Post-Earnings Announcement Drift and Ownership Structurein the Modern Japanese Stock Market*
JUNKO JINUSHI†
School of Business Administration,
HITOTSUBASHI UNIVERSITY

ABSTRACTThis study investigates the existence of post-earnings announcement drift (PEAD) in
the Japanese stock market in the 21st century. Drawing on rational inattention models,
which suggest that investors’ limited processing capacities cause an underreaction toearnings announcements, this study determines whether the underreaction still exists inthe modern Japanese stock market despite the development of technologies that assist
investors in processing information. Furthermore, it examines whether the ownership
structure, which reflects varying processing costs, affects the results. It reveals three key
findings using a sample of 60,124 firm-year observations on the Japanese stock market
from 2002 to 2020. First, PEAD decreases over the sample period. Second, it does notdecrease in earnings announcements of firms with low foreign investor ownership or
high individual investor ownership, indicating that underreaction persists when stocks
are traded by investors who do not apply sophisticated technologies and continue to incur
higher processing costs. Finally, this study attributes these changes in investors’ tradingstyle to an increase in the price response to earnings announcements.
JEL Classifications: G14, M41
Keywords: Post-Earnings Announcement Drift; Rational Inattention; Processing Cost;
Ownership Structure; Earnings Announcement* I thank Masahiro Enomoto (co-editor), Tetsuyuki Kagaya, Takuma Kochiyama, Makoto Nakano, and two anonymous
reviewers for their comments. The author appreciates the many helpful comments from seminar participants at
Hitotsubashi University and the 12th International Conference of The Japanese Accounting Review. This study was
supported by JSPS KAKENHI Grant Number 21J20641.
†
Address: School of Business Administration Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japan.
Phone: +81-42-580-8000; Email: bd211002@g.hit-u.ac.jp
Received August 29, 2021; accepted September 26, 2023; available online December 26, 2023
DOI: 10.11640/tjar.13.2023.01
Copyright©2023 Research Institute for Economics and Business Administration – Kobe University.

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1. Introduction
Post-earnings announcement drift (PEAD) describes that stock prices gradually incorporatenews surprises on the earnings announcement dates over multiple days.1 Due to its inconsistency
with the efficient market hypothesis, which is the premise of portfolio theory, PEAD has been
examined for its existence in various countries and considered to be a globally observed
phenomenon (e.g., Fink 2021). However, recent studies report that PEAD has disappeared in the
United States (US) (Richardson et al. 2010; Chordia et al. 2014; Calluzzo et al. 2019; Martineau
2022). Against this backdrop, this study aims to investigate whether PEAD is still observed in themodern Japanese stock market.
I assume that technological developments assist investors in processing earnings
announcement information and explore whether a decrease in the information processing cost canlead to a reduction in PEAD. Specifically, I examine whether there is a decline in PEAD over time
and whether the ownership structure influences the trend. Rational inattention models assume thatinvestors’ limited processing capacity results in underreaction to earnings announcements(Blankespoor et al. 2020), and the 21st century has witnessed innovations in technology that lower
the information processing cost. For example, large asset management firms—expecting to leverage
the latest technologies to enhance their investment performance—have established research anddevelopment (R&D) departments. Additionally, the Tokyo Stock Exchange (TSE) introduced
technological innovations, such as eXtensible Business Reporting Language (XBRL) in 2008 andArrowhead in 2010, which facilitate quick access to financial data and trade of stocks. The
prevalence of these technological advances in the 21st century is expected to lead to a decrease in
PEAD over time. However, the benefits of technological development may not be equallydistributed among investors. It is reasonable to assume that large, more sophisticated investors are
better positioned to benefit from technologies. Thus, PEAD may not decline for firms owned bysmall, less sophisticated investors.
This study evaluates whether the reduction of information processing costs due totechnological development has led to a decrease in PEAD, using a sample of the Japanese market
in the 21st century. Two reasons drive the focus on the 21st century. First, there have been two
significant technological events in the TSE—the introduction of XBRL and Arrowhead, which
affect the information processing cost as shown in prior literature. Second, the 21st century ismarked by the third Artificial Intelligence (AI) boom, which is characterized by machine learningand deep learning (Matsuo 2015). Technological development is non-linear; it occurs inbreakthroughs, followed by stagnation. Following the first and second AI booms, the 21st century
can be considered the era of the third AI boom.2 For example, in the early 2000s, the developmentof the Web made large amounts of data available, and research on machine learning techniques
advanced. In 2012, the “Super Vision,” developed by a team at the University of Toronto, led tothe spread of deep learning technology. The technologies of the third AI boom may have been
introduced into investors’ trading strategies, lowering the cost of information processing. This
study focuses on the Japanese market because some stocks are less likely to be traded by investors
who use sophisticated technologies. Foreign and individual investors conduct more than 90 percent

1This study focuses on not only earnings surprise information but also the entire information disclosed in the earnings
announcements (Section 4).
2
The first AI boom was in the late 1950s-1960s, and the second AI boom was in the 1980s.

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

3of trading in Japan. While most foreign investors are considered sophisticated institutional
investors and have access to cutting-edge technology, individual investors are less likely to use
sophisticated technology (Cready 1988; Bhattacharya 2001). Previous research shows that foreign
investors prefer well-known firms, such as stocks with a high percentage of overseas sales, and thoseincluded in the Morgan Stanley Capital International (MSCI) index (Miyajima and Ogawa 2016).
Using Japan as a sample, we can explore whether there is a difference in the trend of PEAD
between stocks traded by foreign investors who use sophisticated technology and those who do not.
According to Iwatsubo and Watikins (2021, 2), “The TSE is the only developed market exchangethat provides long-term and consistent data on stock transactions classified by investor type”. In
other countries such as the US, it is difficult to design appropriate research studies owing to thelack of publicly available data.
This study analyzes a sample of 60,124 firm-year observations listed on the Japanese equity
market from April 2002 to March 2020, measuring the underreaction to earnings announcements
at the year level using an unbiased regression model (Biais et al. 1999). Consistent with theprediction, PEAD decreases during the sample period. Further analysis reveals that this trend
depends on the ownership structure, with PEAD decreasing only in the earnings announcements
for firms-year with high levels of foreign ownership or low levels of individual ownership. Thisfinding aligns with the prediction that sophisticated investors are more likely to use the latest
technologies, and technological improvements contribute toward the mitigation of PEAD. To
complement the primary analysis, this study conducts two additional analyses. First, to explore
whether the observed decrease in PEAD results from enhanced price sensitivity to earnings
announcements, this study uses abnormal return variance (AVAR) as a proxy for price response toearnings announcements. The results show that AVAR increases more in earnings announcementsof firms with high foreign investor ownership or low individual investor ownership, indicating thatthe newest technologies enable investors to process information more quickly, thereby mitigating
PEAD. Next, to investigate the potential impact of regulation fair disclosure on PEAD, this study
analyzes the enactment of the regulation in Japan in 2016. The regulation was implemented toprevent the leakage of private information before earnings announcements. Following a priceleads-earnings analysis, the study demonstrates that the ability of stock price to predict earnings
surprises weakened and reactions to earnings surprises increased after the regulation. However, theresults do not indicate that the observed decrease in PEAD is influenced by the regulation of fairdisclosure.
This study contributes to the literature in three ways. First, the findings of this study confirm
that PEAD has decreased in the Japanese stock market, consistent with prior evidence in the US.
Although PEAD is considered a global phenomenon (Ball and Brown 2019; Fink 2021), its
disappearance has only been evident in the US market (Richardson et al. 2010; Chordia et al. 2014;
Calluzzo et al. 2019; Martineau 2022). Using non-US data, this study further supports thepossibility that PEAD has declined in the 21st-century stock market rather than resulting from
fortuity in the US market. The study’s findings differ from previous Japanese literature on PEAD
(Okada and Saeki 2014; Jin 2019) in two ways. First, this study focuses on the time-series trend of
PEAD. Although Jin (2019) examines the trend of PEAD around the introduction of XBRL, tothe author’s knowledge, this study is the first to examine the long-term trend of PEAD in the 21st
century. Second, it focuses on the underreaction to the entire information of the earnings
announcement rather than just the earnings surprise, which other Japanese literature has examined.
This approach enables the study to examine the influence of technological development on the

