the result for the
firms announcing positive earnings surprises and Models 4 to 6 is the result for the firms announcing
negative earnings surprises. Institutions, individuals, or foreigners STI represents cumulative standardized
trade imbalance by each investor type over the period of -5 to -1. ES is a dummy variable taking one if
analysts’ EPS forecast errors is larger than median and zero otherwise in Models 1 to 3, while it is one if
analysts’ EPS forecast errors is smaller than median and zero otherwise in Models 4 to 6. Firm size is a
natural log of market capitalization measured at the end of fiscal year. MTB is the ratio of market value of
equity to book value of equity at the end of fiscal year. NumAnal is the number of analysts covering each
firm for the fiscal year. t-values are in parentheses.* and ** indicate statistical significance at the 5% and
1% level, respectively.
41
Table IX
Trading Activities Prior to Earnings Announcements Classified by After-announcement
Stock Returns
Panel A: Abnormal Turnover (ATO) over the event period of -5 to -1
Positive Earnings Surprises
Negative Earnings Surprises
Investor Type
ATO
t-value
ATO
t-value
Institutions
0.0024
0.01
0.0693
0.37
Individuals
-0.3585
-1.74
-0.3814
-1.98*
Foreigners
-0.4616
-1.89
-0.6393
-3.02**
Panel B: Cumulative Standardized Trade Imbalance (STI) over the event period of -5 to -1
Positive Earnings Surprises
Negative Earnings Surprises
Investor Type
STI
t-value
STI
t-value
Institutions
-0.1780
-1.71
-0.3850
-4.26**
Individuals
0.2053
1.79
0.1845
1.91
Foreigners
0.1387
1.27
0.0037
0.04
Panel A (Panel B) reports the abnormal turnover (cumulative standardized trade imbalance) over the event
period of -5 to -1 by investor type. An earnings announcement is classified as a positive (negative) surprise if
the after-announcement cumulative abnormal return is positive (negative). The cumulative abnormal return
is measured over the event period of 0 to 5. * and ** indicate statistical significance at the 5% and 1% level,
respectively.
42
Figure 1
The Fraction of Market Capitalization Held by Each Investor Type
45
40
35
(%)
30
25
20
15
10
5
0
1999
2000
2001
2002
2003
Institutional investors
2004
2005
2006
2007
Individual investors
2008
2009
2010
2011
2012
Foreign investors
This figure shows the fraction of market capitalization held by institutional investors, individual investors,
and foreign investors over the period of 1999~2012.
43