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ability to process more complex information, making a unique contribution to the Japanese
literature on PEAD.
Second, it contributes to the emerging accounting field of research on the rational inattention
model (Blankespoor et al. 2020). Using the Japanese market environment, in which more than 90
percent of the changes in trading volume (in JPY) are caused by foreign and individual investors,
this study finds that PEAD remains in firms with low foreign ownership and high individualownership. This suggests that the limitation of processing capacity affects underreaction,
supporting the implications of rational inattention models. These findings are important for firms,
because a change in investors’ processing costs may alter the optimal level of disclosure.
Finally, this study contributes to the literature on the cause of the increase in price responses
to earnings announcements. Beaver et al. (2018) assert that the price response to earnings
announcements is dramatically increasing and that this trend is quite different from the past; manystudies have sought to explain this reason. While some studies suggest that the increase may be
attributed to accounting information other than earnings (Hand et al. 2022; Barth et al. 2023),
others report that changes in investor trading styles may impact the price reactions to earnings
announcements (Lee and Watts 2021; Thomas et al. 2022). The findings of this study highlightthe importance of changes in investor trading styles as a contributing factor.
The remainder of this study is organized as follows. Section 2 introduces the Japaneseshareholder trend. Section 3 reviews the literature and develops hypotheses. Section 4 describes themethodology and samples. Section 5 presents the results. Section 6 conducts the robustness analysis,
followed by additional analyses in Section 7. Finally, Section 8 concludes the study.

2. Trend of Shareholder Composition in JapanThe ownership structure of Japanese publicly traded companies has drastically changed overthe past five decades. Figure 1 depicts the shareholder distribution and stock market status in Japan.
Banks and business firms were the predominant shareholders in Japan until the late 1990s.
However, owing to the banking crisis in 1997 and changes in the accounting system, such as themarket evaluation of cross-shareholdings in 2002, these bank stock holdings and crossshareholdings fluctuated in the late 1990s. Consequently, foreign investors replaced banks andbusiness firms as the main shareholders of Japanese stocks.
Panel A of Figure 1 illustrates the trend in the Japanese shareholding ratio by shareholder type.
In 1991, financial institutes and business corporations accounted for 43% and 29%, respectively,
whereas in 2019, they constituted 30% and 22%, respectively. Conversely, foreign investors’
proportion increased from 6% in 1991 to 30% in 2019.
Panel B displays the trend in trading volume (in JPY) by shareholder type. Similar to theshareholding ratio, the financial institutes’ trading volume (in JPY) decreased after the bank crisis.
In contrast, foreign investors’ trading volume (in JPY) has increased since 2000 and reached 71%
in 2019, indicating that they are the most popular traders in the current Japanese stock market.
Additionally, individual investors represented the second-highest shareholder type in 2019, with
91% of the total trading volume (in JPY) coming from foreign and individual investors. Therefore,
foreign and individual investors are the primary drivers of information into the Japanese stock
market prices.

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

FIGURE 1. TRANSITION OF JAPANESE SHAREHOLDING RATIOS AND TRADING VOLUMES
Panel A: Transition of Japanese Shareholding Ratios by Shareholder Type (Based on Market Value)

Reference: Japan Exchange Group Share Distribution Survey.

Panel B: Transition of Trading Volumes by Shareholder Type (in JPY)

Reference: NEEDS FinancialQUEST

5

6The Japanese Accounting Review, 13 (2023), 1-36

3. Related Literature and Hypotheses
3.1. Prior Literature
3.1.1. Disappearance of PEAD
PEAD is the underreaction to news surprise or the phenomenon in which stock prices
gradually incorporate the initial surprise over multiple days. Ball and Brown (1968) first mentioned
PEAD in their seminal research and, Bernard and Thomas (1989) provided more convincing
evidence. After that, numerous empirical studies have been conducted as evidence of efficienthypothesis anomalies (Fink 2021).
Recent research indicates that PEAD is disappearing from the US stock market (Richardson
et al. 2010; Chordia et al. 2014; Calluzzo et al. 2019; Martineau 2022), which is attributed toliquidity improvement (Chordia et al. 2014), active trading of low latency traders (Chordia andMiao. 2020), and dissemination of its existence since its publication (Calluzzo et al. 2019).
However, because the contribution of each factor towards PEAD is still uncertain, it remains
unclear whether this discovery is observed only in the US or in other countries with different
features.
3.1.2. Ownership Structure and PEAD
Previous studies have demonstrated a correlation between investor type and PEAD, with naive
investors who lack an understanding of the time-series characteristics of earnings being identified
as the cause of PEAD (Bernard and Thomas 1989). It is generally believed that institutional
investors are sophisticated, whereas individual investors are unsophisticated and naive (Bernard
and Thomas 1989). Consistent with this assumption, prior studies have established that higher
institutional ownership reduces PEAD (Bartov et al. 2000; Shu 2013), and transient institutional
investors also decrease PEAD (Ke and Ramalingegowda 2005). Moreover, foreign institutional
investors reduce mispricing (Qin and Bai 2014; Shin and Park 2018) and accelerate the speed ofprice discovery (Cai et al. 2020). While Hirshleifer et al. (2008) and Kaniel et al. (2012) contend
that the trading behaviour of individual investors is not necessarily naive, Bhattacharya (2001) andBattalio and Mendenhall (2005) argue that small and less sophisticated individual investors are thenaive investors identified by Bernard and Thomas (1989).3 In the Japanese stock market, mostforeign investors are institutional investors including pension funds, investment trusts, insurancecompanies, and hedge funds based abroad. Given the high volume of transactions (Section 2), mostinvestors are assumed to be sophisticated and transient institutional investors.

3.2. Hypotheses Development
One explanation for PEAD’s existence is that its costs outweigh its benefits. These costs
include direct costs (e.g., transaction costs) (Bhushan 1994; Ng et al. 2008) and indirect costs suchas information processing costs (Blankespoor et al. 2020; Fink 2021). Blankespoor et al. (2020)
identify three processing costs: awareness, acquisition, and integration, each derived frominformation processing steps that acknowledge the existence of disclosure, extract value-relevant
information from the disclosure, and analyze information related to firm value.

3

In addition to research that focuses on institutional investors (foreign investors) and individual investors, some studies
have investigated the effect of high-frequency traders (Bhattacharya et al. 2020; Ke and Zhang 2020; Chakrabartya et
al. 2022) and low latency traders (Chordia and Miao 2020).

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

7

Rational inattention models assume that investors have limited processing capacity andallocate scarce resources to process information rationally (Veldkamp 2011). Empirical evidence
suggests that these processing costs are related to PEAD. For example, Hirshleifer et al. (2009)
and Blankespoor et al. (2020) find that PEAD is stronger when many firms announce earnings on
the same day. Moreover, Barinov et al. (2022) suggest that PEAD is stronger for conglomerates
than for single-segment companies, and Kang et al. (2017) report that it increases with the degreeof international diversification.
However, technological advancements in the 21st century are expected to decrease theseinformation processing costs. For example, large investment management firms have establishedtheir own R&D departments to research and develop operational methods using cutting-edge
technology. The website of Mitsubishi UFJ Trust Investment Technology Institute, a subsidiaryand research division of a major Japanese investment management firm, states that it is enhancing
and automating business efficiency using robotic process automation and analyzing information
using data analysis techniques such as AI and machine learning.4 Bhattacharya et al. (2018) provide
evidence that larger institutional investors have already adopted comparable technologies before
the introduction of XBRL. Therefore, investors are expected to regularly engage in R&D activities
that incorporate state-of-the-art technology to enhance their operational performance and reduceinvestors’ awareness, acquisition, and integration costs.
Another example of technological innovations is the introduction of XBRL and Arrowhead.
XBRL, a standardized XML-based language, was introduced in 2008 to promote the creation,
distribution, and use of financial information. It facilitates direct data import into systems andspreadsheets, thus enabling investors to process and analyze data without re-entering them. The
introduction of XBRL is anticipated to reduce acquisition and integration costs (Blankespoor2019). Similarly, Arrowhead and co-location services were introduced by TSE on January 4, 2010,
reducing order processing time from 3 seconds to 5 milliseconds. Since then, Arrowhead has beenrenewed three times, with order response times decreasing to less than 1 millisecond in July 2012,
0.5 milliseconds in September 2015, and 0.2 milliseconds in November 2019. This innovation has
accelerated the entry of high-frequency trading (HFT) firms into the Japanese market, and it isbelieved that approximately 40 percent of trading volume (in JPY) is executed via co-location
services to achieve HFT (Ohyama et al. 2021). HFT is a type of algorithmic trading in which
orders are automatically determined and placed by a computer in accordance with a predetermined
program without human intervention. Chakrabartya et al. (2022) demonstrate that HFT mitigatedhuman cognitive limitations and increased market reaction to US firms’ earnings announcements.
Thus, the increased use of algorithmic trading, including HFT, is expected to significantly reducethe integration costs associated with investors analyzing information.
Based on the technological advancements of both investors and the exchange market, it isanticipated that the information processing cost in the 21st-century Japanese stock market has
decreased. Therefore, if the primary cause of PEAD is information processing costs, PEAD is
expected to decline over time. Accordingly, the following hypothesis is proposed:
Hypothesis 1: PEAD is decreasing in the Japanese stock market.

4

Available at: https://www.mtec-institute.co.jp/en/business/data_analytics/.

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The impact of technological advancements on investors’ information processing costs is
expected to vary based on the investors’ size and sophistication (Blankespoor et al. 2020). Larger
investors tend to benefit more from economies of scale and can afford cutting-edge, expensive
equipment and skilled labor. Moreover, the reduction of marginal information processing costs
resulting from the implementation of the technology is greater for sophisticated investors.
In the early stages of XBRL adoption in the US, information asymmetries between individual
and institutional investors increased (Blankespoor et al. 2014), suggesting that institutional
investors were better equipped to benefit from XBRL. As individual investors are smaller and lesssophisticated than institutional investors, the cost of mastering XBRL could outweigh its benefits.
Therefore, individual investors may rationally choose not to use XBRL, and its introduction would
not reduce the cost of information acquisition or integration for them, explaining the existence ofPEAD. The effects of HFT and algorithmic trading, and R&D capabilities may vary between
individual and foreign investors. According to Ohlson’s (1975) theory, the value of information
increases with the size of the investor’s wealth, and research indicates that individual investors tend
to use less costly information (Cready 1988; Bhattacharya 2001). HFT and algorithmic trading,and R&D activities require expensive equipment or advanced skills, making these technologies
more accessible to larger and more sophisticated institutional investors. Thus, individual investors
with less wealth may continue to incur higher integration costs, while foreign investors can reduce
information acquisition and integration costs by utilizing cutting-edge technology, thereby
reducing PEAD.
Individual and foreign investors constituted approximately 90 percent of the trading volume(in JPY) in the Japanese stock market in 2019 (Section 2). If individual and foreign investors
conduct 90 percent of all trades on the day of the earnings announcement, the impact oftechnological developments on PEAD may differ between stocks primarily traded by foreign
investors and those by individual investors. Hence, the following hypotheses are derived.
Hypothesis 2.1: PEAD persists in firms with low levels of foreign ownership.
Hypothesis 2.2: PEAD persists in firms with high levels of individual ownership.
Hypotheses 2.1 and 2.2 assume that sophisticated and unsophisticated investors set the stock
price (Hand 1990), the proportion of shareholdings is correlated with a percentage of actual trades,
and the investor group with a high proportion of shareholdings sets the stock price. However, ahigh foreign (individual) ownership ratio does not necessarily indicate that foreign (individual)
investors set the stock price on the earnings announcement date, as the ownership structure merelyreflects who owns the shares.
To address this issue, cut-off points are employed as a narrow solution, following Balsam etal. (2002) and Collins et al. (2003). Specifically, I examine whether PEAD decreases in the sample
with a low level of foreign ownership (a high level of individual ownership), by not treating foreign(individual) ownership as a continuous variable. This is because, in a sample with a low level offoreign ownership (high level of individual ownership), foreign investors (other investors besides
individual investors) are unlikely to determine the stock price on the day of the earnings
announcement. Overall, the hypotheses are tested by verifying whether PEAD decreases in samples
where the necessary conditions are unmet.

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

9

4. Methodology and Data
4.1. Research Design
Two methods are used to detect PEAD: one focuses on earnings surprises and subsequent
returns, while the other focuses on returns on the day of the earnings announcement andsubsequent returns. The first method, applied by seminal studies such as Ball and Brown (1968)
and Bernard and Thomas (1989), uses the difference between actual earnings and market
expectations as the earnings surprise, which is a standard research design for studying PEAD.
However, calculating the earnings surprise requires unobservable expected earnings, which may
produce unintended results due to estimation errors. Furthermore, earnings announcements
contain information other than earnings (e.g., other financial information such as sales and R&D
expenditure and non-financial information such as management forecasts and management
conference calls) that earnings surprises do not capture. To address these issues, the latter approach,using an unbiased regression model introduced by Biais et al. (1999), is effective. This model
assumes that future prices will be “efficient” prices and regresses the price change in the future(typically sixty business days) on the price change around the announcement date. The results ofthis regression help determine whether there is any information that the stock price does notincorporate on the earnings announcement date.
This study detects PEAD using an unbiased regression approach because the interest is the
improvement of processing costs for fundamental information including non-earnings information,
which has become increasingly significant in recent years. For example, Nallareddy et al. (2020)
show that cash flows have more explanatory power for future cash flows than earnings. Hand et al.
(2022) demonstrate that eleven income statement-related item surprises and two cash flow
statement-related item surprises significantly correlate with the stock price. Barth et al. (2023)
classify firms into four groups (new economy or non-new economy, profit or loss) to demonstratethat the accounting items strongly associated with stock prices vary by group.5,6 Given that thisstudy examines whether technological advancements have reduced the information processing costof acquiring and integrating the entire information disclosed in the earnings announcement, a
return approach that emphasizes the entire information disclosed in earnings announcements is
adopted. In addition, stock prices respond not only to earnings surprises (the difference between
actual earnings and the market’s expected earnings for the current period) but also to management
forecast surprises (the difference between management earnings guidance and market’s expected
earnings for the following period). As nearly all Japanese companies release earnings forecasts forthe following fiscal year on the day of earnings announcements, it is unclear which of the two
factors, realized earnings surprises or management forecast surprises, should be used to identify
PEAD. Accordingly, this study measures PEAD by returns. However, a similar analysis is also
conducted using earnings surprises in Section 6.
Following prior research (Biais et al.1999; Martineau 2022), the following model is estimatedannually to detect PEAD:
5Barth et al. (2023) identify key items in each group: net income and book value of net assets (for non-new economy
and profit firms), book value of net assets (for non-new economy and loss firms), net income and operating cash flow(for new economy and profit firms), and cash, book value of net assets, total assets, and R&D expenditures (for new
economy and loss firms).
6
Moreover, Shao et al. (2021) reveal that the relationship between earnings and stock prices has weakened, however the
relationship between fundamental news and stock prices, as measured by earnings announcement returns, has increased.

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-1, 60

ARi,y

-1, 1

= β0 + β1 ARi,y + εi

(1).

-1, 60The dependent variable (ARi,y ) is the post-earnings announcement return for firm i in fiscal year
y, which is defined as the buy-and-hold abnormal return from earnings announcements −1 to + 60
-1, 1
business days. The explanatory variable (ARi,y ) is the abnormal return around the earnings
announcement of firm i in fiscal year y, which is defined as the cumulative abnormal announcement
window return over three days (earnings announcement date ± 1). I estimate abnormal returns in
two ways using the market model and the Carhart four-factor model (Carhart 1997). I estimate αiand γi for each firm-year over the non-earnings-announcement windows (from day −260 to day −3
relative to the date of the annual earnings announcement date) from the following equations:Market model:
Returni,d = αi + γi MPd + εi,d(2),Carhart four-factor model:
Returni,d = αi + γ1,i MPd + γ2,i SMBd + γ3,i HMLd + γ4,i MOMd + εi,d(3),where Returni,d is the daily stock price return of firm i at day d, MPd, SMBd, HMLd, and MOMd are
the corresponding market returns, size returns, book value returns, and momentum returns at dayd, respectively. The daily factor returns data (MPd, SMBd, HMLd, and MOMd) are obtained from
Kenneth R. French Data Library.7 To adjust for the effect of the exchange rate (Glück et al. 2021),
I convert the downloaded factors into Japanese JPY. Refer to Appendix A for further details of theadjustment method. When the content of the earnings announcement is not fully reflected in thestock price on the earnings announcement date, the drift continues and β1 should be higher (Biaiset al. 1999; Ball and Shivakumar 2008).

4.2. Data
All data are acquired from the NEEDS-FinancialQUEST database of Nikkei, Inc. The sample
consists of 60,124 firm-year earnings announcements excluding financial sector entities (e.g., banks,
securities, and insurance) listed in the Japanese equity market from April 2002 to March 2020.8,9,10
Observations with a difference greater than sixty days between the fiscal year-end date andannouncement date are removed. Additionally, when estimating beta from the market model or
Carhart four-factor model, the observations with fewer than forty returns are excluded. The sample

7

Daily MP, SMB, and HML returns are obtained from “Fama/French Japanese 3 Factors [Daily]” and daily MOM
returns are obtained from “Japanese Momentum Factor (Mom) [Daily]” in Kenneth R. French Data Library(https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).
8
Considering the possibility that the earnings announcement is released after the stock market closes, I defined earnings
announcement day as the business day after the earnings announcement day in the database.
9
The accounting period of most Japanese firms ends in March. In this study, I classify announcements that end from
April 2002 to March 2003 as fiscal year 2003, and April 2003 to March 2004 as fiscal year 2004, and so on.
10
When the announcement day is different between the consolidated and non-consolidated financial reports, I choose
the latter because it is released faster. However, as over 95 percent of firms release them on the same day, this choice
has negligible effect on the results.

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

11

is restricted to those that utilize all variables in this analysis. Finally, I winsorize all continuous
variables at 0.5% and 99.5% of their respective distribution to reduce the effects of outliers.
The shareholder composition data utilized for this analysis is the number that is described in
the security report. Annually, Japanese corporations report six categories of shareholder distribution
in the security report: “government and local governments,” “financial institutes,” “financial
instruments businesses,” “other business firms,” “foreign investors,” and “individual investors.” Theratios of foreign and individual ownership used in this study are obtained from the NEEDSFinancialQUEST database.
Panel A of Table 1 displays the descriptive statistics for the foreign ownership (FOR) andindividual ownership (IND) ratios by fiscal year. The number of observations remained relatively
stable, with the highest number being 3,557 in the fiscal year ending between April 2006 andMarch 2007 (fiscal year 2007) and the lowest being 3,127 in the fiscal year ending between April
2012 and March 2013 (fiscal year 2013). Foreign investor ownership has been increasing over time
(Section2). In the fiscal year ending between April 2002 and March 2003 (fiscal year 2003), themean (median) rate is 5.0% (1.1%), while in the fiscal year ending between April 2019 and March
2020 (fiscal year 2020), it rises to 12% (7.3%). Conversely, the ownership of individual investors
remained relatively stable, with maximum mean (median) of 47 % (45 %) and a minimum mean
(median) of 43% (41%).
To test Hypotheses 2.1 and 2.2, I establish the thresholds for high and low foreign ownershipand individual ownership. Although there is no theoretical justification, I use each fiscal year’s
twentieth and eightieth percentile as the cut-off points, consistent with previous research (Handet al. 2022; Martineau 2022). Panel A of Table 1 presents the twentieth percentile of the foreign
ownership ratio and the eightieth percentile of the individual ownership ratio. The maximum of
the twentieth percentile of the foreign ownership ratio is 1.3%, and the minimum is 0.1%. The
maximum of the eightieth percentile of the individual ownership ratio is 68%, and the minimum
is 62%.
Panel B of Table 1 shows the overlap of the subsamples using a 2 × 2 matrix table with rowsof FOR and columns of IND. If observations with less than (more than) the twentieth percentile
for foreign investors are classified into groups with more than (less than) the eightieth percentile
for individual investors, the two groups may represent two sides of the same coin. The results inPanel B indicate this possibility. If foreign and individual investors’ distribution are independent,
the number of observations in less (more) than the twentieth percentile and more (less) than theeightieth percentile should be 2,405 (38,479). However, the actual number of observations is 3,868(39,942), which indicates that firms classified into Low FOR (High FOR) are more likely to beclassified into High IND (Low IND). Consequently, this study’s results using foreign or individual
investors as cut-off points are not necessarily completely different but are the results of using asample of observations with relatively low (high) foreign ownership and relatively high (low)individual ownership from two distinct perspectives.

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The Japanese Accounting Review, 13 (2023), 1-36

TABLE 1. DESCRIPTIVE STATISTICS OF SHAREHOLDER OWNERSHIP
Panel A: Descriptive Statistics

Panel B: Sample Distribution

Notes: Panel A displays the descriptive statistics of FOR and INV by fiscal year. Panel B represents
the sample joint distribution using a 2 × 2 matrix table with rows of FOR and columns of IND.
The values in parentheses are the number of samples when FOR and IND were independent.
Please see the definitions of FOR and IND in the Appendix B.

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

13

5. Results
5.1. Visual Results
Figure 2 displays the average cumulative abnormal returns following earnings announcements.
I divide the observations for each fiscal year into ten groups based on the magnitude of theabnormal return on the day of the earnings announcement. I then calculate the mean cumulative
abnormal return values for each group over the period of +2 to +60 business days after the earnings
announcements. To visualize this trend, I divide the sample period into four-time intervals: FY
2003–FY 2007, FY 2008–FY 2011, FY 2012–FY 2015, and FY 2016–FY 2020. Figure 2 illustrates
these results, only for group 2 (with the second-lowest abnormal returns on earnings
announcements) and group 9 (with the second-highest abnormal returns on earnings
announcements), to facilitate reading.11
Figure 2 shows that group 9 exhibits higher cumulative abnormal returns following earnings
announcements compared with group 2, indicating that investors are unable to fully incorporate
positive or negative news on the earnings announcement day and such news is gradually reflectedin stock prices. Comparing the results from the four periods reveals that the difference incumulative abnormal returns between groups 2 and 9 decreases from period 1 to period 4,
suggesting a decline in PEAD over the sample period.

5.2. Regression Results
Figure 3 displays the regression results of PEAD. Each fiscal year, equation (1) is regressed, andcoefficient β1 is obtained. Panel A of Figure 3 presents the estimated β1 for the full sample. Whenthe stock price underreacts (overreacts) to earnings announcements, β1 should be higher (lower).
In the early sample period, stock prices tend to underreact to earnings announcements. However,the situation has improved in recent years, indicating a decrease in PEAD in the sample period.
Panels B and C report the results of the subsample analysis. Equation (1) is separately regressed
each fiscal year for firms with high foreign ownership (individual ownership) and low foreign
ownership (individual ownership) at the twentieth percentage point (eightieth percentage point)
cut-off. Panel B presents the results for foreign investors. In the sample above the twentiethpercentage point (High FOR), β1 decreases, as in Panel A. However, in the below twentiethpercentage point sample (Low FOR), β1 does not decrease, suggesting that PEAD persists in firms
with low foreign shareholder ownership. Panel C presents the results for individual investors. In
the below eightieth percentage point sample (Low IND), β1 decreases, as in Panel A. Nevertheless,in the above eightieth percentage point sample (High IND), β1 does not decrease, revealing that
PEAD persists in firms with high individual shareholder ownership.
Table 2 presents the results of the OLS regression of β1 on the trend variable T, which takes
the value 1 if the fiscal year is 2003, 2 if 2004, and so on. Panel A shows the results of the market
model, while Panel B shows the results of the Carhart four-factor model. Column (1) represents
the full sample (Panel A of Figure 3), columns (2) and (3) represent the foreign investor subsample(Panel B of Figure 3), and columns (4) and (5) represent the individual investor subsample (Panel
C of Figure 3). Similar to Figure 3, the coefficients T are negative and statistically significant in
the full sample (column 1), High FOR (column 2), and Low IND (column 4). Conversely, the T
coefficients are not statistically significant in Low FOR (column 3) and High IND (column 5).
11Groups 2 and 9 were chosen instead of groups 1 and 10 to mitigate the effect of outliers.

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The Japanese Accounting Review, 13 (2023), 1-36

FIGURE 2. AVERAGE CUMULATIVE ABNORMAL RETURNS AFTER EARNINGS ANNOUNCEMENT

Notes: The figure depicts the average cumulative abnormal returns following earnings
announcements by each group. For clarity, only the results for group 2 (the second-lowest
abnormal returns on earnings announcements) and group 9 (the second-highest abnormal
returns) are presented, although observations are divided into 10 groups. “M2 (M9)” is the resultfor group 2 (group 9) calculated by the market model, and “FF2 (FF9)” is the result for group 2(group 9) calculated by the Carhart four-factor model.

In conclusion, Figure 3 and Table 2 indicate that PEAD decreases from the fiscal year ending
between April 2002 and March 2020. However, this trend is not observed in the earnings
announcements of firms with low levels of foreign investor ownership or high levels of individual
investor ownership. These findings support the hypothesis that foreign investors can benefit morefrom the evolution of technologies than individual investors, resulting in relatively low processing
costs and rapid incorporation of information into stock prices. When the foreign shareholder ratio
is low or the individual shareholder ratio is high and the most advanced technologies are not fully
utilized to reflect information into stock prices, processing costs may remain relatively high, leading
to the existence of PEAD.12
12

To test the effects of technological development on PEAD more directly, I examine whether the decrease in PEAD
is large, especially after the introduction of XBRL and Arrowhead, by extending Table 2. The unreported findings,
however, do not show that their introductions are the main drivers of the decreasing trend of PEAD. One possible
reason for these inconsistent results can be the influence of technologies other than XBRL and Arrowhead. If
sophisticated investors have already used cutting-edge technologies before the introduction of XBRL and Arrowhead,
their introductions might not have accelerated the decline in PEAD. In short, while the results do not indicate that

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

15

FIGURE 3. POST-EARNINGS ANNOUNCEMENT DRIFT: REGRESSION RESULTS
Panel A: Full Sample

Panel B: Foreign Ownership (FOR)Panel C: Individual Ownership (IND)

Notes: The figure displays the regression results of equation (1). Panel A shows the estimated β1
for each fiscal year in the full sample. Panels B and C report the results of the subsample analysis.
Panel B illustrates the estimated β1 for the point of the 20th percentile of foreign ownership, andPanel C shows the results for the point of the 80th percentile of individual ownership each fiscal
year. Please see the definitions of variables in the Appendix B.

the introduction of XBRL and Arrowhead results in a decrease in PEAD, these findings do not necessarily deny the
hypothesis of this study. This means that direct evidence regarding technological developments reducing PEAD whenusing two examples of technological developments in the stock exchange market cannot be obtained.

Notes: The table presents the regression results of β1 on T. Column (1) shows the results for the full sample, while columns (2)–(5) report
the results of the subsample analysis. Columns (2) and (3) report the regressed results separately at the point of the twentieth percentage
point of FOR each fiscal year. Columns (4) and (5) present the regressed results separately at the eightieth percentage point of IND eachfiscal year. The White’s standard error is used; ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively,
using a two-tailed t-test. Please see the definitions of variables in the Appendix B.

Panel B: Factor Model

Panel A: Market Model

TABLE 2. TREND OF POST-EARNINGS ANNOUNCEMENT DRIFT

16
The Japanese Accounting Review, 13 (2023), 1-36

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

17

6. Robustness Analysis
6.1. Change in the Cut-off Points
In the primary analysis, the twentieth or eightieth percentage point is utilized as theshareholding ratio cut-off. In this section, a similar analysis is conducted using different cut-offs,
namely the thirtieth or the seventieth percentage point and the fiftieth percentage points, todetermine the initial results change.
Panels A and B of Table 3 show the same analysis results as the market model in Table 2 butwith different cut-offs for the shareholding ratio, namely the thirtieth or the seventieth percentage
points and the fiftieth percentage point. Similar to the findings of Table 2, the results indicate that
PEAD decreases in subsamples with a low and high proportion of foreign and individual investors,
respectively. This implies that the results of the primary analysis are independent of the ownership
split.

6.2. PEAD Using Earnings Surprise and Forecast Innovation
The primary analysis employs the abnormal return on the earnings announcement day as ameasure of news surprises to test the hypothesis that technological progress mitigates underreaction
to unexpected news surprises. This is because the study focuses on the ability to integrate
fundamental information including non-earnings information, which appears to have a high costof information processing. However, PEAD literature focuses on a specific accounting item
(particularly earnings), which is believed to require less processing cost. This section examines
whether underreaction to a specific item surprise is also mitigated and determines whether the
reduction in underreaction can be detected at varying levels of processing cost.
TSE requires all listed firms to report quarterly condensed financial reports (Kessan-Tanshin)that include key current financial items and management forecasts for the upcoming year. Although
the disclosure of management forecasts is voluntary, most companies typically provide them(effectively mandated). Managers usually forecast the upcoming year’s sales, operating income,ordinary income, net income, and earnings per share on Kessan-Tanshin. Most forecasts are point
estimates with only a few range estimates. Additionally, TSE establishes a “significance rule” that
requires managers to revise their most recent forecasts when significant changes occur. For instance,
if the newly calculated net income forecast differs from the most recent publication by more than
30 percent, managers must revise it.
The central topics regarding this system are the credibility and usefulness of management
forecasts. According to Kato et al. (2009), managers exhibit opportunistic bias by providing upwardforecasts and later revise downward during the fiscal year. Owing to such downward revisions, thedifference between actual earnings and the most recent forecasts is typically non-negative.
Moreover, Ota (2011) identifies systematic bias in management forecasts and reveals that forecast
errors are associated with financial distress, firm growth, firm size, and prior forecast errors.
Despite concerns regarding their credibility, prior studies reveal that management forecasts
play an important role in the Japanese stock market. For example, Kato et al. (2009) confirm thatstock prices react to the initial management forecasts and conclude that they are informative despite
their inherent bias. Ota (2010) relies on value relevance research and demonstrates thatmanagement forecasts have the highest correlation and incremental explanation power with stock.
Furthermore, he clarifies that more than 90 percent of changes in analysts’ forecasts are solely
explained by management forecasts.

Notes: The table presents the regression results of β1 on T separately for High FOR (IND) and Low FOR (IND). Columns (1) and (2)of Panel A are the regressed results separately at the point of the 30th percentile of FOR each fiscal year. Models (3) and (4) of Panel A
present the regressed results separately at the point of the 70th percentile of IND each fiscal year. Panel B presents the results at the
point of the 50th percentile of FOR and IND each fiscal year. White’s standard error is used. ***, **, and * indicate statistical significanceat the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see the definitions of variables in the Appendix B.

Panel B: The 50th Percentile

Panel A: The 30th or 70th Percentile

TABLE 3. TREND OF POST-EARNINGS ANNOUNCEMENT DRIFT USING VARIOUS CUT-OFFS

18
The Japanese Accounting Review, 13 (2023), 1-36

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

19This empirical evidence indicates that both current earnings and management forecasts are
essential in the Japanese stock market. This section confirms the underreaction to both current
earnings and management forecast surprises. For current earnings surprises, I define them as thedifference between actual earnings reported on the day of the earnings announcement and market
expectation, which is typically estimated from the (1) previous year’s earnings, (2) analysts’ expected
earnings, and (3) most recent management forecast. Previous years’ earnings are called naive models,and analysts’ expected earnings are typically used in the US. However, in Japan, where analyst
coverage is low, most firms publish management forecasts; consequently, many studies use
management forecasts to estimate expected earnings. Thus, this study uses the most recent
management forecast to estimate expected earnings and refers to the difference between actual
earnings and management guidance as the “earnings surprise.” For management forecast surprises,
I define them, following Kato et al. (2009), as the difference between forecasted earnings for fiscal
year t + 1 and actual earnings for fiscal year t and refer to it as “forecast innovation.”
This study assumes that information processing costs are high for all information on theearnings announcement date, management foretastes, and current earnings. The processing cost of
all information on the date of earnings announcement is highest because it contains various
financial items and non-financial information, though management forecast and current earnings
are presented as single numeric values. The information processing cost for the management
forecast is higher than that for current earnings because management forecasts have less credibility
than actual earnings, and investors need information and analysis on how reliable the management
forecasts are. For example, Kato et al. (2009) find that investors discount management forecasts
issued by unreliable managers, and Ota (2011) reveals that analysts do not necessarily takemanagement forecasts at face value. These findings imply that investors and analysts must invest
time and effort to evaluate the reliability of information provided by management forecasts. In
summary, I verify whether comparable results are obtained when using news surprises with varying
levels of information processing costs compared to the primary analysis.
To examine the underreaction to earnings surprise and forecast innovation, I employ thefollowing regression model:
2,60

ARi

= β0 + β1 T × SUPCi,y + β2 T × SUPFi,y + ∑ Control Variables+ ∑ Firm Dummies + ∑ Year Dummies + εi,y

2,60

(4),

where ARiis the firm i’s cumulative abnormal return from +2 to +60 days after the earnings
announcement, SUPCi,y is the decile rank of the earnings surprise [(the current ordinary income –the most recent management forecast for the current ordinary income) / market capitalization] offirm i in fiscal year y, and SUPFi,y is the deciles rank of the forecast innovation [(management
forecast for next year’s ordinary income − the current ordinary income ) / market capitalization] offirm i in fiscal year y. If underreactions to earnings surprise and forecast innovation are mitigated,both the coefficients of T × SUPCi,y, and T × SUPFi,y should be negative.
As control variables, the model includes natural logarithms of market capitalization (lnMVE),
natural logarithms of the difference between the fiscal year-end date and earnings announcement
date (lnLAG), loss dummies (LOSS), number of firms announcing earnings on the same day(lnINT), stock beta (BETA), book-to-market ratio (BM), return on total assets (ROA), debt ratio(DEBT), total asset growth rate (GROWTH), stock volatility (RV) and accounting standard

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The Japanese Accounting Review, 13 (2023), 1-36

dummy. Appendix B summarizes the definitions of these variables. The firm-year clustered
standard errors are used.
Panel A of Table 4 shows the descriptive statistics. Both SUPC and SUPF range from 0 to 1
due to their rank-variable nature. Panel B reveals the correlation matrix. The correlation between
SUPF and AR2,60 is positive indicating market underreaction to forecast innovation. However, there
is no positive correlation between SUPC and AR2,60, which is inconsistent with underreaction.
Table 5 presents the regression results of equation (4). Column (1) represents the result for
the full sample. Although the coefficient of forecast innovations (SUPF) is statistically significant,neither cross-term is significant. Columns (2) and (3) present the results for the foreign investors
subsample, while columns (4) and (5) present the results for the individual investors subsample.
The coefficients of earnings surprise (SUPC) or cross-term of earnings surprise and trend variable(T × SUPC) are statistically non-significant in almost all columns. The forecast innovation (SUPF)
coefficient is significantly positive across all columns, confirming that the market underreacts toforecast innovation. The cross-terms between forecast innovation and trend variable (T × SUPF)
are non-negative in the samples with Low FOR (column 3) and High IND (column 5), implyingthat PEAD does not decrease in the samples with low foreign investor ownership or high individual
investor ownership. Conversely, the cross-terms between forecast innovation and the trend variable
are negative in the samples with High FOR (column 2) or Low IND (column 4), which is consistent
with the main analysis.
In conclusion, the results suggest that (1) there is no underreaction to earnings surprises in theJapanese market in the 21st century and (2) underreaction to forecast innovation is decreasing inthe sample with high foreign investor ownership (low individual investor ownership). The firstfinding indicates that current earnings, which require the least amount of information processing,are immediately incorporated into stock prices on the day of the earnings announcement, and there
is no drift in the 21st century. The second finding suggests that the underreaction is mitigated evenat lower levels of information processing costs, which reinforces the primary findings.
Nevertheless, these findings may not be related to information processing costs. Even if there
were no information processing costs, unreliable forecasts would still be discounted, and there
would be an underreaction (Ng et al. 2013). For instance, if the management’s earnings forecast
for the upcoming year is 100, but investors deem it unreliable and overly optimistic, it might beinterpreted as a discounted 70. When 100 is reported in the following year’s earnings
announcement, the stock price will reflect this value. Therefore, underreaction can occur even if
there are no information processing costs. Consistent with this explanation, Ng et al. (2013) findthat underreaction is greater when management forecasts are less reliable. Moreover, Atiase et al.
(2005) discover that underreaction is greater with forecast innovations than earnings surprise,suggesting a strong preference for credibility. In short, the reasons for underreaction to less credible
information include: (1) the time and effort required to evaluate the reliability of the information(it takes time to evaluate 70 when investors receive 100 from management forecast), and (2) theneed to await the release of new information after considering all available information (afterinvestors estimate 70, it takes time to get reliable information from the following year’s earnings
announcement). Thus, it is difficult to determine which path is responsible for the underreaction,and impossible to predict with certainty that the findings are related to information processing
costs.

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

21

TABLE 4. DESCRIPTIVE STATISTICS AND CORRELATION MATRIX
Panel A: Descriptive Statistics(continued on next page)Notes: Panel A presents the descriptive statistics. In Panel B, the upper-right triangular matrix is Spearman’s rank-order correlation, andthe bottom-left triangular matrix is the Pearson correlation. * indicates statistical significance at a less than 0.05 level of significanceusing a two-tailed t-test. Please see the definitions of variables in the Appendix B.

Panel B: Correlation Matrix (N =46,252)

TABLE 4. (continued)

22
The Japanese Accounting Review, 13 (2023), 1-36

Notes: The table presents the result of the regression analysis of equation (4). Column (1) displays the result of the full sample analysis.
Columns (2) and (3) show the results of equation (4) separately at the 20th percentile point of FOR each fiscal year, and columns (4)and (5) show the results of equation (4) separately at the 80th percentile point of IND each fiscal year. Control variables include tnMVE,
lnLAG, LOSS, lnINT, BETA, BM, ROA, DEBT, GROWTH, RV, and accounting standard dummy. Standard errors are clustered by firmyear. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see thedefinitions of variables in the Appendix B.

TABLE 5. POST EARNINGS ANNOUNCEMENT (EARNINGS AND FORECAST SURPRISE)
Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market
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The Japanese Accounting Review, 13 (2023), 1-36

7. Additional Analysis
7.1. Abnormal Return Variance and PEAD
Underreaction to earnings announcements on the Japanese stock market has decreased in the
21st century (Section 5). If the information released on earnings announcement dates remains
constant, the underreaction mitigation should lead to an increase in the stock price reaction toearnings announcements. To supplement and bolster the credibility of the primary empiricalfindings, this section conducts additional analyses regarding stock price reactions on the date ofearnings announcements.
Several studies have attempted to explain the increase in price response to earnings
announcements in the US and Japan (Beaver et al. 2018; Jinushi 2022). Beaver et al. (2020)
investigate whether management guidance or analyst forecasts bundled with earnings
announcements are a possible cause, given the increase in such bundled earnings announcements,and find that this cannot fully explain the phenomenon. Other studies consider not only the change
in the contents of earnings announcements but also the influence of investors. Thomas et al. (2022)
incorporate findings from the finance literature. They demonstrate that the measurement of priceresponse to earnings announcements used by Beaver et al. (2018) is influenced by the amount of
information during the non-earnings announcement period, microstructure noise, and market
efficiency. Lee and Watts (2021) observe that the increase may be partially explained by algorithmic
trading at the time of earnings announcement, and it does not necessarily indicate an increase in
the contents of earnings announcement information.
This section investigates whether the evolution of information technology has mitigated theunderreaction by increasing the stock price reaction on the date of earnings announcements.
Specifically, I examine if the reaction to earnings announcements is increasing in samples with highlevels of foreign ownership or low levels of individual investor ownership, where I observed a
reduction in PEAD in the primary analysis.
To measure the price reaction to earnings announcements, I use AVAR as a measure following
prior research (Beaver et al. 2018, 2020). AVAR is calculated as the square of the three-day
cumulative abnormal returns in the earnings-announcement window divided by the variance of thethree-day cumulative abnormal returns in the non-earnings-announcement window. The
following model is estimated separately for firms with low foreign and high individual ownership:
AVARi,y = γ0 + γ1 Ty + ∑ Control Variables + ∑ Firm Dummies + εi,y(5),where AVAR is the dependent variable, and T is the independent variable. Control variables include
natural logarithmic value of market value (lnMVE), natural logarithms of the difference between
the fiscal year-end date and earnings announcement date (lnLAG), loss dummy (LOSS), natural
logarithmic value of the number of firms releasing earnings announcements on the same day(lnINT), stock beta (BETA), book-to-market ratio (BM), return on assets (ROA), debt ratio(DEBT), total asset growth rate (GROWYH), the absolute value of earnings surprise scaled bymarket value (|SUPC|), absolute value of forecast innovation scaled by market value (|SUPF|), andaccounting standard dummies. The firm-year clustered standard error is used.
Table 6 displays the regression results. Column (1) represents the full sample, columns (2) and(3) are the subsample of foreign investors, and columns (4) and (5) represent the subsample ofindividual investors. The T coefficient is significantly positive in the full sample analysis in column

Notes: The table reports the results of the regression of AVAR on T. Column (1) presents the result of the full sample, columns (2) and(3) show the regression results separately at the 20th percentile point of FOR each fiscal year, and columns (4) and (5) present the results
at the 80th percentile point of IND each fiscal year. Control variables include lnMVE, lnLAG), LOSS, lnINT, BETA, BM, ROA, DEBT,GROWTH, RV, |SUPC|, |SUPF| and the accounting standard dummies. Standard errors are clustered by firm-year. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see the definitions of variables in the
Appendix B.

TABLE 6. PRICE RESPONSE TO EARNINGS ANNOUNCEMENT
Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market
25

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The Japanese Accounting Review, 13 (2023), 1-36(1), confirming that the stock price responsiveness increases on the day of the earnings
announcement. However, the T coefficient is statistically non-significant in the subsamples of Low
FOR in column (3) and High IND in column (5), where no decrease in PEAD is observed. These
results suggest that the development of information technology increases the responsiveness ofstock prices on the day of the earnings announcement, thereby mitigating underreaction.

7.2. Impact of Regulation Fair DisclosureThis section examines the impact of the regulation fair disclosure, which was enacted in 2006.
The regulation requires firms to provide the same information to the general public simultaneously
if they provide private information to certain analysts. This regulation may have affected PEAD
by decreasing the amount of information disclosed before the earnings announcement date, thereby
increasing the amount of information disclosed on the day of the earnings announcement. Owing
to investors’ limited information processing capacity, the increase in new information released on
the day of earnings announcements may make it impossible to process all information immediately.
Thus, implementing the regulation fair disclosure may lead to an increase in PEAD.
To examine the change in PEAD after the introduction of the regulation fair disclosure, I
conduct several analyses. First, a price-leads-earnings analysis is conducted (Arif et al. 2019). The
following equation is estimated separately, before and after FD.
pre

SUPCi,y or SUPFi,y = β0 + β1ARi, y + ∑ Control Variables + ∑ Firm Dummies+ ∑ Year Dummies + εi,y(6).

Following Horie and Kagaya (2021), the fiscal year ending between April 2014 and March
2016 (fiscal year 2015 and 2016) are considered as Before FD, and the fiscal year ending between
preApril 2017 and March 2019 (fiscal year 2018 and 2019) are considered as After FD. ARi, y is thefirm i’s cumulative abnormal return from the end of the fiscal year to the date of earnings
announcement in fiscal year y. Control variables include lag of earnings (lEARN), returns over the
fiscal year (RET), cumulative abnormal return from +2 to +60 business days after the earnings
announcement (AR2,60), and cumulative abnormal return from −1 to 1 around the earnings
announcement (AR−1,1).
Next, to measure both of the price reaction and the underreaction to earnings surprise, thefollowing equation is estimated separately, before and after FD.
2,60
AR-1,1i, y or ARi, y = β0 + β1SUPCi,y + β2SUPFi,y + ∑ Control Variables+ ∑ Firm Dummies + ∑ Year Dummies + εi,y(7).

Control variables include natural logarithmic value of market value (lnMVE), natural logarithmsof the difference between the fiscal year-end date and earnings announcement date (lnLAG), loss
dummy (LOSS), natural logarithmic value of the number of firms releasing earnings
announcements on the same day (lnINT), stock beta (BETA), book-to-market ratio (BM), returnon assets (ROA), debt ratio (DEBT), total asset growth rate (GROWYH), return volatility (RV),
and accounting standard dummies. The firm-year clustered standard error is used.
Panel A of Table 7 displays the regression results. Column (1) presents the results of therealized earnings surprise, whereas column (2) shows the results of the forecast innovation. In

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

27

column (1), the coefficient of ARpre is significantly positive Before FD, indicating that information
regarding earnings surprises is incorporated beforehand. However, ARpre becomes non-significant
After FD, suggesting that information is no longer incorporated. In Column (2), the ARpre
coefficients are insignificant, indicating that management-forecast information is not incorporated
into stock prices before the earnings announcement. Panel B of Table 7 shows the results for both
the responses and PEAD for earnings surprise and forecast innovation. Column (1) compares thestock price responsiveness to earnings surprises Before FD and After FD. The coefficients of SUPCand SUPF increase from 2.584 to 4.127 and 8.538 to 10.177, respectively, indicating that the stock
price responsiveness to earnings surprises increases After FD. Column (2) compares PEAD Before
FD and After FD. The coefficients of SUPC and SUPF increase from −0.313 to 1.094 and 2.215
to 3.614, respectively, but they are statistically non-significant.
Therefore, the results indicate that although the amount of information incorporated into thestock price before the earnings announcement decreases slightly after the regulation fair disclosure,the increase in the price response to earnings announcements is likely to prevent the increase in
PEAD. This suggests that the regulation fair disclosure has a non-significant influence on the main
findings of this study.

8. Conclusion
Since Ball and Brown’s (1968), technological advancements have revolutionized the stock
market by enabling investors to process information from earnings announcements more efficiently.
This study aims to determine whether PEAD exists in the Japanese stock market in the 21st
century.
Using Japanese data, this study finds that PEAD has decreased during the sample period,
except for earnings announcements of firms with a low level of foreign investor ownership or highlevel of individual investor ownership. This suggests that, in such subsamples, the latest
technologies may not be fully utilized to incorporate information into stock prices, and processing
costs may remain high. Moreover, these changes in investor trading styles can be attributed to theacceleration of the price response to earnings announcements.
However, this study has some limitations. First, it is unclear whether the disappearance ofPEAD is a permanent or temporary trend. Second, the assumption that the shareholdings ratio of
foreign investors (individual investors) is correlated with the trading volume (in JPY) on theearnings announcement date may not be accurate. Future studies should employ more sophisticatedmeasurement techniques to verify this assumption, such as directly measuring trading volume (inJPY) using investors’ account data. Third, this study does not fully eliminate the possibilities ofalternative interpretations. It uses ownership structure as a proxy for the development of
information technology; however, it is an indirect measure. Ownership structure may be a proxyfor other factors, and future studies should examine this issue using more direct data on information
technology.

Panel A: Price Lead Earnings

TABLE 7. RESULTS OF REGULATION FAIR DISCLOSURE(continued on next page)28
The Japanese Accounting Review, 13 (2023), 1-36

Notes: The table presents the results of the regulation FD analysis. Panel A shows the regression results of equation (6). Control variables
include lEARN, RET, AR2,60, AR-1,1, and the accounting standard. Panel B shows the regression results of equation (7). Column (1)shows the results of AR-1, 1, and column (2) presents the results of AR2,60. Control variables include lnMVE, lnLAG, LOSS, lnINT, BETA,
BM, ROA, DEBT, GROWTH, RV, and accounting standard dummy. Standard errors are clustered by firm-year. ***, **, and * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see the definitions of variables in the
Appendix B.

Panel B: Response to Earnings Announcement and Post Earnings Announcement Drift

TABLE 7. (continued)
Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market
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The Japanese Accounting Review, 13 (2023), 1-36

Appendix A: Carhart Four-Factor Model
This study obtains factor returns from Kenneth French’s data library, commonly used
database for applying Fama–French factor models in empirical research. However, using
downloaded factors without adjustments can pose a significant issue since these factors are provided
in US dollars (Glück et al. 2021). To address this issue, I convert factor returns into Japanese yen(JPY).
Following Glück et al. (2021), the market premium and other premiums (HML, SMB,
and MOM) are adjusted for currency fluctuations using the following formula.
Market premium:

Other premiums:

JPY

JPY

MP t

=

1

JPY

USD/JPY(1 + rFX,t )JPYPremium t=

1 + MPUSD+ rUSD) – 1 – r f,t ,t
f,t
1

USD/JPY(1 + rFX,t )PremiumUSD,t
JPY

where MP t is the market factor in JPY, MPUSD
is the market factor in USD, Premium t istthe other factor (e.g, HML, SMB, and MOM factors) in JPY, 𝑃𝑟𝑒𝑚𝑖𝑢𝑚is the other factor in
JPY
USD/JPY
USD
USD, rf,t is the Japanese risk-free rate, rf,t is the US risk-free rate, and rFX,t
is the discrete
returns of the spot exchange rate between USD and JPY on day t. Data for MPUSD,
𝑃𝑟𝑒𝑚𝑖𝑢𝑚,t
JPY
USD
and rf,t are obtained from Kenneth French’s data library. The Japanese risk-free rate (r f,t ) isbased on the interest rate for 10-year JGBs from the International Interest Rate Information
USD/JPY
published by the Ministry of Finance.13 The exchange rate (rFX,t ) is obtained from the Bank ofJapan’s BOJ Time-Series Data Search.14

13
14

Data are available at: https://www.mof.go.jp/jgbs/reference/interest_rate/index.htm.
I obtained the Tokyo market dollar/yen spot as of 17:00 (FM08'FXERD04). Available at: https://www.statsearch.boj.or.jp/ssi/cgi-bin/famecgi2?cgi=$nme_a000&lstSelection=FM08.

Jinushi: Post-Earnings Announcement Drift and Ownership Structure in the Modern Japanese Stock Market

31

APPENDIX B: DEFINITIONS OF VARIABLES
Variables

Definitions

FOR

=

IND

=

ARk,l

=

β1
T

=
=

AVAR

=

ARpre

=

SUPC

=

SUPF

=

lnMVE

=

Percentage of shares held by foreign investors, defined as the number ofshares held by foreign firms, etc. divided by the number of outstanding shares
excluding the shares less than one unit at the fiscal year. High FOR (Low
FOR) indicates the subsample where the level of FOR is above (below) the
20th percentile point.
Percentage of shares held by individual investors, defined as the number ofshares held by individuals and others divided by the number of outstanding
shares excluding the shares less than one unit at the fiscal year. High IND(Low IND) indicates the subsample where the level of IND is above (below)
the 80th percentile point.
Cumulative abnormal return from k to l business days around the annual
earnings announcement. Abnormal returns are calculated using the market
model or the Carhart four-factor model. The earnings announcement day isdefined as the business day after the earnings announcement day in the
database.
Estimated coefficient from equation (1) for each fiscal year.
Trend variable, which takes 1 if the fiscal year is 2003 and 2 if the fiscal year
is 2004, and so on.
Abnormal return variance, defined as the square of the three-day cumulative
abnormal return (AR−1,1) around the earnings announcement divided by thevariance of three-day cumulative abnormal returns in the non-earningsannouncement window. Abnormal returns are calculated using the market
model or the Carhart four-factor model.
Cumulative abnormal return from the end of the fiscal year to the earnings
announcement date. Abnormal returns are calculated from the market modelor Carhart four-factor model.
Decile rank of an earnings surprise, defined as the difference between thecurrent year’s ordinary income and the latest management forecast for thecurrent year’s ordinary income deflated by the market value at the end of the
fiscal year. To facilitate interpretation, the variable is converted to (decile
rank − 1) / 9. The absolute value of SUPC is |SUPC|.
Decile rank of forecast innovation, which is defined as the difference between
management forecast for next year’s ordinary income and current year’s
ordinary income deflated by the market value at the end of the fiscal year. To
facilitate interpretation, the value is converted to (decile rank − 1) / 9. The
absolute value of SUPF is |SUPF|.
Natural logarithm of the market value of equity at the end of the fiscal year.
The market value of equity equals the closing price times the shares
outstanding excluding treasury stock at the end of the fiscal year.

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lnLAG

=

LOSS

=

lnINT

=

BETA
BM

=
=

ROA

=

DEBT

=

GROWTH

=

RV

=

lEARN

=

RET

=

Natural logarithm of the difference between the fiscal year-end date and theannual earnings announcement date.
Loss dummy variable, which takes 1 when ordinary income is negative, and0 otherwise.
Natural logarithm of the number of firms that release quarterly, semi-annual,or annual Kessan-Tanshin on the same day as the firm announced annual
Kessan-Tanshin.
The stock beta (γi), which is estimated from equation (2)Book-to-market ratio. The book value of equity is the shareholders’ equity atthe end of the fiscal year. The market value of equity equals the closing pricetimes the shares outstanding excluding treasury stock at the end of the fiscal
year.
Return on assets, defined as ordinary income divided by total assets at theend of the fiscal year.
Debt ratio for total assets, defined as interest-bearing debt (short-term debt
+ long-term debt, corporate bond and lease obligations in the current
liabilities + long-term debt, corporate bond and lease obligations in the longterm liabilities) divided by total assets at the end of the fiscal year.
Growth rate of total assets, defined as (total assetst − total assetst-1) / total
assetst-1.
Return volatility in the non-earnings-announcement window (from day −260
to day −3 relative to the annual earnings announcement date.
Ordinary income in the previous year divided by the market value of equity
at the end of the previous fiscal year.
Stock price return over the fiscal year, defined as the difference between theclosing price of the previous fiscal year and that of the current fiscal year,
divided by the closing price of the previous fiscal year.

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